diff --git a/.ipynb_checkpoints/L4_Deployment_Tricks-checkpoint.ipynb b/.ipynb_checkpoints/L4_Deployment_Tricks-checkpoint.ipynb
new file mode 100644
index 0000000..5c25463
--- /dev/null
+++ b/.ipynb_checkpoints/L4_Deployment_Tricks-checkpoint.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9f307d83-f207-40a4-9d66-6ee8fd6b6c00",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Lesson 4: Node Deployment Tips & Tricks!\n",
+    "\n",
+    "<b><u>Instructors</b></u>: Ishan Mishra, Phil Culliton\n",
+    "<br>\n",
+    "<b><u>Special Thanks to</b></u>: Madhava Jay\n",
+    "\n",
+    "Our stack is constantly evolving and getting better and more user-friendly. For the current release version of PyGrid and Hagrid, here are a few tips and tricks I've found useful on a Linux/OS X/WSL environment when it comes to working with this infrastructure.\n",
+    "\n",
+    "If you come up with any useful tricks of your own, please do share them with us and the community!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "715eee4b-fa7f-4484-ab71-b092835dcb11",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd624d42-0392-447a-9de4-2f3822af2446",
+   "metadata": {},
+   "source": [
+    "## Aliases\n",
+    "Here are some bash aliases I use for varous purposes, such as in order to ease the process of launching my local domain node."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de4e0e91-01dc-48a9-96b4-b2c4d1c6c028",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# Activates the venv, and navigates to the Syft directory\n",
+    "alias syft=\"conda activate <env_name> && cd <path/to/Pysyft>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f59b557-5853-4fe8-a951-7c90bde50379",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# Launches the local domain node with one word\n",
+    "alias launch=\"hagrid launch <node_name> domain to docker:8081+ tail=True\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2f6bdc8-f1cb-4b75-a97d-d3277c40d77a",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# Shuts down all local domain nodes\n",
+    "alias land=\"hagrid land all\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d782924b-3deb-4002-8a02-1cc75ef0336c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50fc4c9a-eb7c-490c-98f1-edd19b91f8db",
+   "metadata": {},
+   "source": [
+    "# Clearing Up Space\n",
+    "Docker has a reputation for taking up a suprisingly large amount of space as its containers are running. Here are a few ways I've found to mitigate those effects. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ff124a8-75c0-4a0e-be47-091f4d19883e",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# This cleans up after docker\n",
+    "# WARNING: will delete all currently unused or inactive containers, networks, volumes, etc- this will result in losing any data you uploaded to  #Domain Nodes that aren't currently running\n",
+    "\n",
+    "hard_reset() {\n",
+    "        echo \"Pruning Network\"\n",
+    "        docker network prune -f\n",
+    "        echo \"Pruning Volume\"\n",
+    "        docker volume prune -f\n",
+    "        echo \"Pruning System\"\n",
+    "        docker system prune -f\n",
+    "        echo \"Pruning Containers\"\n",
+    "        docker container prune -f\n",
+    "}\n",
+    "\n",
+    "# This will delete active or unused containers, networks, but won't delete any data you've uploaded or stored.\n",
+    "\n",
+    "cleanup() {\n",
+    "        echo \"Pruning Network\"\n",
+    "        docker network prune -f\n",
+    "        echo \"Pruning System\"\n",
+    "        docker system prune -f\n",
+    "        echo \"Pruning Containers\"\n",
+    "        docker container prune -f\n",
+    "}\n",
+    "\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c530773e-0973-455d-8884-b6a83147ed6c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dba2e61a-dc86-4dba-9e44-fb2555c11227",
+   "metadata": {},
+   "source": [
+    "# Testing and debugging\n",
+    "Here are some commands that will make it easier to work with Docker containers, while testing, debugging, or for other purposes. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb5c3696-ce57-4874-abb2-2a0f3dcb7add",
+   "metadata": {},
+   "source": [
+    "#### <b> ctop </b>\n",
+    "ctop is a handy tool that lets you quickly monitor and manage all your containers (both active and inactive):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7ad64d8-83da-496c-86e6-0bcb32006762",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# To install, simply\n",
+    "sudo apt install ctop\n",
+    "\n",
+    "# An alternative version of this package can be obtained from:\n",
+    "https://github.com/bcicen/ctop\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d86de22f-1b91-41b7-8f05-2070c7b815bc",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# ctop is a quick way to monitor containers\n",
+    "ctop\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "526c5add-40c9-465f-a2ba-990112a082ac",
+   "metadata": {},
+   "source": [
+    "By hovering over a container and pressing enter, you gain the ability to:\n",
+    "\n",
+    "- <b> View: </b>\n",
+    "    - <b> Single View </b> (keybind 'o'): view details of that specific container, including ports, health, CPU/MEM usage, NET/IO, environment variables, and much more.\n",
+    "    - <b> Log View </b> (keybind 'l'): View the logs of just this container. This is in contrast to launching a local domain node with tail=True, as that method continuousy outputs all the container logs in real time.\n",
+    "- <b> Interact: </b>\n",
+    "    - <b> Stop </b> (keybind 's'): Stop the container.\n",
+    "    - <b> Pause </b> (keybind 'p'): Pause the container.\n",
+    "    - <b> Restart </b> (keybind 'r'): Restart the container.\n",
+    "    - <b> Exec shell </b> (keybind 'e'): This lets you run commands inside this shell. This is very handy if you want to run test files inside a container, for instance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed81829b-fe69-4dd8-8848-f4da1a060e91",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745b115e-48ee-45e0-ac93-7bbbb7bba742",
+   "metadata": {},
+   "source": [
+    "## Miscellaneous Hagrid Tricks\n",
+    "\n",
+    "<b> Seeing the logs from Hagrid in real time: </b>\n",
+    "``` \n",
+    "# Add the \"tail=True\" parameter!\n",
+    "hagrid launch local_node tail=True\n",
+    "```\n",
+    "\n",
+    "<b> Seeing a list of commands being run in Hagrid: </b>\n",
+    "``` \n",
+    "# Use the cmd=True parameter!\n",
+    "hagrid launch local_node cmd=True\n",
+    "```\n",
+    "\n",
+    "<b> Shutting down your docker containers without losing data: </b>\n",
+    "```\n",
+    "# This is great if the \"hagrid land\" command isn't working\n",
+    "docker rm `docker ps -aq` --force\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0613e05-9a03-429b-8188-0508d4c7aa54",
+   "metadata": {},
+   "source": [
+    "<b> Resetting Domain Node Storage/Memory </b>\n",
+    "\n",
+    "<t> By deleting the db container, you can do a hard reset on the domain node's storage and memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30b313f7-82e0-4cf5-8500-d43fdd10b8f0",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb4cf1af-b20a-4a3b-9a36-16e7f33edf7f",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/L4_NodeMaintenance-checkpoint.ipynb b/.ipynb_checkpoints/L4_NodeMaintenance-checkpoint.ipynb
new file mode 100644
index 0000000..d151495
--- /dev/null
+++ b/.ipynb_checkpoints/L4_NodeMaintenance-checkpoint.ipynb
@@ -0,0 +1,1104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2658e30a-4ce1-4e3c-a631-a5bcf7b096aa",
+   "metadata": {},
+   "source": [
+    "# Lesson 4: Node Management\n",
+    "\n",
+    "<b><u>Instructors</b></u>: Ishan Mishra, Phil Culliton\n",
+    "\n",
+    "\n",
+    "In this lesson, we'll pull back the curtain a bit and show you how to manage, maintain and customize your node. We'll be walking you through adding new users, changing priviledges, creating and responding to requests, and everything to do with network nodes!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aeb1825-a452-48fa-b8b0-687dd7eef4f1",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b655bad5-1774-4c2d-936e-a35a9719e884",
+   "metadata": {},
+   "source": [
+    "## Concept 1. Logging in! \n",
+    "\n",
+    "\n",
+    "### 1.1 Via PySyft\n",
+    "There are two ways to log into your own node, as the Data Owner. The first way is using the PySyft library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac1b13ac-b456-449c-825b-7d5629d62f0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import syft as sy\n",
+    "import numpy as np\n",
+    "from syft.core.adp.entity import Entity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9e7745e9-0cc0-417a-aa51-456ceba2e67a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: CHANGE YOUR USERNAME AND PASSWORD!!! \n",
+      "\n",
+      "Anyone can login as an admin to your node right now because your password is still the default PySyft username and password!!!\n",
+      "\n",
+      "Connecting to http://localhost:8081... done! \t Logging into adp... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "domain_node = sy.login(email=\"info@openmined.org\", password=\"changethis\", port=8081)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63431254-02bb-4fd1-a4df-e848d6042b58",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "### 1.2 Via the UI\n",
+    "\n",
+    "The second way involves using the same username and password, but logging in through the UI, which is accessible at <a href=\"localhost:8081\">localhost:8081</a>\n",
+    "\n",
+    "Please note, if you launched Hagrid to a different port, please replace 8081 with the appropriate portnumber!\n",
+    "\n",
+    "![alt text](ui_images/login_screen.png \"UI Login Screen\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d4eaa2a-72aa-4f5b-aa45-65ea0b8d6df4",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cef3b59-837c-4909-82ba-a82e7af312a2",
+   "metadata": {},
+   "source": [
+    "## Concept 2. Users!\n",
+    "\n",
+    "You might not be the only person using your domain node! Let's see how you can go about checking all the users on your domain node, as well as how to add and configure new ones!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "698d72df-b9dd-448d-ad60-fa692dc41a35",
+   "metadata": {},
+   "source": [
+    "### 2.1 Seeing all users\n",
+    "To see all the current users on a domain node, you can simply do the following once you've signed in using the PySyft library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "950009d8-e448-46ea-8de9-67fe9352fd4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>added_by</th>\n",
+       "      <th>allocated_budget</th>\n",
+       "      <th>budget</th>\n",
+       "      <th>budget_spent</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>daa_pdf</th>\n",
+       "      <th>email</th>\n",
+       "      <th>id</th>\n",
+       "      <th>institution</th>\n",
+       "      <th>name</th>\n",
+       "      <th>role</th>\n",
+       "      <th>verify_key</th>\n",
+       "      <th>website</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2021-11-03 02:48:47.495871</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>info@openmined.org</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>Owner</td>\n",
+       "      <td>dd784cd6c0278a9cd11b69cdf4c4ca0076e661e3dd2e74...</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.user_api.UserRequestAPI at 0x7fbb0e6ee040>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# See all the users\n",
+    "domain_node.users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fd5c9d6-3d25-42c8-ad43-faee2d310757",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "\n",
+    "Alternatively, if you logged in using the UI, you are able to see the Users by clicking the \"Users\" field.\n",
+    "\n",
+    "![alt text](ui_images/users.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d162ad28-789d-43c8-b16a-6f77414216fa",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bc5e346-31a0-4798-9ac4-7dabc935f8cc",
+   "metadata": {},
+   "source": [
+    "### 2.2 Creating a New User!\n",
+    "\n",
+    "Creating a new user is again a really simple process. We just need to specify the following parameters when we're creating the account. Note that many of these can be changed and updated retroactively!\n",
+    "\n",
+    "Let's now try creating a user via PySyft, and validating that it worked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "19d2b482-dcf3-4237-b426-76122c004495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new user\n",
+    "domain_node.users.create(\n",
+    "    **{\n",
+    "        \"name\": \"Sheldon Cooper\",\n",
+    "        \"email\": \"sheldon@caltech.edu\",\n",
+    "        \"password\": \"bazinga\",\n",
+    "        \"budget\": 100\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "505904f9-f962-4e97-879a-7539bfe37eeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>added_by</th>\n",
+       "      <th>allocated_budget</th>\n",
+       "      <th>budget</th>\n",
+       "      <th>budget_spent</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>daa_pdf</th>\n",
+       "      <th>email</th>\n",
+       "      <th>id</th>\n",
+       "      <th>institution</th>\n",
+       "      <th>name</th>\n",
+       "      <th>role</th>\n",
+       "      <th>verify_key</th>\n",
+       "      <th>website</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2021-11-03 02:48:47.495871</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>info@openmined.org</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>Owner</td>\n",
+       "      <td>dd784cd6c0278a9cd11b69cdf4c4ca0076e661e3dd2e74...</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2021-11-03 06:24:10.657167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sheldon@caltech.edu</td>\n",
+       "      <td>2</td>\n",
+       "      <td></td>\n",
+       "      <td>Sheldon Cooper</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>2fef564b79c4fef21d1549a891d624d84e71c482af0c07...</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.user_api.UserRequestAPI at 0x7fbb0e6ee040>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "domain_node.users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccda843e-7fc8-4708-9d30-5caf6a5960f4",
+   "metadata": {},
+   "source": [
+    "By default, this new user is a Data Scientist. However, other roles are also possible. You can see all the roles that are available by accessing <a href=\"localhost:8081/permissions\"> the UI at localhost:8081/permissions </a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc7630b-ba2d-4f7c-a09a-5fce6dcd95a0",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03f7fd2c-0d90-40bd-a2f5-e6d3d33a505b",
+   "metadata": {},
+   "source": [
+    "### 2.3 User Permissions\n",
+    "\n",
+    "It's understandable that you might want to control how much a given user can change and tinker with your domain node. To allow for this, we've added customizable user permissions!\n",
+    "\n",
+    "As mentioned previously, these are accessible by accessing the <a href=\"localhost:8081/permissions\"> the UI at localhost:8081/permissions </a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a55018a-432c-413c-bb65-737affb6d181",
+   "metadata": {},
+   "source": [
+    "![alt text](ui_images/permissions_unexpanded.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f1895c-824f-44eb-9ceb-55eea4fe0f11",
+   "metadata": {},
+   "source": [
+    "Each of the 4 roles can be configured as you see fit. The parameters you can adjust are shown below:\n",
+    "\n",
+    "![alt text](ui_images/permissions_all.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "179c9cec-8ed2-4695-baf8-c627773d1d6c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea1b2b82-1787-4c68-a8a5-1964c2bcf10b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Concept 3. Requests\n",
+    "\n",
+    "Requests are instances where a User wants to do something that they don't have authorization to do.\n",
+    "\n",
+    "\n",
+    "As of the current release, there are 3 major types of requests:\n",
+    "- <b> Data Access</b> requests: Occurs when someone wanting to use the data on the node.\n",
+    "- <b>Privacy budget upgrade</b> requests: Occurs when a DS needs an upgrade of the privacy budget allocated to them.\n",
+    "- <b>New Data Scientist Account</b> requests: Occurs when a Data Scientist wishes to create an account on the domain node.\n",
+    "\n",
+    "With each type of request, they can either be accepted, rejected, approved or granted. We'll show you how to do each, but first, let's show you how a user would put in a request."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1b3f494-d5e0-45b0-9a61-bd94ebb08167",
+   "metadata": {},
+   "source": [
+    "### 3.1 Putting in a Request\n",
+    "\n",
+    "Remember that data scientist account we created awhile back? We'll be making use of him now. Let's first use him to sign into our domain node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e39b28cd-0ab8-4148-b598-1fb6f0fcd654",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connecting to http://localhost:8081... done! \t Logging into adp... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_scientist_node = sy.login(email=\"sheldon@caltech.edu\", password=\"bazinga\", port=8081)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0673d45-427e-4d17-bfdf-5649eebc326f",
+   "metadata": {},
+   "source": [
+    "Now that he's logged in, let's say he checks the privacy budget available to him:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fc3e5be4-fd01-42e4-8800-48d9dd22808d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scientist_node.privacy_budget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef10606d-577f-484d-af24-4531c46e7997",
+   "metadata": {},
+   "source": [
+    "Now let's say our data scientist isn't happy with this much privacy budget, and that he wants more. He could put in a request for more privacy budget, as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5a6a2f31-da50-4d28-93be-3d9103cc6653",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requested 1000 epsilon of budget. Call .privacy_budget to see if your budget has arrived!\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_scientist_node.request_budget(eps=1000, reason=\"I want to do data exploration\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68ec4bdc-5f10-4665-8141-27d5c87b5df5",
+   "metadata": {},
+   "source": [
+    "### 3.2 Responding to a Request"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "190a0c54-c4d4-4695-9190-6f598dd695df",
+   "metadata": {},
+   "source": [
+    "The request that the Data Scientist put in would be logged in the domain node, and could be viewed by the Data Owner at any time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3636a5fa-5f9c-483c-a800-ecff0e18cf2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Requested Object's tags</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Request ID</th>\n",
+       "      <th>Requested Object's ID</th>\n",
+       "      <th>Requested Object's type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]</td>\n",
+       "      <td>I want to do data exploration</td>\n",
+       "      <td>&lt;UID: 3ece116558bf41cfb4680847173b6c41&gt;</td>\n",
+       "      <td>&lt;UID: 6e2fecb392834b6589f616b7c19cd72f&gt;</td>\n",
+       "      <td>&lt;Budget&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "[<RequestMessage: 3ece116558bf41cfb4680847173b6c41>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# See all the requests made to the domain node\n",
+    "domain_node.requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf3d6c9c-493e-49a7-bcaf-f8aa519580ba",
+   "metadata": {},
+   "source": [
+    "Now, the Data Owner could investigate this request:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3bd573af-ffbd-4a2c-8333-315e0169a32f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I want to do data exploration'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "domain_node.requests[0].request_description"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c83a73-afdb-47cb-ace7-da82b4a91fb8",
+   "metadata": {},
+   "source": [
+    "Let's say he decides to turn it down due to how much privacy budget is requested. He would run this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b0aef9d8-6a76-4c09-b472-150848fd5f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain_node.requests[0].deny()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c082af80-b4b0-448f-a8f4-fc75d04cb371",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain_node.requests[0].reject()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e2a6f80-f97b-4c8b-9f3d-9fe0b6d84979",
+   "metadata": {},
+   "source": [
+    "If instead he wanted to accept the request, he could instead run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e5afb00e-2066-41c0-88ad-cb0941cfeb9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain_node.requests[0].accept()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5432f70-f755-48c5-8699-d0f7dddd42bc",
+   "metadata": {},
+   "source": [
+    "It's important to note that after each grant or refusal, the request disappears from the object store and is no longer visible.\n",
+    "\n",
+    "![alt text](ui_images/no_requests.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bcda20d-6003-42b8-80dc-4b764f3ea2c3",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "152d1127-f5fc-4087-9268-bd1c01a9dac4",
+   "metadata": {},
+   "source": [
+    "## Concept 4. Network Nodes!\n",
+    "\n",
+    "A Network Node is a level of abstraction above a domain node. It's a server which exists outside of any data owner's institution, providing services to a network fo data owners and data scientists, such as dataset searching and bulk project approval (the ability to participate in projects across groups of domains and data scientsts at a time).\n",
+    "\n",
+    "We can view the available networks, and put in a request to join them via the UI:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c6424c2-2809-4786-8eee-5280eb33cc63",
+   "metadata": {},
+   "source": [
+    "![alt text](ui_images/networks.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24b12e20-14f2-4b83-a256-422a24253fdf",
+   "metadata": {},
+   "source": [
+    "We can also do this via PySyft. To see the list of available networks we could join, we simply need to do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ae83711a-b72f-439b-8527-dc4a55560af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>host_or_ip</th>\n",
+       "      <th>vpn_host_or_ip</th>\n",
+       "      <th>protocol</th>\n",
+       "      <th>port</th>\n",
+       "      <th>admin_email</th>\n",
+       "      <th>website</th>\n",
+       "      <th>slack</th>\n",
+       "      <th>slack_channel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>omnet</td>\n",
+       "      <td>13.64.187.229</td>\n",
+       "      <td>100.64.0.1</td>\n",
+       "      <td>http</td>\n",
+       "      <td>80</td>\n",
+       "      <td>support@openmined.org</td>\n",
+       "      <td>https://www.openmined.org/</td>\n",
+       "      <td>https://slack.openmined.org/</td>\n",
+       "      <td>omnet</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>United Nations PET Lab (Mock)</td>\n",
+       "      <td>13.64.14.221</td>\n",
+       "      <td>100.64.0.1</td>\n",
+       "      <td>http</td>\n",
+       "      <td>80</td>\n",
+       "      <td>support@openmined.org</td>\n",
+       "      <td>https://www.openmined.org/</td>\n",
+       "      <td>https://slack.openmined.org/</td>\n",
+       "      <td>petlab</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<syft.registry.NetworkRegistry at 0x7fbb07e938b0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sy.networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c0ff222-26ab-466e-b6bd-f3dab4dad402",
+   "metadata": {},
+   "source": [
+    "To join one, we would call:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8c19b159-c6df-47ab-8e44-84034ffa98f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connecting to http://13.64.14.221... done! \t Logging into united_nations_mock... as GUEST...done!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<NetworkClient: united_nations_mock>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "un_network = sy.networks[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deaec0c0-9404-4365-8213-92c6e5a6e964",
+   "metadata": {},
+   "source": [
+    "A few things to note here- \n",
+    "- We joined the Network as a Guest. This has limited capabilities.\n",
+    "- Instead of indexing by its position in the networks, we could also index by name\n",
+    "\n",
+    "- <b> Upon selecting a network, we have access to all the public properties of the network!</b>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34d67d98-1ea2-423c-9c25-2f7cc730dee5",
+   "metadata": {},
+   "source": [
+    "After joining the network node, we can see all the domains that are attached to that network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "93b276ae-8e1e-4c3c-b29e-41858ad53dbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\t     opengrid network"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>host_or_ip</th>\n",
+       "      <th>id</th>\n",
+       "      <th>is_vpn</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100.64.0.3</td>\n",
+       "      <td>84a28770a17d4b6199d78d66b3bdc8c3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>relaxed_song</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>100.64.0.1</td>\n",
+       "      <td>a2f53454633f4896852aae45d7bc4096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>strange_he</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100.64.0.4</td>\n",
+       "      <td>201e5de97fc94b53a39b37a729efbb69</td>\n",
+       "      <td>1</td>\n",
+       "      <td>kind_kaliouby</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<NetworkClient: opengrid>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opengrid = sy.networks[0]\n",
+    "opengrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8243d0bc-3cff-4d5e-8b01-a23da83e04f2",
+   "metadata": {},
+   "source": [
+    "Let's try to join one of these domains. We start by indexing into it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afcf7957-9a9c-44a8-bf8c-4ec19f5c0eea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pclient = opengrid[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dab09d83-2f6e-4384-9dc2-69977bceaa88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(This is a logged out ProxyClient() object for a domain called 'kind_kaliouby'. Please call .login(email, password) to get a full client you can use for stuff.)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pclient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2c64318-35b2-4086-bde2-5de21879c5a7",
+   "metadata": {},
+   "source": [
+    "We then we login as normal, as we would do to any domain!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "66f0da43-d60a-41be-859e-06d2c606593e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logged in to kind_kaliouby as info@openmined.org\n"
+     ]
+    }
+   ],
+   "source": [
+    "client = pclient.login(email=\"info@openmined.org\", password=\"changethis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7177ffd3-17da-4ac5-a617-5d5ec8199ede",
+   "metadata": {},
+   "source": [
+    "Now we can view datasets, and use this domain node like we would any other:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "388d28ae-c830-4ef7-93c7-f0e674a7dbac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "(no datasets found)"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.dataset_api.DatasetRequestAPI at 0x7fe8a22b0130>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client.datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fda5d207-1f49-4da6-851c-7179c5d849e9",
+   "metadata": {},
+   "source": [
+    "In the event that we wanted our domain node to join a network, we could proceed as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "316ced1e-9690-4720-8850-28c274c303e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: CHANGE YOUR USERNAME AND PASSWORD!!! \n",
+      "\n",
+      "Anyone can login as an admin to your node right now because your password is still the default PySyft username and password!!!\n",
+      "\n",
+      "Connecting to None... done! \t Logging into ecstatic_chollet... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Login to our local domain node\n",
+    "domain = sy.login(email=\"info@openmined.org\", password=\"changethis\", port=8081)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "73412218-3132-46da-bfec-4b7d704cbb7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔌 <DomainClient - kind_kaliouby: <UID: 201e5de97fc94b53a39b37a729efbb69>> successfully connected to the VPN: http://40.83.192.48:80/api/v1\n",
+      "Waiting to connect to VPN.\n",
+      "Connected to VPN\n",
+      "Application submitted.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Applying to join is as simple as a one liner.\n",
+    "domain.apply_to_network(opengrid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "021df7bb-46a7-47e3-ad4f-66b920b74fad",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "In this notebook, you created a new user, learned how to change the user permissions, file and respond to requests, and even join a network node!\n",
+    "In the next lesson, we'll be giving you a bigger taste of remote data science, and what you can do with it. Stay tuned!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "695b6f6e-0bcb-4939-9f6e-602f28fe8b2d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<hr>\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46d6b0fa-77c3-4adf-a963-7f18bdd697e5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/L3_DataPreparation.ipynb b/L3_DataPreparation.ipynb
new file mode 100644
index 0000000..bad438c
--- /dev/null
+++ b/L3_DataPreparation.ipynb
@@ -0,0 +1,1722 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "089363e9-1bf8-4ace-8487-0670192ad039",
+   "metadata": {},
+   "source": [
+    "# Lesson 3: Data Preparation\n",
+    "<b><u>Instructors</b></u>: Ishan Mishra, Phil Culliton\n",
+    "\n",
+    "\n",
+    "<br>\n",
+    "Data preparation is particularly important when doing Remote Data Science and Machine Learning. This is because the data scientist doesn't inherently have the ability to freely check how clean the data is, and whether it needs pre-processing. As such, the responsibility falls onto the Data Owner to ensure the data is clean, annotated, and usable.\n",
+    "\n",
+    "\n",
+    "In this notebook, you'll walk in the steps of a Data Owner (someone who has new and potentially sensitive information) and learn about:\n",
+    "<ul>\n",
+    "    <li> Data Acquisition </li>\n",
+    "    <li> Quality Checks </li>\n",
+    "    <li> Annotation </li>\n",
+    "    <li> Converting your data to a PyGrid compatible format </li>\n",
+    "    <li> How to load data into the node </li>\n",
+    "    <li> Linking data from multiple sources </li>\n",
+    "</ul>\n",
+    "\n",
+    "\n",
+    "Let's get started!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8c5cd9b-1ffa-4f9e-b585-3493d897bc60",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f3eebeb-6139-4fcf-b7f0-cf2bd5c0d4b4",
+   "metadata": {},
+   "source": [
+    "### Here's what you'll need to install for this lesson\n",
+    "\n",
+    "In this lesson, we'll be using the new library `matplotlib`. If you haven't already, please install it using:\n",
+    "\n",
+    "`conda install matplotlib`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "650a2141-63fa-4248-a0ec-9323e779d6fb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## 3.1 Data Acquisition!\n",
+    "\n",
+    "Data Acquisition focuses on generating and capturing data into a system. Broadly speaking, it's made up of two phases: <b> data harvesting</b>, and <b>data ingestion</b>. The former, we'll cover in this lesson, and the latter, we'll show you over the next two lessons.\n",
+    "\n",
+    "### 3.1.1 Data Harvesting\n",
+    "\n",
+    "In this course, we're using generated or pre-gathered datasets, but in the real world data comes in many forms and contexts — we'll cover some in the next section. How do you get your own data? The phrase \"data harvesting\" might sound like a callback to movies about robotic uprisings, but the wording is truly operative! In this process we are:\n",
+    "\n",
+    "1. Identifying sources of potentially fruitful data\n",
+    "2. Building a process to gather it\n",
+    "3. Running that process\n",
+    "4. Carefully checking the outcomes\n",
+    "5. Putting the data in a shareable format\n",
+    "6. Delivering the data to intended recipient\n",
+    "\n",
+    "Thinking of the data gathering process as akin to a farmer's harvest is actually a useful framing of the relevant tasks and problems.\n",
+    "\n",
+    "- There may be better sources of data\n",
+    "- The data may be flawed or need care and pruning\n",
+    "- The data and format should align with the intended recipient's needs\n",
+    "- etc.\n",
+    "\n",
+    "The key problem here, however, is `Is this the data that we need?` No amount of harvesting improvements or fancy formatting can solve for data that doesn't suit the problem you're trying to solve in some key aspect. To help with this, let's consider some useful ways to think about data.\n",
+    "\n",
+    "### 3.1.2 The 4 V's of Data \n",
+    "\n",
+    "When it comes to thinking about data, there are usually four major things to think about, commonly referred to in the industry as the 4 V's of data. They are:\n",
+    "\n",
+    "<ol>\n",
+    "<li><b> Volume </b> refers to the quantity or amount of data in question.\n",
+    "An example of low volume: if you're collecting sensitive data about people with a rare condition.\n",
+    "An example of high volume would be most social media applications that you've heard of. For instance, Facebook has more users than China has people. And each of those people are making posts, uploading pictures, liking content — that adds up to trillions of photos that they can use for data science and machine learning. </li>\n",
+    "<br>\n",
+    "<li><b> Velocity </b> refers to the rate at which new data is being gathered or collected.\n",
+    "For instance, if you're a company, performance reviews might only come once a quarter.\n",
+    "But if you're YouTube, then in one day, you have over 700,000 hours of new videos added. For context, that's longer than the average human lifespan. So if a new person was born tomorrow, and they spent every moment of their life just trying to watch the YouTube videos uploaded on the day they were born, on average they wouldn't be able to get through them all. </li>\n",
+    "<br>\n",
+    "<li><b> Variety </b> refers to the diversity of the data that's being collected. For example, think of the difference between a dataset consisting of polls, and a dataset consisting of emails. No two emails are necessarily quite the same. They could contain quite literally, anything — text about anything, pictures of anything, attachments of any kind, etc. </li>\n",
+    "<br>\n",
+    "\n",
+    "<li><b> Value </b> refers to the idea that not all kinds of data are of equal value. Let's say you're collecting medical images, and some of the images were corrupted during the acquisition process, and were blurry and grainy as a result. That data isn't quite as valuable as a pristine scan.</li>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d908198-3f7f-4cb3-bea3-587026333b8e",
+   "metadata": {},
+   "source": [
+    "### 3.1.3 Hands-on\n",
+    "In our case, let's say we use data concerning the number of COVID cases per country. Let's load it and take a look at the first few rows!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c0a85288-4f43-4732-98b1-1b9e3bc6eae5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "import pandas as pd\n",
+    "raw_data = pd.read_csv(\"dataset/L3_raw_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "03a5c8e2-642c-4686-921c-51b9ca9ab89b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "      <td>88</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1030</td>\n",
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+       "      <td>1042</td>\n",
+       "      <td>568</td>\n",
+       "      <td>930</td>\n",
+       "      <td>1044</td>\n",
+       "      <td>2352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1140</td>\n",
+       "      <td>1113</td>\n",
+       "      <td>3099</td>\n",
+       "      <td>92</td>\n",
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+       "      <td>284</td>\n",
+       "      <td>1095</td>\n",
+       "      <td>44</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>465</td>\n",
+       "      <td>522</td>\n",
+       "      <td>1176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1211</td>\n",
+       "      <td>1378</td>\n",
+       "      <td>2821</td>\n",
+       "      <td>113</td>\n",
+       "      <td>575</td>\n",
+       "      <td>319</td>\n",
+       "      <td>267</td>\n",
+       "      <td>295</td>\n",
+       "      <td>1167</td>\n",
+       "      <td>52</td>\n",
+       "      <td>...</td>\n",
+       "      <td>577</td>\n",
+       "      <td>231</td>\n",
+       "      <td>655</td>\n",
+       "      <td>449</td>\n",
+       "      <td>224</td>\n",
+       "      <td>600</td>\n",
+       "      <td>224</td>\n",
+       "      <td>577</td>\n",
+       "      <td>492</td>\n",
+       "      <td>1258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1238</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>107</td>\n",
+       "      <td>520</td>\n",
+       "      <td>265</td>\n",
+       "      <td>296</td>\n",
+       "      <td>316</td>\n",
+       "      <td>1186</td>\n",
+       "      <td>42</td>\n",
+       "      <td>...</td>\n",
+       "      <td>473</td>\n",
+       "      <td>209</td>\n",
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+       "      <td>473</td>\n",
+       "      <td>231</td>\n",
+       "      <td>607</td>\n",
+       "      <td>275</td>\n",
+       "      <td>407</td>\n",
+       "      <td>421</td>\n",
+       "      <td>1109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1093</td>\n",
+       "      <td>2075</td>\n",
+       "      <td>2964</td>\n",
+       "      <td>117</td>\n",
+       "      <td>578</td>\n",
+       "      <td>371</td>\n",
+       "      <td>359</td>\n",
+       "      <td>349</td>\n",
+       "      <td>1151</td>\n",
+       "      <td>48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>550</td>\n",
+       "      <td>227</td>\n",
+       "      <td>648</td>\n",
+       "      <td>447</td>\n",
+       "      <td>232</td>\n",
+       "      <td>563</td>\n",
+       "      <td>188</td>\n",
+       "      <td>338</td>\n",
+       "      <td>432</td>\n",
+       "      <td>1148</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 175 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0     1     2    3     4    5    6    7     8   9  ...   165  166   167  \\\n",
+       "0  2280  2226  6198  184  1242  688  566  568  2190  88  ...  1030  414  1444   \n",
+       "1  1140  1113  3099   92   621  344  283  284  1095  44  ...   515  207   722   \n",
+       "2  1211  1378  2821  113   575  319  267  295  1167  52  ...   577  231   655   \n",
+       "3  1238  1587  2356  107   520  265  296  316  1186  42  ...   473  209   620   \n",
+       "4  1093  2075  2964  117   578  371  359  349  1151  48  ...   550  227   648   \n",
+       "\n",
+       "   168  169   170  171  172   173   174  \n",
+       "0  802  468  1042  568  930  1044  2352  \n",
+       "1  401  234   521  284  465   522  1176  \n",
+       "2  449  224   600  224  577   492  1258  \n",
+       "3  473  231   607  275  407   421  1109  \n",
+       "4  447  232   563  188  338   432  1148  \n",
+       "\n",
+       "[5 rows x 175 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ee93c43-ee4f-4848-ba08-4a31b7366805",
+   "metadata": {},
+   "source": [
+    "In this dataset, each column corresponds to a <b> country</b>, each row corresponds to a new <b> month </b> where data was collected, and each value in this DataFrame corresponds to the number of COVID19 cases in the country at the start of that month. \n",
+    "\n",
+    "So for instance, Country 0 had 2280 COVID cases at the start of when this data was collected (row 0), and only 451 when the data was last collected (row 53)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "58d63f95-0b88-428d-94a9-58bb588aea0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2280, 451)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[\"0\"][0], raw_data[\"0\"][53]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c3080f0-973d-4437-a133-21193e315bc3",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4727484-454a-4a96-b9e1-a806997137a4",
+   "metadata": {},
+   "source": [
+    "## 3.2 Quality Check\n",
+    "Checking the quality of a dataset can involve finding missing values, identifying outliers and anomalies (using methods such as an [Isolation Forest](https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html) or [k-Nearest Neighbours](https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py)) or visualizing the dataset. \n",
+    "It might also involve using external information that we know — for instance, about the sources or about how the data was collected.\n",
+    "\n",
+    "Let's walk through an example using the dataset again:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0001c907-e0c5-4a3c-b4ab-c7562e06c638",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th>166</th>\n",
+       "      <th>167</th>\n",
+       "      <th>168</th>\n",
+       "      <th>169</th>\n",
+       "      <th>170</th>\n",
+       "      <th>171</th>\n",
+       "      <th>172</th>\n",
+       "      <th>173</th>\n",
+       "      <th>174</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2280</td>\n",
+       "      <td>2226</td>\n",
+       "      <td>6198</td>\n",
+       "      <td>184</td>\n",
+       "      <td>1242</td>\n",
+       "      <td>688</td>\n",
+       "      <td>566</td>\n",
+       "      <td>568</td>\n",
+       "      <td>2190</td>\n",
+       "      <td>88</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1030</td>\n",
+       "      <td>414</td>\n",
+       "      <td>1444</td>\n",
+       "      <td>802</td>\n",
+       "      <td>468</td>\n",
+       "      <td>1042</td>\n",
+       "      <td>568</td>\n",
+       "      <td>930</td>\n",
+       "      <td>1044</td>\n",
+       "      <td>2352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1140</td>\n",
+       "      <td>1113</td>\n",
+       "      <td>3099</td>\n",
+       "      <td>92</td>\n",
+       "      <td>621</td>\n",
+       "      <td>344</td>\n",
+       "      <td>283</td>\n",
+       "      <td>284</td>\n",
+       "      <td>1095</td>\n",
+       "      <td>44</td>\n",
+       "      <td>...</td>\n",
+       "      <td>515</td>\n",
+       "      <td>207</td>\n",
+       "      <td>722</td>\n",
+       "      <td>401</td>\n",
+       "      <td>234</td>\n",
+       "      <td>521</td>\n",
+       "      <td>284</td>\n",
+       "      <td>465</td>\n",
+       "      <td>522</td>\n",
+       "      <td>1176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1211</td>\n",
+       "      <td>1378</td>\n",
+       "      <td>2821</td>\n",
+       "      <td>113</td>\n",
+       "      <td>575</td>\n",
+       "      <td>319</td>\n",
+       "      <td>267</td>\n",
+       "      <td>295</td>\n",
+       "      <td>1167</td>\n",
+       "      <td>52</td>\n",
+       "      <td>...</td>\n",
+       "      <td>577</td>\n",
+       "      <td>231</td>\n",
+       "      <td>655</td>\n",
+       "      <td>449</td>\n",
+       "      <td>224</td>\n",
+       "      <td>600</td>\n",
+       "      <td>224</td>\n",
+       "      <td>577</td>\n",
+       "      <td>492</td>\n",
+       "      <td>1258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1238</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>107</td>\n",
+       "      <td>520</td>\n",
+       "      <td>265</td>\n",
+       "      <td>296</td>\n",
+       "      <td>316</td>\n",
+       "      <td>1186</td>\n",
+       "      <td>42</td>\n",
+       "      <td>...</td>\n",
+       "      <td>473</td>\n",
+       "      <td>209</td>\n",
+       "      <td>620</td>\n",
+       "      <td>473</td>\n",
+       "      <td>231</td>\n",
+       "      <td>607</td>\n",
+       "      <td>275</td>\n",
+       "      <td>407</td>\n",
+       "      <td>421</td>\n",
+       "      <td>1109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1093</td>\n",
+       "      <td>2075</td>\n",
+       "      <td>2964</td>\n",
+       "      <td>117</td>\n",
+       "      <td>578</td>\n",
+       "      <td>371</td>\n",
+       "      <td>359</td>\n",
+       "      <td>349</td>\n",
+       "      <td>1151</td>\n",
+       "      <td>48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>550</td>\n",
+       "      <td>227</td>\n",
+       "      <td>648</td>\n",
+       "      <td>447</td>\n",
+       "      <td>232</td>\n",
+       "      <td>563</td>\n",
+       "      <td>188</td>\n",
+       "      <td>338</td>\n",
+       "      <td>432</td>\n",
+       "      <td>1148</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 175 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0     1     2    3     4    5    6    7     8   9  ...   165  166   167  \\\n",
+       "0  2280  2226  6198  184  1242  688  566  568  2190  88  ...  1030  414  1444   \n",
+       "1  1140  1113  3099   92   621  344  283  284  1095  44  ...   515  207   722   \n",
+       "2  1211  1378  2821  113   575  319  267  295  1167  52  ...   577  231   655   \n",
+       "3  1238  1587  2356  107   520  265  296  316  1186  42  ...   473  209   620   \n",
+       "4  1093  2075  2964  117   578  371  359  349  1151  48  ...   550  227   648   \n",
+       "\n",
+       "   168  169   170  171  172   173   174  \n",
+       "0  802  468  1042  568  930  1044  2352  \n",
+       "1  401  234   521  284  465   522  1176  \n",
+       "2  449  224   600  224  577   492  1258  \n",
+       "3  473  231   607  275  407   421  1109  \n",
+       "4  447  232   563  188  338   432  1148  \n",
+       "\n",
+       "[5 rows x 175 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8913dbd4-dfa6-40e7-acf2-2285d401d79d",
+   "metadata": {},
+   "source": [
+    "Now let's say, for instance, that when we were given the dataset, we were told that a lot of immigration and emmigration happened in the first month of this dataset, and as such a lot of double counting of COVID19 cases happened, and the global numbers of COVID19 were reported as twice of what they really were.\n",
+    "\n",
+    "We could try to naively tackle this by just dividing the data from the first month by half. Let's try it out and see what happens!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "31f5b683-61cc-4390-b8bb-ed6e0dfae50b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divide the data from the first month by half\n",
+    "raw_data.iloc[0] /= 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72f5b5ee-a7a6-40b1-a875-72e344e2cab2",
+   "metadata": {},
+   "source": [
+    "Now let's try to visualize the dataset and see if anything suspicious looking appears:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5abf254c-7063-4d37-8b44-88964fa05d33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>165</th>\n",
+       "      <th>166</th>\n",
+       "      <th>167</th>\n",
+       "      <th>168</th>\n",
+       "      <th>169</th>\n",
+       "      <th>170</th>\n",
+       "      <th>171</th>\n",
+       "      <th>172</th>\n",
+       "      <th>173</th>\n",
+       "      <th>174</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1140</td>\n",
+       "      <td>1113</td>\n",
+       "      <td>3099</td>\n",
+       "      <td>92</td>\n",
+       "      <td>621</td>\n",
+       "      <td>344</td>\n",
+       "      <td>283</td>\n",
+       "      <td>284</td>\n",
+       "      <td>1095</td>\n",
+       "      <td>44</td>\n",
+       "      <td>...</td>\n",
+       "      <td>515</td>\n",
+       "      <td>207</td>\n",
+       "      <td>722</td>\n",
+       "      <td>401</td>\n",
+       "      <td>234</td>\n",
+       "      <td>521</td>\n",
+       "      <td>284</td>\n",
+       "      <td>465</td>\n",
+       "      <td>522</td>\n",
+       "      <td>1176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1140</td>\n",
+       "      <td>1113</td>\n",
+       "      <td>3099</td>\n",
+       "      <td>92</td>\n",
+       "      <td>621</td>\n",
+       "      <td>344</td>\n",
+       "      <td>283</td>\n",
+       "      <td>284</td>\n",
+       "      <td>1095</td>\n",
+       "      <td>44</td>\n",
+       "      <td>...</td>\n",
+       "      <td>515</td>\n",
+       "      <td>207</td>\n",
+       "      <td>722</td>\n",
+       "      <td>401</td>\n",
+       "      <td>234</td>\n",
+       "      <td>521</td>\n",
+       "      <td>284</td>\n",
+       "      <td>465</td>\n",
+       "      <td>522</td>\n",
+       "      <td>1176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1211</td>\n",
+       "      <td>1378</td>\n",
+       "      <td>2821</td>\n",
+       "      <td>113</td>\n",
+       "      <td>575</td>\n",
+       "      <td>319</td>\n",
+       "      <td>267</td>\n",
+       "      <td>295</td>\n",
+       "      <td>1167</td>\n",
+       "      <td>52</td>\n",
+       "      <td>...</td>\n",
+       "      <td>577</td>\n",
+       "      <td>231</td>\n",
+       "      <td>655</td>\n",
+       "      <td>449</td>\n",
+       "      <td>224</td>\n",
+       "      <td>600</td>\n",
+       "      <td>224</td>\n",
+       "      <td>577</td>\n",
+       "      <td>492</td>\n",
+       "      <td>1258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1238</td>\n",
+       "      <td>1587</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>107</td>\n",
+       "      <td>520</td>\n",
+       "      <td>265</td>\n",
+       "      <td>296</td>\n",
+       "      <td>316</td>\n",
+       "      <td>1186</td>\n",
+       "      <td>42</td>\n",
+       "      <td>...</td>\n",
+       "      <td>473</td>\n",
+       "      <td>209</td>\n",
+       "      <td>620</td>\n",
+       "      <td>473</td>\n",
+       "      <td>231</td>\n",
+       "      <td>607</td>\n",
+       "      <td>275</td>\n",
+       "      <td>407</td>\n",
+       "      <td>421</td>\n",
+       "      <td>1109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1093</td>\n",
+       "      <td>2075</td>\n",
+       "      <td>2964</td>\n",
+       "      <td>117</td>\n",
+       "      <td>578</td>\n",
+       "      <td>371</td>\n",
+       "      <td>359</td>\n",
+       "      <td>349</td>\n",
+       "      <td>1151</td>\n",
+       "      <td>48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>550</td>\n",
+       "      <td>227</td>\n",
+       "      <td>648</td>\n",
+       "      <td>447</td>\n",
+       "      <td>232</td>\n",
+       "      <td>563</td>\n",
+       "      <td>188</td>\n",
+       "      <td>338</td>\n",
+       "      <td>432</td>\n",
+       "      <td>1148</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 175 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0     1     2    3    4    5    6    7     8   9  ...  165  166  167  \\\n",
+       "0  1140  1113  3099   92  621  344  283  284  1095  44  ...  515  207  722   \n",
+       "1  1140  1113  3099   92  621  344  283  284  1095  44  ...  515  207  722   \n",
+       "2  1211  1378  2821  113  575  319  267  295  1167  52  ...  577  231  655   \n",
+       "3  1238  1587  2356  107  520  265  296  316  1186  42  ...  473  209  620   \n",
+       "4  1093  2075  2964  117  578  371  359  349  1151  48  ...  550  227  648   \n",
+       "\n",
+       "   168  169  170  171  172  173   174  \n",
+       "0  401  234  521  284  465  522  1176  \n",
+       "1  401  234  521  284  465  522  1176  \n",
+       "2  449  224  600  224  577  492  1258  \n",
+       "3  473  231  607  275  407  421  1109  \n",
+       "4  447  232  563  188  338  432  1148  \n",
+       "\n",
+       "[5 rows x 175 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# See the resultant data\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36db95d0-22a3-4a20-9593-f904bf7fcfca",
+   "metadata": {},
+   "source": [
+    "Well that's odd — the data from the first month and second month appear to be exactly the same. If this happened to just one or two countries, that might be plausible, but for every country? That seems unlikely.\n",
+    "\n",
+    "There likely was an error when transferring this data, and a duplication error occurred. We can fix this by simply removing the first month from our dataset, and starting our analysis from the second month (row 1 in the table above), and onwards."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d33b2214-8fe5-4787-8d56-5c5aa29acf54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove duplicated row\n",
+    "raw_data.drop([0], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8326c068-5e4a-40cf-ba7b-3e707469a1a3",
+   "metadata": {},
+   "source": [
+    "At this point, we might decide to visualize our dataset and see if there are any obvious outliers or anomalies. Let's look at the first country!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ddd7fef2-7ca3-440c-8bbd-6e17690f0db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d31e7c01-7a4b-428c-8a5b-78a344bb9366",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fae8bbcf280>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(raw_data[\"24\"], \"r-*\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ed96e05f-68c7-4390-8571-6c481fe5818f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[\"0\"].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bb0378da-80cd-43e4-bb0e-1778b132785e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def plot_extrapolated_country(idx):\n",
+    "    x = list(range(53))\n",
+    "    y = raw_data[str(idx)]\n",
+    "    \n",
+    "    plt.plot(y)\n",
+    "    \n",
+    "    for x1, y1 in zip(x, y):\n",
+    "        plt.plot(x1, y1) #, 'ro')\n",
+    "\n",
+    "    z = np.polyfit(x, y, 2)\n",
+    "    f = np.poly1d(z)\n",
+    "\n",
+    "    new_points = range(12)\n",
+    "    for x2 in new_points:\n",
+    "        plt.plot(55 + x2, f(55 + x2), 'bo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f252c345-c5e2-4f00-83c1-5f24f5992656",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZCoakp7LoiknxTkVOQ4VCRPrFK7vr+MWfd7G37vibsV0HG/mfV1/nE3MmMjI7I47ZSWc0aiQi/eJbv9vC+r1H+Nr/vMaMMXm8f8YoNr9+lPTUFD7zzpJ4pyedUKEQkT7X3u6UVzfwwQtGc+G44Ty1+QA/+sN23OGT7yjmnNyseKconVChEJE+t+/IGzQ2t/GOcwv4+KUTuOXKSdTUN/HSzkO89216cGWiU6EQkT63vboBgGmjct6MnZOXxfyZY0+3iyQQDWaLSJ/beiBSKKYW5cY5E+kOFQoR6XPbqxsYO3wIuVn6nUQyUqEQkT637UADU4tyzryhJCQVChHpUy1t7eysbWTqKF12SlYqFCLSpyoPNdLc1s40jU8krW4XCjObZmbro6Z6M/t7M/uqme2Lin8gap87zazCzLaZ2fuj4vNCrMLM7oiKl5jZ6hB/0Mz0002RJHNyIHuaziiSVrcLhbtvc/eZ7j4TmAUcBx4Nq390cp27PwFgZtOBG4AZwDzgP80s1cxSgf8ArgGmAzeGbQG+G441GTgMLOpuviISH9sPNJBicG6hxiiSVW9denovsMPdKzvZZj6wwt1PuPsuoAKYHaYKd9/p7s3ACmC+mRlwFfBw2H8ZcG0v5Ssi/WRbdQPFBdlkpafGOxXppt4qFDcAD0Qt32ZmG8xsqZmNCLGxwN6obapC7HTxfOCIu7d2iL+FmS02szIzK6utre35pxGRXrO9+pjGJ5JcjwtFGDf4EPCrELoLOBeYCewHftDT9zgTd1/i7qXuXlpYWNjXbyciXfRGcxu7DzXqh3ZJrjdu4XENsNbdqwFOvgKY2d3Ab8PiPmB81H7jQozTxA8Bw80sLZxVRG8vIkmgouYY7nCeBrKTWm9cerqRqMtOZjY6at2HgU1h/nHgBjPLNLMSYArwMvAKMCV0OGUQuYz1uLs78CxwXdh/IfBYL+QrIv1kW7jHk35Dkdx6dEZhZtnAXOCzUeHvmdlMwIHdJ9e5+2Yzewh4DWgFbnX3tnCc24CngVRgqbtvDsf6ErDCzL4JrAPu6Um+ItK/tlc3kJGWwsSRQ+OdivRAjwqFuzcSGXSOjt3cyfbfAr4VI/4E8ESM+E4iXVEikoS2HWhgcmEOaan6bW8y0389Eekz2w40aHxiAFChEJE+cfR4CwfqmzQ+MQCoUIhIn9heE27dodbYpKdCISJ9YtsBdTwNFCoUItInth1oIDczjTHDsuKdivSQCoWI9Ilt1Q1MHZVL5LZtksxUKESk17k726sbdOuOAUKFQkR6XW3DCY4cb2GaHn86IKhQiEiv27y/HtBA9kChQiEive436/aRm5nGzPHD452K9AIVChHpVQePneCJjfv5yKxxDM3ojRtUS7ypUIhIr3rwlb20tDmfmDMx3qlIL1GhEJFe09buLF9VyTvOzWfyORrIHihUKESk16zcUs3rR5tYcJnOJgYSXUAUkZhq6pt4aechNu07yuySfN4zrfCMtwv/5apKRuVl8b63FfVTltIfVChEhCPHm9l5sJGdtY2s33uYv+w4xM7aRgBSDO5+YReFuZl85OJxfOyS8ZQUZL/lGDtrj/FC+UFunztVz58YYFQoRAapbQca+JfHNlFe3cDh4y1vxrMzUpldMpIbLhnPZZMKmDoqh+e3H+TBV/Zw9ws7+dmfdvDOKQV8ff75pxSM5av3kJZi3DB7fDw+jvQhFQqRQcjdueORDew62MgH3j6aSQXZlIRpwsihbzkjmDu9iLnTi6iub+LhNVX87E87mPdvz/NP75/Gpy4vobm1nV+V7WXe+aM4J1c3ARxoelwozGw30AC0Aa3uXmpmI4EHgWIiz82+3t0PW+TuYD8GPgAcBz7p7mvDcRYC/zcc9pvuvizEZwH3AkOIPC71C+7uPc1bZDB7/NXXWbfnCN/7yAVcf0nXzwCK8rK49T2TuW7WOL786Ea++bst/G7jfuZMyqe+qZUFlxX3XdISN711IfE97j7T3UvD8h3ASnefAqwMywDXAFPCtBi4CyAUlq8AlxJ5RvZXzGxE2Ocu4Jao/eb1Us4ig1JTSxvffXIrM8bk8ZFZ47p1jKK8LO5eUMqPb5jJroON3PXcDqYV5XJJ8Ygz7yxJp68uPc0H3h3mlwHPAV8K8fvCGcEqMxtuZqPDts+4ex2AmT0DzDOz54A8d18V4vcB1wJP9lHeIgPe3c/v5PWjTfzwYzNJTen+LcDNjPkzx/KOcwv46R/LmTt9lG4pPkD1RqFw4Pdm5sB/ufsSoMjd94f1B4CTvXJjgb1R+1aFWGfxqhjxU5jZYiJnKEyYMKGnn0dkwKqub+KuP+1g3oxRzJmU3yvHLMzN5Gvzz++VY0li6o1CcYW77zOzc4BnzGxr9Ep391BE+kwoTksASktLNX4hchrff3obrW3OnR84L96pSBLp8RiFu+8LrzXAo0TGGKrDJSXCa03YfB8QPXI2LsQ6i4+LEReRs7Sx6ii/XlvFpy4vZmL+W38HIXI6PSoUZpZtZrkn54GrgU3A48DCsNlC4LEw/ziwwCLmAEfDJaqngavNbEQYxL4aeDqsqzezOaFjakHUsUSki9ydb/zuNUYOzeDWqybHOx1JMj299FQEPBoGsNKA+939KTN7BXjIzBYBlcD1YfsniLTGVhBpj/0UgLvXmdk3gFfCdl8/ObANfJ6/tsc+iQayRc7a6l11vLyrjq/Pn0FeVnq805Ek06NC4e47gQtjxA8B740Rd+DW0xxrKbA0RrwM0EiZSA8seX4n+dkZXF+qX03L2dMNWUQGuPLqBv64tYYFlxWTlZ4a73QkCalQiAxwS57fSVZ6Cjfr1t/STSoUIgNYdX0Tv1m/j+tLxzMyOyPe6UiSUqEQGcB+8efdtLU7n7liUrxTkSSmQiEyQB070cry1ZVcc/5oJuQPjXc6ksRUKEQGqBUv76GhqZXFV+psQnpGz6MQSXI1DU08sHovY4ZncfHEEUwqyKa13Vn64i4uLRnJheOHxztFSXIqFCJJ7PebD3DHIxupa2x+MzZ8aDoT87N5/WgT3/ywfoIkPadCIZKEjje38o3fvsYDL+9lxpg8ViyegwFr9xxmbeUR1u45zOWT83n31HPinaoMACoUIklmY9VR/m7FOnYfauRz7zqX2+dOJSMtMtw4pSiXj12iW+1L71KhEEkiJ1rb+PSyV0hLMe7/zBwuO7d3nikh0hkVCpEk8vj616ltOMEvF81WkZB+o/ZYkSTh7tzz4i6mFeVyxeSCeKcjg4gKhUiS+MuOQ2w90MCiK0r0bGrpVyoUIkni5y/spCAngw/NHBPvVGSQUaEQSQIVNcd4dlstn5gzUbcKl36nQiGSBJb+eRcZaSl8Yo5uFS79T4VCJEG0tzsvlh9k/9E3TonXNTbzyNoq/uaisRTkZMYpOxnMut0ea2bjgfuIPDfbgSXu/mMz+ypwC1AbNv1nd38i7HMnsAhoA/7O3Z8O8XnAj4FU4Ofu/p0QLwFWAPnAGuBmd//rvQpEuqnxRCv/+VwFdY0tFOZkUJCbSUHOySmynJuZhpnR1u5sr25gTeVh1lYe5rX99UwqzObyyQVcfm4BE/OH9nhwedfBRr748Ku8svswmWkpfPLyYj7/rskMG5rO/asraWpp59NXlPTSpxc5OxZ5jHU3djQbDYx297VmlkvkL/JrgeuBY+7+rx22nw48AMwGxgB/AKaG1duBuUAV8Apwo7u/ZmYPAY+4+woz+xnwqrvf1VlepaWlXlZW1q3PJIPDq3uP8IUV66isO05+dgaHGpuJ9b9BZloKBTmZHH2jhWMnWgEoyMlkxpg8tlc3sP9oEwBjhw/hXdMK+cjF47h4wvCzKhpt4eZ9//r7bWSmpfAPV0/j1b1HeHT9PnIz0/jbd0/mF3/exXmj87jv07N75fOLxGJma9y9NNa6bp9RuPt+YH+YbzCzLcDYTnaZD6xw9xPALjOrIFI0ACrcfWdIdgUwPxzvKuDjYZtlwFeBTguFyOm0tTs/+9MOfvTMds7JzeSBW+YwZ1I+be1OXWMzB4+d+OvUEFmuPXaCoRmpzJo4glkTRjJ+5BDMDHdn18FG/lxxkBcrDvLo2n3cv3oPkwqz+eis8fzNxWMpysuiqaWNQ43N1B1r5vDxZppb22ltb6elzTnR2s4vV1Xy6t4jzJ1exDevPZ+ivCwAbrlyEt97aivffWorAN//qM4mJH565ZfZZlYMXASsBi4HbjOzBUAZ8A/ufphIEVkVtVsVfy0sezvELyVyuemIu7fG2F7krOyoPcadj2zk5V11/K8LRvPta9/OsKHpAKSmGIW5mRTmdv36v5kxqTCHSYU53HxZMcdOtPLEhv38as1evvvUVr7/9FaGpKfS2NzW6XFGZmfw7zdexAcvGH3KmcjbRufxi0/NZtXOQ2x+vZ4rp+gHdhI/PS4UZpYD/Br4e3evN7O7gG8QGbf4BvAD4NM9fZ8z5LAYWAwwYYJuiCYRbe3Oc9tqWPZSJc9vryU7I5V//eiFfOTisb3+g7WczDSuv2Q8118ynl0HG3l03T6ONbWSn5NBfnYGI7MzGJGdQVZaKmmpRnqqkZ6awjm5WQzJOH2765xJ+cyZpFt1SHz1qFCYWTqRIrHc3R8BcPfqqPV3A78Ni/uA8VG7jwsxThM/BAw3s7RwVhG9/SncfQmwBCJjFD35TJL8WtraWfaX3dz3UiV76o5TlJfJ7XOncuPsCWd11tBdJQXZ3D536pk3FEkSPel6MuAeYIu7/zAqPjqMXwB8GNgU5h8H7jezHxIZzJ4CvAwYMCV0OO0DbgA+7u5uZs8C1xHpfFoIPNbdfGVwqGlo4rbl63h5dx2zi0fypXnncfWMItJT1Qku0l09OaO4HLgZ2Ghm60Psn4EbzWwmkUtPu4HPArj75tDF9BrQCtzq7m0AZnYb8DSR9til7r45HO9LwAoz+yawjkhhEonpld113Lp8LQ1Nrfz4hpnMn6khLZHe0O322ESl9tjBx9259y+7+dbvtjBuxBB+dvMszhuVF++0RJJKn7THiiSCTfuO8m9/2M4fttQwd3oRP7j+QvKy0uOdlsiAokIhSalsdx0/fbaC57bVkpuZxp3XnMct75xESopuvy3S21QoJGk0t7azcks19/5lN6t31TEyO4N/ev80br5sos4iRPqQCoUkvC3763mobC+PrX+dusZmRg/L4l8+OJ0bZ49naIa+wiJ9Tf+XSUJpbWtne/UxNlQd4dWqo6ytPMy26gYyUlOYO72I60rHceWUQlJ1iUmk36hQSNzVNTbz+80HeHLTAVbvOkRTSzsAeVlpXDh+ODfMHs+1M8cyIjsjzpmKDE4qFNLrWtva2X3oOFnpKeRkppGdmUZ6agpt7c6hxhPUNpygpuEElQcbeWZLNat21tHW7owfOYQbLpnARROGc8G44RT3wu27RaTnVCik17g7z22r5dtPbKG85tgp6zLTUmhpa6e9w892JhVm87fvOpd5549ixpg8FQaRBKRCIZ1qa/cujQdsfv0o335iC3+uOERJQTbf/vDbSUsxjp1opfFEK8dOtJKZlvLmXVoLc7MYNSyLMcOyVBxEEpwKhbxFS1ukDfX+l/fyYnkto4cNYWpRDtNG5TFtVA7Dh2ZwuLGZujDtOtjIU5sPMGxIOl/539O56dKJZKTp3koiA4UKxSD0RnMbL5TXUl5zjJzMNPKGpJGXlc7QjDReKK/lobIqDh47wehhWXzq8hIOHTvB1gMNvFhxkJa2U68dpaYYI7Mz+MwVJdz2nilvPuNBRAYOFYpBwN2prj/Bc9tq+MOWal4oP8iJ1vaY26YYXHXeOXz80gm8a+o5p1x2amlrZ/fBRuqbWhiZncnIoRnkDUnTpSORAU6FIkm1tLVTXn2MTa8fZdfBRtwhNQVSU1JINaO+qYU9dcfZG6aTT1obO3wIN86ewNzpRVw0YThvNLdR39RK/Rst1De1MPmcHEYPGxLzPdNTU5hSlNufH1NEEoAKRZy0tTut7e1kpKac8V/kTS1tbD3QwKZ9R9n8+lE27atn24EGmtsiZwVpKYZZ5Jgnu4qGpKcyYeRQxo8cwmXn5jNh5FDmTMrnvFG5p7zf0Iw08nP6/mE+IpK8VCj6wNHjLbzR0kZqipGWYqSlGseb23h17xHW7T3Cuj2H2VB1lOPNbZhBVloqWekpZKWnkpmWQmZaKpnpKWSmpdDQ1Ep5zTHaQgUYNiSd88fm8anLi5k+Jo/zxw6jOD/7zUtE7o47mKFLQiLSK1QozqC93amsO85rr9ezZX89lXXHGT9iCOeNzuNto3IpKcimtd1ZU3mY58treWH7QV7bX3/a46WlGDPG5HF96XgKczNpamkLUztvtLTR3NrOidY2TrS209zazuhhWbzvbUWcPzaPGWOGMW7EkE4LgFnk7EJEpLeoUHTik794mZd31XE8XN9PTTFGD8viyY37aQ3/ws9ISyHFoKmlnfRUY9bEEfzj1VPJz8mktd1pbWunrd1JSzHePm4YM8YMIys9NZ4fS0TkrKhQdKKkIJvi/Gymj85j+pg8Jp+TQ1Z6Ks2t7eyoPcbWA/Vs2d9AW7tzxeQCZpeMJDtTf6QiMrDoUagiItLpo1AT/uezZjbPzLaZWYWZ3RHvfEREBpuELhRmlgr8B3ANMB240cymxzcrEZHBJaELBTAbqHD3ne7eDKwA5sc5JxGRQSXRC8VYYG/UclWIncLMFptZmZmV1dbW9ltyIiKDQaIXii5x9yXuXurupYWFhfFOR0RkQEn0QrEPGB+1PC7ERESknyR6oXgFmGJmJWaWAdwAPB7nnEREBpWE/nWYu7ea2W3A00AqsNTdN8c5LRGRQWXA/eDOzGqBym7uXgAc7MV0+kuy5g3Jm7vy7l/Ku+9NdPeYg7wDrlD0hJmVne6XiYksWfOG5M1defcv5R1fiT5GISIicaZCISIinVKhONWSeCfQTcmaNyRv7sq7fynvONIYhYiIdEpnFCIi0ikVChER6ZQKRZAsz70ws6VmVmNmm6JiI83sGTMrD68j4pljLGY23syeNbPXzGyzmX0hxBM6dzPLMrOXzezVkPfXQrzEzFaH78uD4c4BCcfMUs1snZn9NiwnfN5mttvMNprZejMrC7GE/p4AmNlwM3vYzLaa2RYzuywZ8u4KFQqS7rkX9wLzOsTuAFa6+xRgZVhONK3AP7j7dGAOcGv4M0703E8AV7n7hcBMYJ6ZzQG+C/zI3ScDh4FF8UuxU18AtkQtJ0ve73H3mVG/QUj07wnAj4Gn3P084EIif+7JkPeZufugn4DLgKejlu8E7ox3Xp3kWwxsilreBowO86OBbfHOsQuf4TFgbjLlDgwF1gKXEvm1bVqs70+iTERuorkSuAr4LWBJkvduoKBDLKG/J8AwYBehQShZ8u7qpDOKiC499yKBFbn7/jB/ACiKZzJnYmbFwEXAapIg93D5Zj1QAzwD7ACOuHtr2CRRvy//BnwRaA/L+SRH3g783szWmNniEEv070kJUAv8Ilzq+7mZZZP4eXeJCsUA45F/uiRsz7OZ5QC/Bv7e3euj1yVq7u7e5u4zifwLfTZwXnwzOjMz+yBQ4+5r4p1LN1zh7hcTuRR8q5ldGb0yQb8nacDFwF3ufhHQSIfLTAmad5eoUEQk+3Mvqs1sNEB4rYlzPjGZWTqRIrHc3R8J4aTIHcDdjwDPErlkM9zMTt59ORG/L5cDHzKz3UQeIXwVkWvoiZ437r4vvNYAjxIpzon+PakCqtx9dVh+mEjhSPS8u0SFIiLZn3vxOLAwzC8kcv0/oZiZAfcAW9z9h1GrEjp3Mys0s+FhfgiRcZUtRArGdWGzhMvb3e9093HuXkzk+/xHd7+JBM/bzLLNLPfkPHA1sIkE/564+wFgr5lNC6H3Aq+R4Hl3WbwHSRJlAj4AbCdy/fnL8c6nkzwfAPYDLUT+FbOIyLXnlUA58AdgZLzzjJH3FUROuzcA68P0gUTPHbgAWBfy3gT8vxCfBLwMVAC/AjLjnWsnn+HdwG+TIe+Q36th2nzy/8VE/56EHGcCZeG78htgRDLk3ZVJt/AQEZFO6dKTiIh0SoVCREQ6pUIhIiKdUqEQEZFOqVCIiEinVChERKRTKhQiItKp/w/gNr8eQbEzaQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_extrapolated_country(129)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9c67efe9-764f-4ce0-9c71-a5519e949885",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "129"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "thingies = []\n",
+    "for i in range(raw_data.shape[-1]):\n",
+    "    x = range(53)\n",
+    "    y = raw_data[str(i)]\n",
+    "    slope, intercept = np.polyfit(x, y, 1)\n",
+    "    thingies.append(slope) #raw_data.T[i, -1] - raw_data.T[i, 0])\n",
+    "    \n",
+    "thingies.index(max(thingies))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0fdaf6f8-d371-43fe-a064-f5a7a14073a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(raw_data).plot(legend=False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c34d0da1-0e3e-4aaf-b26a-744e9a5c9dac",
+   "metadata": {},
+   "source": [
+    "This seems alright, and doesn't seem to have any clear outliers or anomalies. We could do this for other countries, and maybe even employ other visualization methods such as clustering or PCA, depending on the type of data we're working with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70939805-bc5c-40d6-8fd1-183680c81db2",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0584e48f-0ed6-4e0a-a0a6-465bf1383c91",
+   "metadata": {},
+   "source": [
+    "## 3.3. Data Annotation\n",
+    "\n",
+    "In many cases, it's not just data that we need. It's annotated, or in other words, <b> labelled </b> data. For example, let's say I wanted to train a Neural Network to classify images of lungs based on whether or not the person had a healthy lung. Having the images alone wouldn't be enough; I'd also need a way of knowing which image corresponded to a healthy lung, and which ones don't.\n",
+    "\n",
+    "There are other kinds of annotation as well, such as semantic segmentation, bounding boxes, landmark and keypoint, and others. We won't go into too much detail about these, but feel free to check out some of the external resources and references at the bottom of this notebook!\n",
+    "\n",
+    "### 3.3.1 Common annotation types\n",
+    "\n",
+    "Most often annotations (or *labels*) will take the form of:\n",
+    "1. Binary values (true or false)\n",
+    "2. Classes (discrete states of being — cat or dog or airplane)\n",
+    "3. Continuous values (population counts, dollars and cents, stock market returns)\n",
+    "\n",
+    "In the data we're using here, we've got a good example of number 3 — population counts. While it is bounded by physical realities, in theory it could be any number between -infinity and +infinity. A machine learning model would need to be able to predict anything in that range.\n",
+    "\n",
+    "Binary values and classes are considerably more constrained — while some multi-class problems have thousands (or millions!) of classes, they are all discrete and map to defined states.\n",
+    "\n",
+    "### 3.3.2 Creating annotations\n",
+    "\n",
+    "The process of labelling can be very simple, or very complex, depending on the nature of the annotations. For example, the annotations mentioned at the top of this section — healthy or unhealthy lungs — would most likely need to be:\n",
+    "\n",
+    "1. Diagnosed by a radiologist (or several radiologists)\n",
+    "2. Recorded (and adjudicated when several radiologists were involved)\n",
+    "3. Checked for accuracy\n",
+    "\n",
+    "More complex annotations (like identifying features of an unhealthy lung) would require bounding boxes or segmentations to be drawn as well, which would need to go through the same process.\n",
+    "\n",
+    "On the other hand, dog vs. cat labels might be applied very quickly, using an automated system or by having humans hand-label them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42c468b9-885a-4979-96bc-2ea120784426",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "261de034-408c-43e9-b55a-61ad66e55869",
+   "metadata": {},
+   "source": [
+    "## 3.4. Data and PyGrid\n",
+    "\n",
+    "As mentioned earlier in the course, PyGrid is the library we've made that lets you deploy nodes and conduct remote data science. Currently, we primarily support data in the form of NumPy arrays; however, we plan to expand this further. Additionally, many common data science formats are easily convertible to NumPy arrays, such as Pandas DataFrames, Tensorflow or PyTorch tensors, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ddeb8d90-2dac-4637-a938-485d1548f94e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raw_data is of type: <class 'pandas.core.frame.DataFrame'>\n",
+      "raw_data is of type: <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert a Pandas Dataframe to NumPy array\n",
+    "print(f'raw_data is of type: {type(raw_data)}')\n",
+    "raw_data = raw_data.values\n",
+    "print(f'raw_data is of type: {type(raw_data)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a69125e4-579e-4b1c-a2bf-d72040d58d45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test_data is of type: <class 'torch.Tensor'>\n",
+      "test_data is of type: <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert a PyTorch Tensor to a NumPy array\n",
+    "import torch\n",
+    "test_data = torch.Tensor([1, 2, 3, 4, 5])\n",
+    "print(f'test_data is of type: {type(test_data)}')\n",
+    "test_data = test_data.numpy()\n",
+    "print(f'test_data is of type: {type(test_data)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b06fe90-62bf-46bc-b99b-96710b497d5d",
+   "metadata": {},
+   "source": [
+    "Additionally, to make use of Differential Privacy, the current version of PySyft (0.6.0) supports NumPy arrays having the np.int32 datatype. You can initialize a NumPy array to have this datatype as shown below: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "90c1d0f3-e33b-4985-a9ac-0d0626a18792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "some_data = np.ndarray([1, 0, 1, 0, 1, 0], dtype=np.int32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab723c0a-d880-41ee-827c-8899b526d39d",
+   "metadata": {},
+   "source": [
+    "You can also convert other NumPy arrays to the np.int32 datatype using the astype() method, as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "805c2b1a-8a1f-47c3-8de2-4fafab86a2ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "random_data is of dtype: float64\n",
+      "random_data is now of type: int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "random_data = np.random.random((5, 5)) * 10\n",
+    "print(f'random_data is of dtype: {random_data.dtype}')\n",
+    "random_data = random_data.astype(np.int32)\n",
+    "print(f'random_data is now of type: {random_data.dtype}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96026f42-62d8-4a55-8a7f-2a8ed830371c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6568aaca-1d32-4ef7-8550-a16e4d48fcd0",
+   "metadata": {},
+   "source": [
+    "## 3.5. Differential Privacy & Datasets\n",
+    "\n",
+    "### 3.5.1. A Primer on Differential Privacy\n",
+    "\n",
+    "You were introduced to the idea(s) behind Differential Privacy (DP) in Lesson 1. Feel free to go back to the video if you need a refresher!\n",
+    "\n",
+    "### 3.5.2. DP Metadata needed for PyGrid\n",
+    "\n",
+    "We've made it pretty simple to use PyGrid for Data Owners who want their data to be protected with DP; you only need to add 3 simple pieces of metadata:\n",
+    "* min_val\n",
+    "* max_val\n",
+    "* entitities\n",
+    "\n",
+    "min_val and max_val correspond to the lowest and highest values found in the dataset. Ideally, they'd be data independent; so for instance, if you had regular .png images, the min_val could be 0 and the max_val could be 255, since a pixel value in that format can't go higher than that. If your dataset consisted of ages of various people, the min_val might be 0 years old, and the max_val might be 120 years old (the highest age any human being has ever reached).\n",
+    "\n",
+    "The tighter these bounds are, the more accurate and less privacy budget will be lost when a data scientist performs a computation with the data.\n",
+    "\n",
+    "On the other hand, entities corresponds to the Data Subjects; i.e. the people whose data is in the dataset, whose privacy we're protecting using Differential Privacy.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "<br>\n",
+    "<br>\n",
+    "\n",
+    "### 3.5.3 Understanding Why\n",
+    "\n",
+    "\n",
+    "<b> Let's pause for a moment. </b> <br>\n",
+    "Why do we have to specify min and max values? Why exactly is this necessary?  Let's try to understand with two contrasting examples:\n",
+    "\n",
+    "<ul>\n",
+    "\n",
+    "<li>Let's say dataset A was a series of populations of various countries, such as [7,000,000, 10,000,000, 200,000, etc]. Because we're working with such large numbers (often in the millions and occassionally in the billions), it stands to reason that if we want to protect anonymity using differential privacy, we'd have to add a proportionate amount of noise. Adding a noise value of 5 to a result of 10,000,000 wouldn't really offer much in the way of privacy protection; we'd have to add noise values that's on the order of tens or maybe even hundreds of thousands to offer meaningful protection.</li>\n",
+    "\n",
+    "\n",
+    "<li>Now let's say dataset B was a series of probabilities. For instance, let's say it was an array where every index corresponded to the probability that a person had a given disease. Something like: [0.0, 0.1, 0.9, 0.3, 0.4], which means the person has a 0% probability of having disease 1, a 0.1 probability (or 10% chance) of having disease 2, and so forth.</li>\n",
+    "</ul>\n",
+    "\n",
+    "\n",
+    "If we're going to add noise to dataset A to obscure the results of querying it, we'd have to adjust the magnitude of noise being added proportionally. If we added a value of 10,000 to our probability values, the results would immediately become meaningless. In this case, we might see the magnitude of noise needed to be added to be on the order of 0.1 or 0.01. \n",
+    "\n",
+    "\n",
+    "\n",
+    "This is (one of) the reasons why annotating minimum and maximum values in differential privacy is so important — <b> it increases the accuracy of your computations, and still allows you to get meaningful results while still protecting privacy. </b>\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "<br>\n",
+    "<br>\n",
+    "\n",
+    "### 3.5.4 Annotation with PySyft\n",
+    "\n",
+    "Let's go back to our COVID dataset that we were preparing.\n",
+    "\n",
+    "Now, the actual process of annotating data with DP Metadata is pretty straight forward. We start by importing the PySyft library, and then by calling .private() to the end of your Syft Tensor, as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7c765d97-e091-4351-a0d4-420fb1121c32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import syft as sy\n",
+    "\n",
+    "# select all of Country 0's data\n",
+    "country0_data = raw_data[0, :]\n",
+    "\n",
+    "# Specify it to be a np.int32 dtype\n",
+    "country0_data = country0_data.astype(np.int32)\n",
+    "\n",
+    "# Create an Entity or Data Subject corresponding to country1\n",
+    "from syft.core.adp.entity import Entity\n",
+    "country0 = Entity(name=\"Country 0\")\n",
+    "\n",
+    "# Create a Syft tensor\n",
+    "data = sy.Tensor(country0_data)\n",
+    "\n",
+    "# Add DP metadata\n",
+    "data = data.private(min_val=0, max_val=150000, entities=country0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db66daad-d51b-43cf-bf31-b8687aa31cb9",
+   "metadata": {},
+   "source": [
+    "Now that we're familiar with how to do this, let's repeat this process for all the Countries in our dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0f741fe4-5adf-4406-8727-18af72477dbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = dict()\n",
+    "entities = []\n",
+    "for i in range(raw_data.shape[-1]):\n",
+    "    country_name = f\"Country {i}\"\n",
+    "    \n",
+    "    # Create a new Entity correspoinding to the country and add it to the list\n",
+    "    new_entity = Entity(name=country_name)\n",
+    "    entities.append(new_entity)\n",
+    "    \n",
+    "    # Add it to the Dataset Dictionary\n",
+    "    dataset[country_name] = sy.Tensor(raw_data[:, i].astype(np.int32)).private(min_val=0, max_val=150000, entities=new_entity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61e913e3-0831-42bf-8017-0d94de7c9ece",
+   "metadata": {},
+   "source": [
+    "### 3.5.3. Loading the data to PyGrid!\n",
+    "\n",
+    "Once the appropriate data, metadata, and labels are prepared, uploading the data to your domain node is as simple as running a simple command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c3d7642b-00f2-486a-80d8-9fee4a41bdae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: CHANGE YOUR USERNAME AND PASSWORD!!! \n",
+      "\n",
+      "Anyone can login as an admin to your node right now because your password is still the default PySyft username and password!!!\n",
+      "\n",
+      "Connecting to http://localhost:8081... done! \t Logging into adp... done!\n",
+      "Loading dataset... uploading... SUCCESS!                                                                                                                                      \n",
+      "\n",
+      "Run <your client variable>.datasets to see your new dataset loaded into your machine!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Login to Domain Node\n",
+    "domain_node = sy.login(email=\"info@openmined.org\", password=\"changethis\", port=8081)\n",
+    "\n",
+    "# Upload the dataset!\n",
+    "domain_node.load_dataset(\n",
+    "    assets=dataset, \n",
+    "    name=\"COVID19 Cases in 175 countries\", \n",
+    "    description=\"Weekly data for an entire year\", \n",
+    "    metadata=\"Any metadata you'd like to include goes here\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58e6b255-8490-412f-9a87-e6fefd6d56d8",
+   "metadata": {},
+   "source": [
+    "Notes:\n",
+    "* PyGrid will raise a warning if the data you've uploaded isn't compatible with the Differential Privacy framework we've made."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a397bf1c-c32e-4dbd-a53d-d5464b37c412",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "                #myInput {\n",
+       "                  background-position: 10px 12px; /* Position the search icon */\n",
+       "                  background-repeat: no-repeat; /* Do not repeat the icon image */\n",
+       "                  background-color: #bbb;\n",
+       "                  width: 98%; /* Full-width */\n",
+       "                  font-size: 14px; /* Increase font-size */\n",
+       "                  padding: 12px 20px 12px 40px; /* Add some padding */\n",
+       "                  border: 1px solid #ddd; /* Add a grey border */\n",
+       "                  margin-bottom: 12px; /* Add some space below the input */\n",
+       "                }\n",
+       "\n",
+       "                #myTable {\n",
+       "                  border-collapse: collapse; /* Collapse borders */\n",
+       "                  width: 100%; /* Full-width */\n",
+       "                  border: 1px solid #ddd; /* Add a grey border */\n",
+       "                  font-size: 14px; /* Increase font-size */\n",
+       "                }\n",
+       "\n",
+       "                #myTable th, #myTable td {\n",
+       "                  text-align: left; /* Left-align text */\n",
+       "                  padding: 10px; /* Add padding */\n",
+       "                }\n",
+       "\n",
+       "                #myTable tr {\n",
+       "                  /* Add a bottom border to all table rows */\n",
+       "                  border-bottom: 1px solid #ddd;\n",
+       "                }\n",
+       "\n",
+       "                #myTable tr.header, #myTable tr:hover {\n",
+       "                  /* Add a grey background color to the table header and on hover */\n",
+       "                  background-color: #777;\n",
+       "                }\n",
+       "                </style>\n",
+       "\n",
+       "                <table id=\"myTable\" style=\"width:1000px\">\n",
+       "                  <tr class=\"header\">\n",
+       "                    <th style=\"width:30px\">Idx</th>\n",
+       "                    <th style=\"width:20%;\">Name</th>\n",
+       "                    <th style=\"width:35%;\">Description</th>\n",
+       "                    <th style=\"width:20%;\">Assets</th>\n",
+       "                    <th style=\"width:300px;\">Id</th>\n",
+       "                  </tr>\n",
+       "                \n",
+       "\n",
+       "          <tr>\n",
+       "            <td>[0]</td>\n",
+       "            <td>COVID19 Cases in 175 countries</td>\n",
+       "            <td>Weekly data for an entire year</td>\n",
+       "            <td>[\"Country 0\"] -> Tensor<br /><br />[\"Country 1\"] -> Tensor<br /><br />[\"Country 2\"] -> Tensor<br /><br />...<br /><br /></td>\n",
+       "            <td>21223f33-aab6-4455-8198-19bc80c3e914</td>\n",
+       "          </tr>\n",
+       "        </table>\n",
+       "\n",
+       "        <script>\n",
+       "        function myFunction() {\n",
+       "          // Declare variables\n",
+       "          var input, filter, table, tr, td, i, txtValue;\n",
+       "          input = document.getElementById(\"myInput\");\n",
+       "          filter = input.value.toUpperCase();\n",
+       "          table = document.getElementById(\"myTable\");\n",
+       "          tr = table.getElementsByTagName(\"tr\");\n",
+       "\n",
+       "          // Loop through all table rows, and hide those who don't match the search query\n",
+       "          for (i = 0; i < tr.length; i++) {\n",
+       "            name_td = tr[i].getElementsByTagName(\"td\")[1];\n",
+       "            desc_td = tr[i].getElementsByTagName(\"td\")[2];\n",
+       "            asset_td = tr[i].getElementsByTagName(\"td\")[3];\n",
+       "            id_td = tr[i].getElementsByTagName(\"td\")[4];\n",
+       "            if (name_td || desc_td || asset_td || id_td) {\n",
+       "              name_txtValue = name_td.textContent || name_td.innerText;\n",
+       "              desc_txtValue = desc_td.textContent || name_td.innerText;\n",
+       "              asset_txtValue = asset_td.textContent || name_td.innerText;\n",
+       "              id_txtValue = id_td.textContent || name_td.innerText;\n",
+       "              name_bool = name_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              desc_bool = desc_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              asset_bool = asset_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              id_bool = id_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              if (name_bool || desc_bool || asset_bool || id_bool) {\n",
+       "                tr[i].style.display = \"\";\n",
+       "              } else {\n",
+       "                tr[i].style.display = \"none\";\n",
+       "              }\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "        </script>"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.dataset_api.DatasetRequestAPI at 0x7fae0a2c8610>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "domain_node.datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e0a56393-dd5c-4c84-8258-cbbe3d04a648",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset: COVID19 Cases in 175 countries\n",
+      "Description: Weekly data for an entire year\n",
+      "\n",
+      "WARNING: Too many assets to print... truncating... You may run \n",
+      "\n",
+      " assets = my_dataset.assets \n",
+      "\n",
+      "to view receive a dictionary you can parse through using Python\n",
+      "(as opposed to blowing up your notebook with a massive printed table).\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "        #myInput {\n",
+       "          background-position: 10px 12px; /* Position the search icon */\n",
+       "          background-repeat: no-repeat; /* Do not repeat the icon image */\n",
+       "          background-color: #bbb;\n",
+       "          width: 98%; /* Full-width */\n",
+       "          font-size: 14px; /* Increase font-size */\n",
+       "          padding: 12px 20px 12px 40px; /* Add some padding */\n",
+       "          border: 1px solid #ddd; /* Add a grey border */\n",
+       "          margin-bottom: 12px; /* Add some space below the input */\n",
+       "        }\n",
+       "\n",
+       "        #myTable {\n",
+       "          border-collapse: collapse; /* Collapse borders */\n",
+       "          width: 50%; /* Full-width */\n",
+       "          border: 1px solid #ddd; /* Add a grey border */\n",
+       "          font-size: 14px; /* Increase font-size */\n",
+       "        }\n",
+       "\n",
+       "        #myTable th, #myTable td {\n",
+       "          text-align: left; /* Left-align text */\n",
+       "          padding: 10px; /* Add padding */\n",
+       "        }\n",
+       "\n",
+       "        #myTable tr {\n",
+       "          /* Add a bottom border to all table rows */\n",
+       "          border-bottom: 1px solid #ddd;\n",
+       "        }\n",
+       "\n",
+       "        #myTable tr.header, #myTable tr:hover {\n",
+       "          /* Add a grey background color to the table header and on hover */\n",
+       "          background-color: #777;\n",
+       "        }\n",
+       "        </style>\n",
+       "\n",
+       "        <table id=\"myTable\">\n",
+       "          <tr class=\"header\">\n",
+       "            <th style=\"width:15%;\">Asset Key</th>\n",
+       "            <th style=\"width:20%;\">Type</th>\n",
+       "            <th style=\"width:10%;\">Shape</th>\n",
+       "          </tr>\n",
+       "        \n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 0\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 1\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 2\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 3\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 4\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 5\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 6\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 7\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 8\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 9\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 10\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 11\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 12\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 13\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "\n",
+       "              <tr>\n",
+       "            <td>[\"Country 14\"]</td>\n",
+       "            <td>Tensor</td>\n",
+       "            <td>(53,)</td>\n",
+       "          </tr>\n",
+       "        </table>\n",
+       "\n",
+       "        "
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.dataset_api.Dataset at 0x7fae0a88e280>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "domain_node.datasets[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "075926db-d98b-47a7-918d-e5e63e4c17d5",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b0cc7fd-e3a4-4939-97b7-54576f80bc75",
+   "metadata": {},
+   "source": [
+    "## 3.6. Linking Data From Multiple Sources\n",
+    "\n",
+    "In this course, we'll be using the PySyft and PyGrid frameworks to link data from multiple sources (called nodes). This is both really cool and very useful because it lets us perform data science and machine learning on private data on someone else's machine or server, without compromising the privacy of anyone in the dataset.\n",
+    "\n",
+    "\n",
+    "In remote data science, because there's a high likelihood that all of our data is not coming from the same source, proper data annotation and cleaning becomes particularly important. This is an important distinction, because:\n",
+    "* Different sources may generate data at different rates; some sources stream data whereas others produce data in batches (i.e. in a periodic manner, or at a certain time interval)\n",
+    "* Different sources may also have different measuring capabilities, and this might affect the reliability of a dataset. \n",
+    "* Different nodes may have different privacy budgets alloted to their respective datasets, which means some datasets may be seen and used much less than others.\n",
+    "\n",
+    "<b> DID YOU KNOW? </b> A historical example of data sources having different measuring capabilities were the Geiger counters used in Chernobyl. <p>Immediately after the Chernobyl nuclear accident, many people at the time weren't too concerned because measurements from their Geiger counters showed a measurement of 3.6 Roentgen/hour — the equivalent measurement of 10 X-rays. However, it was later discovered that the Geiger counters in use had a maximum detection limit and sensitivity which meant they couldn't detect or measure radiation amounts higher than 3.6 Roentgen per hour. \n",
+    "    \n",
+    "When new, higher range Geiger counters were used, it was quickly (and shockingly) realized that the radiation being leaked wasn't 3.6 R/h, but around 5.6 R per SECOND. This was the equivalent of 2 Hiroshima nuclear bombs worth of radiation being released per hour.</p>\n",
+    "\n",
+    "Although this is an extreme example, it shows the importance of proper data acquisition and annotating data. Had the authorities known the differences of their data sources at the time, they might have intervened and helped out sooner, and could have saved countless more lives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c989f9aa-9d92-4acd-b408-cec78d3353bb",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9712504-7093-4645-b20b-b94185d4e1e0",
+   "metadata": {},
+   "source": [
+    "## Additional Resources and References\n",
+    "\n",
+    "<ul><li> Lyko K., Nitzschke M., Ngonga Ngomo AC. (2016) Big Data Acquisition. In: Cavanillas J., Curry E., Wahlster W. (eds) New Horizons for a Data-Driven Economy. Springer, Cham. <a> https://doi.org/10.1007/978-3-319-21569-3_4 </a> </li>\n",
+    "    <li> <a href=\"https://www.oreilly.com/library/view/implementing-a-smart/9781491983492/\"> Implementing a Smart Data Platform, O'Reilly Media 2017 </a> </li>\n",
+    "    <li> <a href=\"https://www.oreilly.com/library/view/python-data-cleaning/9781800565661/\"> Python Data Cleaning, O'Reilly Media 2020 </a></li>\n",
+    "    <li> <a href=\"https://ai.googleblog.com/2020/02/open-images-v6-now-featuring-localized.html\"> Examples of Image Annotation Types </a></li>\n",
+    "    <li> <a href=\"https://developers.google.com/machine-learning/glossary#label\"> Good description of annotations / labels </a></li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4500071-50fb-494c-b2d0-8e9ba6a18877",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4693b37c-6e72-46d3-9f84-e0e1c565a20c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/L4_Deployment_Tricks.ipynb b/L4_Deployment_Tricks.ipynb
new file mode 100644
index 0000000..0edb04d
--- /dev/null
+++ b/L4_Deployment_Tricks.ipynb
@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9f307d83-f207-40a4-9d66-6ee8fd6b6c00",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Lesson 4: Node Deployment Tips & Tricks!\n",
+    "\n",
+    "<b><u>Instructors</b></u>: Ishan Mishra, Phil Culliton\n",
+    "<br>\n",
+    "<b><u>Special Thanks to</b></u>: Madhava Jay\n",
+    "\n",
+    "Our stack is constantly evolving and getting better and more user-friendly. For the current release version of PyGrid and HAGrid, here are a few tips and tricks I've found useful on a Linux/OS X/WSL environment when it comes to working with this infrastructure.\n",
+    "\n",
+    "If you come up with any useful tricks of your own, please do share them with us and the community!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "715eee4b-fa7f-4484-ab71-b092835dcb11",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd624d42-0392-447a-9de4-2f3822af2446",
+   "metadata": {},
+   "source": [
+    "## Aliases\n",
+    "Here are some bash aliases I use for varous purposes, such as in order to ease the process of launching my local domain node."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de4e0e91-01dc-48a9-96b4-b2c4d1c6c028",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# Activates the venv, and navigates to the Syft directory\n",
+    "alias syft=\"conda activate <env_name> && cd <path/to/Pysyft>\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f59b557-5853-4fe8-a951-7c90bde50379",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# Launches the local domain node with one word\n",
+    "alias launch=\"hagrid launch <node_name> domain to docker:8081+ --tail=True\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2f6bdc8-f1cb-4b75-a97d-d3277c40d77a",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# Shuts down all local domain nodes\n",
+    "alias land=\"hagrid land all\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d782924b-3deb-4002-8a02-1cc75ef0336c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50fc4c9a-eb7c-490c-98f1-edd19b91f8db",
+   "metadata": {},
+   "source": [
+    "# Clearing Up Space\n",
+    "Docker has a reputation for taking up a suprisingly large amount of space as its containers are running. Here are a few ways I've found to mitigate those effects. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ff124a8-75c0-4a0e-be47-091f4d19883e",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# This cleans up after docker\n",
+    "# WARNING: will delete all currently unused or inactive containers, networks, volumes, etc - this will result in losing any data you uploaded to  #Domain Nodes that aren't currently running\n",
+    "\n",
+    "hard_reset() {\n",
+    "        echo \"Pruning Network\"\n",
+    "        docker network prune -f\n",
+    "        echo \"Pruning Volume\"\n",
+    "        docker volume prune -f\n",
+    "        echo \"Pruning System\"\n",
+    "        docker system prune -f\n",
+    "        echo \"Pruning Containers\"\n",
+    "        docker container prune -f\n",
+    "}\n",
+    "\n",
+    "# This will delete active or unused containers, networks, but won't delete any data you've uploaded or stored.\n",
+    "\n",
+    "cleanup() {\n",
+    "        echo \"Pruning Network\"\n",
+    "        docker network prune -f\n",
+    "        echo \"Pruning System\"\n",
+    "        docker system prune -f\n",
+    "        echo \"Pruning Containers\"\n",
+    "        docker container prune -f\n",
+    "}\n",
+    "\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c530773e-0973-455d-8884-b6a83147ed6c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dba2e61a-dc86-4dba-9e44-fb2555c11227",
+   "metadata": {},
+   "source": [
+    "# Testing and debugging\n",
+    "Here are some commands that will make it easier to work with Docker containers, while testing, debugging, or for other purposes. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb5c3696-ce57-4874-abb2-2a0f3dcb7add",
+   "metadata": {},
+   "source": [
+    "#### <b> ctop </b>\n",
+    "ctop is a handy tool that lets you quickly monitor and manage all your containers (both active and inactive):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7ad64d8-83da-496c-86e6-0bcb32006762",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# To install on Ubuntu, simply\n",
+    "sudo apt install ctop\n",
+    "\n",
+    "# To install on MacOS, simply\n",
+    "brew install ctop\n",
+    "\n",
+    "# An alternative version of this package can be obtained from:\n",
+    "https://github.com/bcicen/ctop\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d86de22f-1b91-41b7-8f05-2070c7b815bc",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "# ctop is a quick way to monitor containers\n",
+    "ctop\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3cde045",
+   "metadata": {},
+   "source": [
+    "<img src=\"ui_images/ctop.png\" width=\"633\" align=\"left\" />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "526c5add-40c9-465f-a2ba-990112a082ac",
+   "metadata": {},
+   "source": [
+    "By hovering over a container and pressing enter, you gain the ability to:\n",
+    "\n",
+    "- <b> View: </b>\n",
+    "    - <b> Single View </b> (keybind 'o'): view details of that specific container, including ports, health, CPU/MEM usage, NET/IO, environment variables, and much more.\n",
+    "    - <b> Log View </b> (keybind 'l'): View the logs of just this container. This is in contrast to launching a local domain node with --tail=True, as that method continuousy outputs all the container logs in real time.\n",
+    "- <b> Interact: </b>\n",
+    "    - <b> Stop </b> (keybind 's'): Stop the container.\n",
+    "    - <b> Pause </b> (keybind 'p'): Pause the container.\n",
+    "    - <b> Restart </b> (keybind 'r'): Restart the container.\n",
+    "    - <b> Exec shell </b> (keybind 'e'): This lets you run commands inside this shell. This is very handy if you want to run test files inside a container, for instance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed81829b-fe69-4dd8-8848-f4da1a060e91",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745b115e-48ee-45e0-ac93-7bbbb7bba742",
+   "metadata": {},
+   "source": [
+    "## Miscellaneous Hagrid Tricks\n",
+    "\n",
+    "<b> Seeing the logs from Hagrid in real time: </b>\n",
+    "``` \n",
+    "# Add the \"--tail=True\" parameter!\n",
+    "hagrid launch local_node --tail=True\n",
+    "```\n",
+    "\n",
+    "<b> Seeing a list of commands being run in Hagrid: </b>\n",
+    "``` \n",
+    "# Use the --cmd=True parameter!\n",
+    "hagrid launch local_node --cmd=True\n",
+    "```\n",
+    "\n",
+    "<b> Shutting down your docker containers without losing data: </b>\n",
+    "```\n",
+    "# This is great if the \"hagrid land\" command isn't working\n",
+    "docker rm `docker ps -aq` --force\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0613e05-9a03-429b-8188-0508d4c7aa54",
+   "metadata": {},
+   "source": [
+    "<b> Resetting Domain Node Storage/Memory </b>\n",
+    "\n",
+    "<t> By deleting the db container, you can do a hard reset on the domain node's storage and memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30b313f7-82e0-4cf5-8500-d43fdd10b8f0",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb4cf1af-b20a-4a3b-9a36-16e7f33edf7f",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/L4_NodeMaintenance.ipynb b/L4_NodeMaintenance.ipynb
new file mode 100644
index 0000000..b41378b
--- /dev/null
+++ b/L4_NodeMaintenance.ipynb
@@ -0,0 +1,1106 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2658e30a-4ce1-4e3c-a631-a5bcf7b096aa",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Lesson 4: Node Management\n",
+    "\n",
+    "<b><u>Instructors</b></u>: Ishan Mishra, Phil Culliton\n",
+    "\n",
+    "\n",
+    "In this lesson, we'll pull back the curtain a bit and show you how to manage, maintain and customize your node. We'll be walking you through adding new users, changing priviledges, creating and responding to requests, and everything to do with network nodes!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aeb1825-a452-48fa-b8b0-687dd7eef4f1",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b655bad5-1774-4c2d-936e-a35a9719e884",
+   "metadata": {},
+   "source": [
+    "## Concept 1. Logging in! \n",
+    "\n",
+    "\n",
+    "### 1.1 Via PySyft\n",
+    "There are two ways to log into your own node, as the Data Owner. The first way is using the PySyft library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac1b13ac-b456-449c-825b-7d5629d62f0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import syft as sy\n",
+    "import numpy as np\n",
+    "from syft.core.adp.entity import Entity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9e7745e9-0cc0-417a-aa51-456ceba2e67a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: CHANGE YOUR USERNAME AND PASSWORD!!! \n",
+      "\n",
+      "Anyone can login as an admin to your node right now because your password is still the default PySyft username and password!!!\n",
+      "\n",
+      "Connecting to http://localhost:8081... done! \t Logging into adp... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "domain_node = sy.login(email=\"info@openmined.org\", password=\"changethis\", port=8081)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63431254-02bb-4fd1-a4df-e848d6042b58",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "### 1.2 Via the UI\n",
+    "\n",
+    "The second way involves using the same username and password, but logging in through the UI, which is accessible at <a href=\"http://localhost:8081\">localhost:8081</a>\n",
+    "\n",
+    "Please note, if you launched Hagrid to a different port, please replace 8081 with the appropriate portnumber!\n",
+    "\n",
+    "![alt text](ui_images/login_screen.png \"UI Login Screen\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d4eaa2a-72aa-4f5b-aa45-65ea0b8d6df4",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cef3b59-837c-4909-82ba-a82e7af312a2",
+   "metadata": {},
+   "source": [
+    "## Concept 2. Users!\n",
+    "\n",
+    "You might not be the only person using your domain node! Let's see how you can go about checking all the users on your domain node, as well as how to add and configure new ones!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "698d72df-b9dd-448d-ad60-fa692dc41a35",
+   "metadata": {},
+   "source": [
+    "### 2.1 Seeing all users\n",
+    "To see all the current users on a domain node, you can simply do the following once you've signed in using the PySyft library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "950009d8-e448-46ea-8de9-67fe9352fd4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>added_by</th>\n",
+       "      <th>allocated_budget</th>\n",
+       "      <th>budget</th>\n",
+       "      <th>budget_spent</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>daa_pdf</th>\n",
+       "      <th>email</th>\n",
+       "      <th>id</th>\n",
+       "      <th>institution</th>\n",
+       "      <th>name</th>\n",
+       "      <th>role</th>\n",
+       "      <th>verify_key</th>\n",
+       "      <th>website</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2021-11-03 02:48:47.495871</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>info@openmined.org</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>Owner</td>\n",
+       "      <td>dd784cd6c0278a9cd11b69cdf4c4ca0076e661e3dd2e74...</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.user_api.UserRequestAPI at 0x7fbb0e6ee040>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# See all the users\n",
+    "domain_node.users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fd5c9d6-3d25-42c8-ad43-faee2d310757",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "\n",
+    "Alternatively, if you logged in using the UI, you are able to see the Users by clicking the \"Users\" field.\n",
+    "\n",
+    "![alt text](ui_images/users.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d162ad28-789d-43c8-b16a-6f77414216fa",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bc5e346-31a0-4798-9ac4-7dabc935f8cc",
+   "metadata": {},
+   "source": [
+    "### 2.2 Creating a New User!\n",
+    "\n",
+    "Creating a new user is again a really simple process. We just need to specify the following parameters when we're creating the account. Note that many of these can be changed and updated retroactively!\n",
+    "\n",
+    "Let's now try creating a user via PySyft, and validating that it worked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "19d2b482-dcf3-4237-b426-76122c004495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new user\n",
+    "domain_node.users.create(\n",
+    "    **{\n",
+    "        \"name\": \"Sheldon Cooper\",\n",
+    "        \"email\": \"sheldon@caltech.edu\",\n",
+    "        \"password\": \"bazinga\",\n",
+    "        \"budget\": 100\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "505904f9-f962-4e97-879a-7539bfe37eeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>added_by</th>\n",
+       "      <th>allocated_budget</th>\n",
+       "      <th>budget</th>\n",
+       "      <th>budget_spent</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>daa_pdf</th>\n",
+       "      <th>email</th>\n",
+       "      <th>id</th>\n",
+       "      <th>institution</th>\n",
+       "      <th>name</th>\n",
+       "      <th>role</th>\n",
+       "      <th>verify_key</th>\n",
+       "      <th>website</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2021-11-03 02:48:47.495871</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>info@openmined.org</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>Owner</td>\n",
+       "      <td>dd784cd6c0278a9cd11b69cdf4c4ca0076e661e3dd2e74...</td>\n",
+       "      <td>&lt;syft.lib.python._SyNone object at 0x7fbb154c3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jane Doe</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2021-11-03 06:24:10.657167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sheldon@caltech.edu</td>\n",
+       "      <td>2</td>\n",
+       "      <td></td>\n",
+       "      <td>Sheldon Cooper</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>2fef564b79c4fef21d1549a891d624d84e71c482af0c07...</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.user_api.UserRequestAPI at 0x7fbb0e6ee040>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "domain_node.users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccda843e-7fc8-4708-9d30-5caf6a5960f4",
+   "metadata": {},
+   "source": [
+    "By default, this new user is a Data Scientist. However, other roles are also possible. You can see all the roles that are available by accessing <a href=\"http://localhost:8081/permissions\"> the UI</a> and clicking on \"Permissions\" in the Menu bar on the left side of the screen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc7630b-ba2d-4f7c-a09a-5fce6dcd95a0",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03f7fd2c-0d90-40bd-a2f5-e6d3d33a505b",
+   "metadata": {},
+   "source": [
+    "### 2.3 User Permissions\n",
+    "\n",
+    "It's understandable that you might want to control how much a given user can change and tinker with your domain node. To allow for this, we've added customizable user permissions!\n",
+    "\n",
+    "As mentioned previously, these are accessible by accessing the <a href=\"http://localhost:8081/permissions\"> the UI</a> and clicking on \"Permissions\" in the Menu bar on the left side of the screen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a55018a-432c-413c-bb65-737affb6d181",
+   "metadata": {},
+   "source": [
+    "![alt text](ui_images/permissions_unexpanded.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f1895c-824f-44eb-9ceb-55eea4fe0f11",
+   "metadata": {},
+   "source": [
+    "Each of the 4 roles can be configured as you see fit. The parameters you can adjust are shown below:\n",
+    "\n",
+    "![alt text](ui_images/permissions_all.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "179c9cec-8ed2-4695-baf8-c627773d1d6c",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea1b2b82-1787-4c68-a8a5-1964c2bcf10b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Concept 3. Requests\n",
+    "\n",
+    "Requests are instances where a User wants to do something that they don't have authorization to do.\n",
+    "\n",
+    "\n",
+    "As of the current release, there are 3 major types of requests:\n",
+    "- <b> Data Access</b> requests: Occurs when someone wanting to use the data on the node.\n",
+    "- <b>Privacy budget upgrade</b> requests: Occurs when a DS needs an upgrade of the privacy budget allocated to them.\n",
+    "- <b>New Data Scientist Account</b> requests: Occurs when a Data Scientist wishes to create an account on the domain node.\n",
+    "\n",
+    "With each type of request, they can either be accepted, rejected, approved or granted. We'll show you how to do each, but first, let's show you how a user would put in a request."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1b3f494-d5e0-45b0-9a61-bd94ebb08167",
+   "metadata": {},
+   "source": [
+    "### 3.1 Putting in a Request\n",
+    "\n",
+    "Remember that data scientist account we created awhile back? We'll be making use of him now. Let's first use him to sign into our domain node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e39b28cd-0ab8-4148-b598-1fb6f0fcd654",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connecting to http://localhost:8081... done! \t Logging into adp... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_scientist_node = sy.login(email=\"sheldon@caltech.edu\", password=\"bazinga\", port=8081)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0673d45-427e-4d17-bfdf-5649eebc326f",
+   "metadata": {},
+   "source": [
+    "Now that he's logged in, let's say he checks the privacy budget available to him:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fc3e5be4-fd01-42e4-8800-48d9dd22808d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scientist_node.privacy_budget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef10606d-577f-484d-af24-4531c46e7997",
+   "metadata": {},
+   "source": [
+    "Now let's say our data scientist isn't happy with this much privacy budget, and that he wants more. He could put in a request for more privacy budget, as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5a6a2f31-da50-4d28-93be-3d9103cc6653",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requested 1000 epsilon of budget. Call .privacy_budget to see if your budget has arrived!\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_scientist_node.request_budget(eps=1000, reason=\"I want to do data exploration\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68ec4bdc-5f10-4665-8141-27d5c87b5df5",
+   "metadata": {},
+   "source": [
+    "### 3.2 Responding to a Request"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "190a0c54-c4d4-4695-9190-6f598dd695df",
+   "metadata": {},
+   "source": [
+    "The request that the Data Scientist put in would be logged in the domain node, and could be viewed by the Data Owner at any time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3636a5fa-5f9c-483c-a800-ecff0e18cf2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Requested Object's tags</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Request ID</th>\n",
+       "      <th>Requested Object's ID</th>\n",
+       "      <th>Requested Object's type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]</td>\n",
+       "      <td>I want to do data exploration</td>\n",
+       "      <td>&lt;UID: 3ece116558bf41cfb4680847173b6c41&gt;</td>\n",
+       "      <td>&lt;UID: 6e2fecb392834b6589f616b7c19cd72f&gt;</td>\n",
+       "      <td>&lt;Budget&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "[<RequestMessage: 3ece116558bf41cfb4680847173b6c41>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# See all the requests made to the domain node\n",
+    "domain_node.requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf3d6c9c-493e-49a7-bcaf-f8aa519580ba",
+   "metadata": {},
+   "source": [
+    "Now, the Data Owner could investigate this request:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3bd573af-ffbd-4a2c-8333-315e0169a32f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I want to do data exploration'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "domain_node.requests[0].request_description"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c83a73-afdb-47cb-ace7-da82b4a91fb8",
+   "metadata": {},
+   "source": [
+    "Let's say he decides to turn it down due to how much privacy budget is requested. He would run this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b0aef9d8-6a76-4c09-b472-150848fd5f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain_node.requests[0].deny()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c082af80-b4b0-448f-a8f4-fc75d04cb371",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain_node.requests[0].reject()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e2a6f80-f97b-4c8b-9f3d-9fe0b6d84979",
+   "metadata": {},
+   "source": [
+    "If instead he wanted to accept the request, he could instead run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e5afb00e-2066-41c0-88ad-cb0941cfeb9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain_node.requests[0].accept()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5432f70-f755-48c5-8699-d0f7dddd42bc",
+   "metadata": {},
+   "source": [
+    "It's important to note that after each grant or refusal, the request disappears from the object store and is no longer visible.\n",
+    "\n",
+    "![alt text](ui_images/no_requests.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bcda20d-6003-42b8-80dc-4b764f3ea2c3",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "152d1127-f5fc-4087-9268-bd1c01a9dac4",
+   "metadata": {},
+   "source": [
+    "## Concept 4. Network Nodes!\n",
+    "\n",
+    "A Network Node is a level of abstraction above a domain node. It's a server which exists outside of any data owner's institution, providing services to a network of data owners and data scientists, such as dataset searching and bulk project approval (the ability to participate in projects across groups of domains and data scientsts at a time).\n",
+    "\n",
+    "We can view the available networks, and put in a request to join them via the UI:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c6424c2-2809-4786-8eee-5280eb33cc63",
+   "metadata": {},
+   "source": [
+    "![alt text](ui_images/networks.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24b12e20-14f2-4b83-a256-422a24253fdf",
+   "metadata": {},
+   "source": [
+    "We can also do this via PySyft. To see the list of available networks we could join, we simply need to do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ae83711a-b72f-439b-8527-dc4a55560af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>host_or_ip</th>\n",
+       "      <th>vpn_host_or_ip</th>\n",
+       "      <th>protocol</th>\n",
+       "      <th>port</th>\n",
+       "      <th>admin_email</th>\n",
+       "      <th>website</th>\n",
+       "      <th>slack</th>\n",
+       "      <th>slack_channel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>omnet</td>\n",
+       "      <td>13.64.187.229</td>\n",
+       "      <td>100.64.0.1</td>\n",
+       "      <td>http</td>\n",
+       "      <td>80</td>\n",
+       "      <td>support@openmined.org</td>\n",
+       "      <td>https://www.openmined.org/</td>\n",
+       "      <td>https://slack.openmined.org/</td>\n",
+       "      <td>omnet</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>United Nations PET Lab (Mock)</td>\n",
+       "      <td>13.64.14.221</td>\n",
+       "      <td>100.64.0.1</td>\n",
+       "      <td>http</td>\n",
+       "      <td>80</td>\n",
+       "      <td>support@openmined.org</td>\n",
+       "      <td>https://www.openmined.org/</td>\n",
+       "      <td>https://slack.openmined.org/</td>\n",
+       "      <td>petlab</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<syft.registry.NetworkRegistry at 0x7fbb07e938b0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sy.networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c0ff222-26ab-466e-b6bd-f3dab4dad402",
+   "metadata": {},
+   "source": [
+    "To join one, we would call:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8c19b159-c6df-47ab-8e44-84034ffa98f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connecting to http://13.64.14.221... done! \t Logging into united_nations_mock... as GUEST...done!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<NetworkClient: united_nations_mock>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "un_network = sy.networks[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deaec0c0-9404-4365-8213-92c6e5a6e964",
+   "metadata": {},
+   "source": [
+    "A few things to note here- \n",
+    "- We joined the Network as a Guest. This has limited capabilities.\n",
+    "- Instead of indexing by its position in the networks, we could also index by name\n",
+    "\n",
+    "- <b> Upon selecting a network, we have access to all the public properties of the network!</b>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34d67d98-1ea2-423c-9c25-2f7cc730dee5",
+   "metadata": {},
+   "source": [
+    "After joining the network node, we can see all the domains that are attached to that network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "93b276ae-8e1e-4c3c-b29e-41858ad53dbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t\t     opengrid network"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>host_or_ip</th>\n",
+       "      <th>id</th>\n",
+       "      <th>is_vpn</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100.64.0.3</td>\n",
+       "      <td>84a28770a17d4b6199d78d66b3bdc8c3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>relaxed_song</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>100.64.0.1</td>\n",
+       "      <td>a2f53454633f4896852aae45d7bc4096</td>\n",
+       "      <td>1</td>\n",
+       "      <td>strange_he</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>100.64.0.4</td>\n",
+       "      <td>201e5de97fc94b53a39b37a729efbb69</td>\n",
+       "      <td>1</td>\n",
+       "      <td>kind_kaliouby</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<NetworkClient: opengrid>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opengrid = sy.networks[0]\n",
+    "opengrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8243d0bc-3cff-4d5e-8b01-a23da83e04f2",
+   "metadata": {},
+   "source": [
+    "Let's try to join one of these domains. We start by indexing into it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afcf7957-9a9c-44a8-bf8c-4ec19f5c0eea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pclient = opengrid[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dab09d83-2f6e-4384-9dc2-69977bceaa88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(This is a logged out ProxyClient() object for a domain called 'kind_kaliouby'. Please call .login(email, password) to get a full client you can use for stuff.)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pclient"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2c64318-35b2-4086-bde2-5de21879c5a7",
+   "metadata": {},
+   "source": [
+    "We then we login as normal, as we would do to any domain!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "66f0da43-d60a-41be-859e-06d2c606593e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logged in to kind_kaliouby as info@openmined.org\n"
+     ]
+    }
+   ],
+   "source": [
+    "client = pclient.login(email=\"info@openmined.org\", password=\"changethis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7177ffd3-17da-4ac5-a617-5d5ec8199ede",
+   "metadata": {},
+   "source": [
+    "Now we can view datasets, and use this domain node like we would any other:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "388d28ae-c830-4ef7-93c7-f0e674a7dbac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "(no datasets found)"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.dataset_api.DatasetRequestAPI at 0x7fe8a22b0130>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client.datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fda5d207-1f49-4da6-851c-7179c5d849e9",
+   "metadata": {},
+   "source": [
+    "In the event that we wanted our domain node to join a network, we could proceed as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "316ced1e-9690-4720-8850-28c274c303e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: CHANGE YOUR USERNAME AND PASSWORD!!! \n",
+      "\n",
+      "Anyone can login as an admin to your node right now because your password is still the default PySyft username and password!!!\n",
+      "\n",
+      "Connecting to None... done! \t Logging into ecstatic_chollet... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Login to our local domain node\n",
+    "domain = sy.login(email=\"info@openmined.org\", password=\"changethis\", port=8081)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "73412218-3132-46da-bfec-4b7d704cbb7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔌 <DomainClient - kind_kaliouby: <UID: 201e5de97fc94b53a39b37a729efbb69>> successfully connected to the VPN: http://40.83.192.48:80/api/v1\n",
+      "Waiting to connect to VPN.\n",
+      "Connected to VPN\n",
+      "Application submitted.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Applying to join is as simple as a one liner.\n",
+    "domain.apply_to_network(opengrid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "021df7bb-46a7-47e3-ad4f-66b920b74fad",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "In this notebook, you created a new user, learned how to change the user permissions, file and respond to requests, and even join a network node!\n",
+    "In the next lesson, we'll be giving you a bigger taste of remote data science, and what you can do with it. Stay tuned!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "695b6f6e-0bcb-4939-9f6e-602f28fe8b2d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<hr>\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46d6b0fa-77c3-4adf-a963-7f18bdd697e5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/L5_RemoteDataScience.ipynb b/L5_RemoteDataScience.ipynb
new file mode 100644
index 0000000..25749aa
--- /dev/null
+++ b/L5_RemoteDataScience.ipynb
@@ -0,0 +1,718 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1a49837a",
+   "metadata": {},
+   "source": [
+    "# Lesson 5: Remote Data Science!\n",
+    "\n",
+    "<b><u>Instructors:</u></b> Ishan Mishra, Phil Culliton\n",
+    "\n",
+    "\n",
+    "In this lesson, we're going to see what it's like to be a data scientist working with remote data through PySyft. We'll be making use of the same tabular COVID19 dataset that we cleaned, annotated, and uploaded in Lesson 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e01d0573-872b-4aef-a44b-3060ee72fea9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import syft as sy\n",
+    "import numpy as np\n",
+    "import matplotlib, matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71e5107a-90ea-40f3-9239-d9f92ab165e5",
+   "metadata": {},
+   "source": [
+    "#### Log into domain\n",
+    "\n",
+    "Here we log into your new node using a non-admin data scientist account. If you used a different email / password\n",
+    "combination to create the data scientist account, enter it here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "234dc92d-0d4d-428d-8b4c-41c3e91e056b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connecting to http://localhost:8081... done! \t Logging into new_phil_node... done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds_domain = sy.login(\n",
+    "    email = \"sheldon@caltech.edu\",\n",
+    "    password = \"bazinga\",\n",
+    "    port = 8081\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70d17a6e-145a-4c0c-9c56-b4154ef166c9",
+   "metadata": {},
+   "source": [
+    "#### Check privacy budget\n",
+    "\n",
+    "The privacy budget represents how much noise the data scientist can remove from a dataset when accessing it. Domains will set a privacy budget per data scientist. More (in this case, 100) can be requested using:\n",
+    "\n",
+    "`ds_domain.request_budget(100)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ec021724-1a82-4e4b-abc4-ccea399cc5f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_domain.privacy_budget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "263aa7de-4f25-4a60-9f4e-dc2184c04d5a",
+   "metadata": {},
+   "source": [
+    "#### List datasets on domain\n",
+    "\n",
+    "There is only one dataset attached to this domain currently - let's take a look! The following command shows the data scientist:\n",
+    "1. The name and index of the available datasets.\n",
+    "2. A description, as entered by the data owner.\n",
+    "3. Any assets contained within the dataset - usually tensors or groups of tensors - and their names. In this case we just have string indices.\n",
+    "4. A unique ID - this can be used to access the dataset in addition to the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d7a97e0a-f7e9-418d-bd6c-8623bfa6f01b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "                #myInput {\n",
+       "                  background-position: 10px 12px; /* Position the search icon */\n",
+       "                  background-repeat: no-repeat; /* Do not repeat the icon image */\n",
+       "                  background-color: #bbb;\n",
+       "                  width: 98%; /* Full-width */\n",
+       "                  font-size: 14px; /* Increase font-size */\n",
+       "                  padding: 12px 20px 12px 40px; /* Add some padding */\n",
+       "                  border: 1px solid #ddd; /* Add a grey border */\n",
+       "                  margin-bottom: 12px; /* Add some space below the input */\n",
+       "                }\n",
+       "\n",
+       "                #myTable {\n",
+       "                  border-collapse: collapse; /* Collapse borders */\n",
+       "                  width: 100%; /* Full-width */\n",
+       "                  border: 1px solid #ddd; /* Add a grey border */\n",
+       "                  font-size: 14px; /* Increase font-size */\n",
+       "                }\n",
+       "\n",
+       "                #myTable th, #myTable td {\n",
+       "                  text-align: left; /* Left-align text */\n",
+       "                  padding: 10px; /* Add padding */\n",
+       "                }\n",
+       "\n",
+       "                #myTable tr {\n",
+       "                  /* Add a bottom border to all table rows */\n",
+       "                  border-bottom: 1px solid #ddd;\n",
+       "                }\n",
+       "\n",
+       "                #myTable tr.header, #myTable tr:hover {\n",
+       "                  /* Add a grey background color to the table header and on hover */\n",
+       "                  background-color: #777;\n",
+       "                }\n",
+       "                </style>\n",
+       "\n",
+       "                <input type=\"text\" id=\"myInput\" onkeyup=\"myFunction()\" placeholder=\"Search for datasets..\">\n",
+       "\n",
+       "                <table id=\"myTable\">\n",
+       "                  <tr class=\"header\">\n",
+       "                    <th style=\"width:30px\">Idx</th>\n",
+       "                    <th style=\"width:20%;\">Name</th>\n",
+       "                    <th style=\"width:35%;\">Description</th>\n",
+       "                    <th style=\"width:20%;\">Assets</th>\n",
+       "                    <th style=\"width:300px;\">Id</th>\n",
+       "                  </tr>\n",
+       "                \n",
+       "\n",
+       "          <tr>\n",
+       "            <td>[0]</td>\n",
+       "            <td>COVID Synthetic Weekly Cases</td>\n",
+       "            <td>Synthetically-generated COVID weekly case dataset</td>\n",
+       "            <td>[\"0\"] -> Tensor<br /><br />[\"1\"] -> Tensor<br /><br />[\"2\"] -> Tensor<br /><br />[\"3\"] -> Tensor<br /><br />[\"4\"] -> Tensor<br /><br />[\"5\"] -> Tensor<br /><br />[\"6\"] -> Tensor<br /><br />[\"7\"] -> Tensor<br /><br />[\"8\"] -> Tensor<br /><br />[\"9\"] -> Tensor<br /><br /></td>\n",
+       "            <td>cb441fdc-aefa-4a44-8f1e-fe61c4e0eb3a</td>\n",
+       "          </tr>\n",
+       "\n",
+       "          <tr>\n",
+       "            <td>[1]</td>\n",
+       "            <td>COVID Synthetic Weekly Cases v6</td>\n",
+       "            <td>Synthetically-generated COVID weekly case dataset</td>\n",
+       "            <td>[\"weeklyCases\"] -> Tensor<br /><br />[\"nextWeekCases\"] -> Tensor<br /><br /></td>\n",
+       "            <td>8d1e7ac7-a562-4f05-805f-9f6cac0a0d54</td>\n",
+       "          </tr>\n",
+       "\n",
+       "          <tr>\n",
+       "            <td>[2]</td>\n",
+       "            <td>COVID Synthetic Weekly Cases v7</td>\n",
+       "            <td>Synthetically-generated COVID weekly case dataset</td>\n",
+       "            <td>[\"weeklyCases\"] -> Tensor<br /><br /></td>\n",
+       "            <td>32ea0eaf-5c47-4b27-8d2b-7a899e227dcc</td>\n",
+       "          </tr>\n",
+       "\n",
+       "          <tr>\n",
+       "            <td>[3]</td>\n",
+       "            <td>COVID19 Cases in 175 countries</td>\n",
+       "            <td>Weekly data for an entire year</td>\n",
+       "            <td>[\"Country 0\"] -> Tensor<br /><br />[\"Country 1\"] -> Tensor<br /><br />[\"Country 2\"] -> Tensor<br /><br />[\"Country 3\"] -> Tensor<br /><br />[\"Country 4\"] -> Tensor<br /><br />[\"Country 5\"] -> Tensor<br /><br />[\"Country 6\"] -> Tensor<br /><br />[\"Country 7\"] -> Tensor<br /><br />[\"Country 8\"] -> Tensor<br /><br />[\"Country 9\"] -> Tensor<br /><br />[\"Country 10\"] -> Tensor<br /><br />[\"Country 11\"] -> Tensor<br /><br />[\"Country 12\"] -> Tensor<br /><br />[\"Country 13\"] -> Tensor<br /><br />[\"Country 14\"] -> Tensor<br /><br />[\"Country 15\"] -> Tensor<br /><br />[\"Country 16\"] -> Tensor<br /><br />[\"Country 17\"] -> Tensor<br /><br />[\"Country 18\"] -> Tensor<br /><br />[\"Country 19\"] -> Tensor<br /><br />[\"Country 20\"] -> Tensor<br /><br />[\"Country 21\"] -> Tensor<br /><br />[\"Country 22\"] -> Tensor<br /><br />[\"Country 23\"] -> Tensor<br /><br />[\"Country 24\"] -> Tensor<br /><br />[\"Country 25\"] -> Tensor<br /><br />[\"Country 26\"] -> Tensor<br /><br />[\"Country 27\"] -> Tensor<br /><br />[\"Country 28\"] -> Tensor<br /><br />[\"Country 29\"] -> Tensor<br /><br />[\"Country 30\"] -> Tensor<br /><br />[\"Country 31\"] -> Tensor<br /><br />[\"Country 32\"] -> Tensor<br /><br />[\"Country 33\"] -> Tensor<br /><br />[\"Country 34\"] -> Tensor<br /><br />[\"Country 35\"] -> Tensor<br /><br />[\"Country 36\"] -> Tensor<br /><br />[\"Country 37\"] -> Tensor<br /><br />[\"Country 38\"] -> Tensor<br /><br />[\"Country 39\"] -> Tensor<br /><br />[\"Country 40\"] -> Tensor<br /><br />[\"Country 41\"] -> Tensor<br /><br />[\"Country 42\"] -> Tensor<br /><br />[\"Country 43\"] -> Tensor<br /><br />[\"Country 44\"] -> Tensor<br /><br />[\"Country 45\"] -> Tensor<br /><br />[\"Country 46\"] -> Tensor<br /><br />[\"Country 47\"] -> Tensor<br /><br />[\"Country 48\"] -> Tensor<br /><br />[\"Country 49\"] -> Tensor<br /><br />[\"Country 50\"] -> Tensor<br /><br />[\"Country 51\"] -> Tensor<br /><br />[\"Country 52\"] -> Tensor<br /><br />[\"Country 53\"] -> Tensor<br /><br />[\"Country 54\"] -> Tensor<br /><br />[\"Country 55\"] -> Tensor<br /><br />[\"Country 56\"] -> Tensor<br /><br />[\"Country 57\"] -> Tensor<br /><br />[\"Country 58\"] -> Tensor<br /><br />[\"Country 59\"] -> Tensor<br /><br />[\"Country 60\"] -> Tensor<br /><br />[\"Country 61\"] -> Tensor<br /><br />[\"Country 62\"] -> Tensor<br /><br />[\"Country 63\"] -> Tensor<br /><br />[\"Country 64\"] -> Tensor<br /><br />[\"Country 65\"] -> Tensor<br /><br />[\"Country 66\"] -> Tensor<br /><br />[\"Country 67\"] -> Tensor<br /><br />[\"Country 68\"] -> Tensor<br /><br />[\"Country 69\"] -> Tensor<br /><br />[\"Country 70\"] -> Tensor<br /><br />[\"Country 71\"] -> Tensor<br /><br />[\"Country 72\"] -> Tensor<br /><br />[\"Country 73\"] -> Tensor<br /><br />[\"Country 74\"] -> Tensor<br /><br />[\"Country 75\"] -> Tensor<br /><br />[\"Country 76\"] -> Tensor<br /><br />[\"Country 77\"] -> Tensor<br /><br />[\"Country 78\"] -> Tensor<br /><br />[\"Country 79\"] -> Tensor<br /><br />[\"Country 80\"] -> Tensor<br /><br />[\"Country 81\"] -> Tensor<br /><br />[\"Country 82\"] -> Tensor<br /><br />[\"Country 83\"] -> Tensor<br /><br />[\"Country 84\"] -> Tensor<br /><br />[\"Country 85\"] -> Tensor<br /><br />[\"Country 86\"] -> Tensor<br /><br />[\"Country 87\"] -> Tensor<br /><br />[\"Country 88\"] -> Tensor<br /><br />[\"Country 89\"] -> Tensor<br /><br />[\"Country 90\"] -> Tensor<br /><br />[\"Country 91\"] -> Tensor<br /><br />[\"Country 92\"] -> Tensor<br /><br />[\"Country 93\"] -> Tensor<br /><br />[\"Country 94\"] -> Tensor<br /><br />[\"Country 95\"] -> Tensor<br /><br />[\"Country 96\"] -> Tensor<br /><br />[\"Country 97\"] -> Tensor<br /><br />[\"Country 98\"] -> Tensor<br /><br />[\"Country 99\"] -> Tensor<br /><br />[\"Country 100\"] -> Tensor<br /><br />[\"Country 101\"] -> Tensor<br /><br />[\"Country 102\"] -> Tensor<br /><br />[\"Country 103\"] -> Tensor<br /><br />[\"Country 104\"] -> Tensor<br /><br />[\"Country 105\"] -> Tensor<br /><br />[\"Country 106\"] -> Tensor<br /><br />[\"Country 107\"] -> Tensor<br /><br />[\"Country 108\"] -> Tensor<br /><br />[\"Country 109\"] -> Tensor<br /><br />[\"Country 110\"] -> Tensor<br /><br />[\"Country 111\"] -> Tensor<br /><br />[\"Country 112\"] -> Tensor<br /><br />[\"Country 113\"] -> Tensor<br /><br />[\"Country 114\"] -> Tensor<br /><br />[\"Country 115\"] -> Tensor<br /><br />[\"Country 116\"] -> Tensor<br /><br />[\"Country 117\"] -> Tensor<br /><br />[\"Country 118\"] -> Tensor<br /><br />[\"Country 119\"] -> Tensor<br /><br />[\"Country 120\"] -> Tensor<br /><br />[\"Country 121\"] -> Tensor<br /><br />[\"Country 122\"] -> Tensor<br /><br />[\"Country 123\"] -> Tensor<br /><br />[\"Country 124\"] -> Tensor<br /><br />[\"Country 125\"] -> Tensor<br /><br />[\"Country 126\"] -> Tensor<br /><br />[\"Country 127\"] -> Tensor<br /><br />[\"Country 128\"] -> Tensor<br /><br />[\"Country 129\"] -> Tensor<br /><br />[\"Country 130\"] -> Tensor<br /><br />[\"Country 131\"] -> Tensor<br /><br />[\"Country 132\"] -> Tensor<br /><br />[\"Country 133\"] -> Tensor<br /><br />[\"Country 134\"] -> Tensor<br /><br />[\"Country 135\"] -> Tensor<br /><br />[\"Country 136\"] -> Tensor<br /><br />[\"Country 137\"] -> Tensor<br /><br />[\"Country 138\"] -> Tensor<br /><br />[\"Country 139\"] -> Tensor<br /><br />[\"Country 140\"] -> Tensor<br /><br />[\"Country 141\"] -> Tensor<br /><br />[\"Country 142\"] -> Tensor<br /><br />[\"Country 143\"] -> Tensor<br /><br />[\"Country 144\"] -> Tensor<br /><br />[\"Country 145\"] -> Tensor<br /><br />[\"Country 146\"] -> Tensor<br /><br />[\"Country 147\"] -> Tensor<br /><br />[\"Country 148\"] -> Tensor<br /><br />[\"Country 149\"] -> Tensor<br /><br />[\"Country 150\"] -> Tensor<br /><br />[\"Country 151\"] -> Tensor<br /><br />[\"Country 152\"] -> Tensor<br /><br />[\"Country 153\"] -> Tensor<br /><br />[\"Country 154\"] -> Tensor<br /><br />[\"Country 155\"] -> Tensor<br /><br />[\"Country 156\"] -> Tensor<br /><br />[\"Country 157\"] -> Tensor<br /><br />[\"Country 158\"] -> Tensor<br /><br />[\"Country 159\"] -> Tensor<br /><br />[\"Country 160\"] -> Tensor<br /><br />[\"Country 161\"] -> Tensor<br /><br />[\"Country 162\"] -> Tensor<br /><br />[\"Country 163\"] -> Tensor<br /><br />[\"Country 164\"] -> Tensor<br /><br />[\"Country 165\"] -> Tensor<br /><br />[\"Country 166\"] -> Tensor<br /><br />[\"Country 167\"] -> Tensor<br /><br />[\"Country 168\"] -> Tensor<br /><br />[\"Country 169\"] -> Tensor<br /><br />[\"Country 170\"] -> Tensor<br /><br />[\"Country 171\"] -> Tensor<br /><br />[\"Country 172\"] -> Tensor<br /><br />[\"Country 173\"] -> Tensor<br /><br />[\"Country 174\"] -> Tensor<br /><br /></td>\n",
+       "            <td>c0f71066-0ca1-4062-a356-bf5793ab9070</td>\n",
+       "          </tr>\n",
+       "\n",
+       "          <tr>\n",
+       "            <td>[4]</td>\n",
+       "            <td>COVID19 Cases in 175 countries</td>\n",
+       "            <td>Weekly data for an entire year</td>\n",
+       "            <td>[\"Country 0\"] -> Tensor<br /><br />[\"Country 1\"] -> Tensor<br /><br />[\"Country 2\"] -> Tensor<br /><br />[\"Country 3\"] -> Tensor<br /><br />[\"Country 4\"] -> Tensor<br /><br />[\"Country 5\"] -> Tensor<br /><br />[\"Country 6\"] -> Tensor<br /><br />[\"Country 7\"] -> Tensor<br /><br />[\"Country 8\"] -> Tensor<br /><br />[\"Country 9\"] -> Tensor<br /><br />[\"Country 10\"] -> Tensor<br /><br />[\"Country 11\"] -> Tensor<br /><br />[\"Country 12\"] -> Tensor<br /><br />[\"Country 13\"] -> Tensor<br /><br />[\"Country 14\"] -> Tensor<br /><br />[\"Country 15\"] -> Tensor<br /><br />[\"Country 16\"] -> Tensor<br /><br />[\"Country 17\"] -> Tensor<br /><br />[\"Country 18\"] -> Tensor<br /><br />[\"Country 19\"] -> Tensor<br /><br />[\"Country 20\"] -> Tensor<br /><br />[\"Country 21\"] -> Tensor<br /><br />[\"Country 22\"] -> Tensor<br /><br />[\"Country 23\"] -> Tensor<br /><br />[\"Country 24\"] -> Tensor<br /><br />[\"Country 25\"] -> Tensor<br /><br />[\"Country 26\"] -> Tensor<br /><br />[\"Country 27\"] -> Tensor<br /><br />[\"Country 28\"] -> Tensor<br /><br />[\"Country 29\"] -> Tensor<br /><br />[\"Country 30\"] -> Tensor<br /><br />[\"Country 31\"] -> Tensor<br /><br />[\"Country 32\"] -> Tensor<br /><br />[\"Country 33\"] -> Tensor<br /><br />[\"Country 34\"] -> Tensor<br /><br />[\"Country 35\"] -> Tensor<br /><br />[\"Country 36\"] -> Tensor<br /><br />[\"Country 37\"] -> Tensor<br /><br />[\"Country 38\"] -> Tensor<br /><br />[\"Country 39\"] -> Tensor<br /><br />[\"Country 40\"] -> Tensor<br /><br />[\"Country 41\"] -> Tensor<br /><br />[\"Country 42\"] -> Tensor<br /><br />[\"Country 43\"] -> Tensor<br /><br />[\"Country 44\"] -> Tensor<br /><br />[\"Country 45\"] -> Tensor<br /><br />[\"Country 46\"] -> Tensor<br /><br />[\"Country 47\"] -> Tensor<br /><br />[\"Country 48\"] -> Tensor<br /><br />[\"Country 49\"] -> Tensor<br /><br />[\"Country 50\"] -> Tensor<br /><br />[\"Country 51\"] -> Tensor<br /><br />[\"Country 52\"] -> Tensor<br /><br />[\"Country 53\"] -> Tensor<br /><br />[\"Country 54\"] -> Tensor<br /><br />[\"Country 55\"] -> Tensor<br /><br />[\"Country 56\"] -> Tensor<br /><br />[\"Country 57\"] -> Tensor<br /><br />[\"Country 58\"] -> Tensor<br /><br />[\"Country 59\"] -> Tensor<br /><br />[\"Country 60\"] -> Tensor<br /><br />[\"Country 61\"] -> Tensor<br /><br />[\"Country 62\"] -> Tensor<br /><br />[\"Country 63\"] -> Tensor<br /><br />[\"Country 64\"] -> Tensor<br /><br />[\"Country 65\"] -> Tensor<br /><br />[\"Country 66\"] -> Tensor<br /><br />[\"Country 67\"] -> Tensor<br /><br />[\"Country 68\"] -> Tensor<br /><br />[\"Country 69\"] -> Tensor<br /><br />[\"Country 70\"] -> Tensor<br /><br />[\"Country 71\"] -> Tensor<br /><br />[\"Country 72\"] -> Tensor<br /><br />[\"Country 73\"] -> Tensor<br /><br />[\"Country 74\"] -> Tensor<br /><br />[\"Country 75\"] -> Tensor<br /><br />[\"Country 76\"] -> Tensor<br /><br />[\"Country 77\"] -> Tensor<br /><br />[\"Country 78\"] -> Tensor<br /><br />[\"Country 79\"] -> Tensor<br /><br />[\"Country 80\"] -> Tensor<br /><br />[\"Country 81\"] -> Tensor<br /><br />[\"Country 82\"] -> Tensor<br /><br />[\"Country 83\"] -> Tensor<br /><br />[\"Country 84\"] -> Tensor<br /><br />[\"Country 85\"] -> Tensor<br /><br />[\"Country 86\"] -> Tensor<br /><br />[\"Country 87\"] -> Tensor<br /><br />[\"Country 88\"] -> Tensor<br /><br />[\"Country 89\"] -> Tensor<br /><br />[\"Country 90\"] -> Tensor<br /><br />[\"Country 91\"] -> Tensor<br /><br />[\"Country 92\"] -> Tensor<br /><br />[\"Country 93\"] -> Tensor<br /><br />[\"Country 94\"] -> Tensor<br /><br />[\"Country 95\"] -> Tensor<br /><br />[\"Country 96\"] -> Tensor<br /><br />[\"Country 97\"] -> Tensor<br /><br />[\"Country 98\"] -> Tensor<br /><br />[\"Country 99\"] -> Tensor<br /><br />[\"Country 100\"] -> Tensor<br /><br />[\"Country 101\"] -> Tensor<br /><br />[\"Country 102\"] -> Tensor<br /><br />[\"Country 103\"] -> Tensor<br /><br />[\"Country 104\"] -> Tensor<br /><br />[\"Country 105\"] -> Tensor<br /><br />[\"Country 106\"] -> Tensor<br /><br />[\"Country 107\"] -> Tensor<br /><br />[\"Country 108\"] -> Tensor<br /><br />[\"Country 109\"] -> Tensor<br /><br />[\"Country 110\"] -> Tensor<br /><br />[\"Country 111\"] -> Tensor<br /><br />[\"Country 112\"] -> Tensor<br /><br />[\"Country 113\"] -> Tensor<br /><br />[\"Country 114\"] -> Tensor<br /><br />[\"Country 115\"] -> Tensor<br /><br />[\"Country 116\"] -> Tensor<br /><br />[\"Country 117\"] -> Tensor<br /><br />[\"Country 118\"] -> Tensor<br /><br />[\"Country 119\"] -> Tensor<br /><br />[\"Country 120\"] -> Tensor<br /><br />[\"Country 121\"] -> Tensor<br /><br />[\"Country 122\"] -> Tensor<br /><br />[\"Country 123\"] -> Tensor<br /><br />[\"Country 124\"] -> Tensor<br /><br />[\"Country 125\"] -> Tensor<br /><br />[\"Country 126\"] -> Tensor<br /><br />[\"Country 127\"] -> Tensor<br /><br />[\"Country 128\"] -> Tensor<br /><br />[\"Country 129\"] -> Tensor<br /><br />[\"Country 130\"] -> Tensor<br /><br />[\"Country 131\"] -> Tensor<br /><br />[\"Country 132\"] -> Tensor<br /><br />[\"Country 133\"] -> Tensor<br /><br />[\"Country 134\"] -> Tensor<br /><br />[\"Country 135\"] -> Tensor<br /><br />[\"Country 136\"] -> Tensor<br /><br />[\"Country 137\"] -> Tensor<br /><br />[\"Country 138\"] -> Tensor<br /><br />[\"Country 139\"] -> Tensor<br /><br />[\"Country 140\"] -> Tensor<br /><br />[\"Country 141\"] -> Tensor<br /><br />[\"Country 142\"] -> Tensor<br /><br />[\"Country 143\"] -> Tensor<br /><br />[\"Country 144\"] -> Tensor<br /><br />[\"Country 145\"] -> Tensor<br /><br />[\"Country 146\"] -> Tensor<br /><br />[\"Country 147\"] -> Tensor<br /><br />[\"Country 148\"] -> Tensor<br /><br />[\"Country 149\"] -> Tensor<br /><br />[\"Country 150\"] -> Tensor<br /><br />[\"Country 151\"] -> Tensor<br /><br />[\"Country 152\"] -> Tensor<br /><br />[\"Country 153\"] -> Tensor<br /><br />[\"Country 154\"] -> Tensor<br /><br />[\"Country 155\"] -> Tensor<br /><br />[\"Country 156\"] -> Tensor<br /><br />[\"Country 157\"] -> Tensor<br /><br />[\"Country 158\"] -> Tensor<br /><br />[\"Country 159\"] -> Tensor<br /><br />[\"Country 160\"] -> Tensor<br /><br />[\"Country 161\"] -> Tensor<br /><br />[\"Country 162\"] -> Tensor<br /><br />[\"Country 163\"] -> Tensor<br /><br />[\"Country 164\"] -> Tensor<br /><br />[\"Country 165\"] -> Tensor<br /><br />[\"Country 166\"] -> Tensor<br /><br />[\"Country 167\"] -> Tensor<br /><br />[\"Country 168\"] -> Tensor<br /><br />[\"Country 169\"] -> Tensor<br /><br />[\"Country 170\"] -> Tensor<br /><br />[\"Country 171\"] -> Tensor<br /><br />[\"Country 172\"] -> Tensor<br /><br />[\"Country 173\"] -> Tensor<br /><br />[\"Country 174\"] -> Tensor<br /><br /></td>\n",
+       "            <td>f1e37695-43f1-4d44-9242-b0d62391e406</td>\n",
+       "          </tr>\n",
+       "        </table>\n",
+       "\n",
+       "        <script>\n",
+       "        function myFunction() {\n",
+       "          // Declare variables\n",
+       "          var input, filter, table, tr, td, i, txtValue;\n",
+       "          input = document.getElementById(\"myInput\");\n",
+       "          filter = input.value.toUpperCase();\n",
+       "          table = document.getElementById(\"myTable\");\n",
+       "          tr = table.getElementsByTagName(\"tr\");\n",
+       "\n",
+       "          // Loop through all table rows, and hide those who don't match the search query\n",
+       "          for (i = 0; i < tr.length; i++) {\n",
+       "            name_td = tr[i].getElementsByTagName(\"td\")[1];\n",
+       "            desc_td = tr[i].getElementsByTagName(\"td\")[2];\n",
+       "            asset_td = tr[i].getElementsByTagName(\"td\")[3];\n",
+       "            id_td = tr[i].getElementsByTagName(\"td\")[4];\n",
+       "            if (name_td || desc_td || asset_td || id_td) {\n",
+       "              name_txtValue = name_td.textContent || name_td.innerText;\n",
+       "              desc_txtValue = desc_td.textContent || name_td.innerText;\n",
+       "              asset_txtValue = asset_td.textContent || name_td.innerText;\n",
+       "              id_txtValue = id_td.textContent || name_td.innerText;\n",
+       "              name_bool = name_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              desc_bool = desc_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              asset_bool = asset_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              id_bool = id_txtValue.toUpperCase().indexOf(filter) > -1;\n",
+       "              if (name_bool || desc_bool || asset_bool || id_bool) {\n",
+       "                tr[i].style.display = \"\";\n",
+       "              } else {\n",
+       "                tr[i].style.display = \"none\";\n",
+       "              }\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "        </script>"
+      ],
+      "text/plain": [
+       "<syft.core.node.common.client_manager.dataset_api.DatasetRequestAPI at 0x7fe573adffa0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_domain.datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d3dffd-b4e2-4f51-b8d2-bc54029dbafb",
+   "metadata": {},
+   "source": [
+    "#### Select the COVID dataset\n",
+    "\n",
+    "`covid_ds` is a pointer to the dataset above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b6e02425-e5ba-4b09-af2f-dcb535d6b5c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_ds = ds_domain.datasets[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b049e413-7754-4d0b-8875-cb220c94f307",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# covid_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "970dca10-da81-4f63-b73e-1c3322d9d4ff",
+   "metadata": {},
+   "source": [
+    "#### Access the tensors in the dataset\n",
+    "\n",
+    "1. Create `result` - a pointer to one of the selected dataset's tensors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "016d9fb0-30fa-4da5-8f4b-b015921d5d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = covid_ds[\"Country 0\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93b04a87-89e9-4f3e-843a-9a24044ef6fa",
+   "metadata": {},
+   "source": [
+    "2. `publish` uses the privacy budget approved by the data owner to access the data in a noised format that does not compromise the original dataset. `sigma` is the amount of privacy budget the data scientist plans to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c79c1042-c168-4846-960d-1c04b33dbbfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "published_result = result.publish(sigma=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc49d4a2-12e2-428d-a2ca-55d33ea1caa7",
+   "metadata": {},
+   "source": [
+    "3. We call `get()` to access the contents of the `published_result` pointer created above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b71f2d2f-a2ca-4c4c-89e2-97746ed033c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.87962505,   4.71938444,   0.21009209,  11.6951488 ,\n",
+       "         1.65871529,   7.51741406,  -2.69433908,   6.69456906,\n",
+       "         9.47428484,   2.95521849,   6.6730533 ,  -5.14659351,\n",
+       "        17.93966438,   2.96712695,   1.68368624,  -7.09433619,\n",
+       "       -19.40986988,  -5.46137163,  -4.09276642,  13.18786713,\n",
+       "        17.52276474,  22.4753219 , -23.93438919,  -5.97426366,\n",
+       "       -10.57195696,   8.46965483,  -2.20720727,  -3.57068655,\n",
+       "         2.16387452,  13.52685285,  -1.19839955, -10.9106257 ,\n",
+       "         4.29427713,  -0.13939782,  -7.40138386,  13.52924455,\n",
+       "         3.6314732 ,   0.54333269,  -8.26701998, -13.7421667 ,\n",
+       "       -17.83994344,  -6.27095021,   2.80863972, -11.48666258,\n",
+       "         9.6186131 , -16.55402525,  13.72067735, -17.28392862,\n",
+       "        -4.05404757, -10.67938915,  -3.42309606,  -9.16542317,\n",
+       "        -3.10181604,  -0.25535211])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "published_result.block_with_timeout(30).get()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1934519f-b545-4d0d-bfeb-5047810bead1",
+   "metadata": {},
+   "source": [
+    "#### Great! Now let's access a portion of the dataset in the same way.\n",
+    "\n",
+    "Note that we are setting `sigma=10` for each object. In this case each object represents a different country's weekly COVID numbers - each country is a \"data subject\" or \"entity\". We pause between each `.publish()` and `.get()` call to allow the server to receive the request and prepare the necessary data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e168badb-9544-436c-a5da-c545da7e715b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100.0\n",
+      "100.0\n",
+      "100.0\n",
+      "100.0\n",
+      "100.0\n",
+      "100.0\n",
+      "100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "published_data = []\n",
+    "\n",
+    "## \"countries\" 24 and 129 had the best / worst rates of infection\n",
+    "for i in [24, 129] + list(range(5)):\n",
+    "    result = covid_ds[f'Country {i}']\n",
+    "    published_result = result.publish(sigma=10)\n",
+    "    published_result.block_with_timeout(30)\n",
+    "    published_data.append(published_result.get())\n",
+    "    print(ds_domain.privacy_budget)\n",
+    "    \n",
+    "published_data = np.asarray(published_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dd79a501-6a69-4aad-8090-0ccc9d95c1b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(published_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba1b5f83-8e9d-435c-9f61-eab3d15572a9",
+   "metadata": {},
+   "source": [
+    "#### Load the dataset portion into a Pandas dataframe.\n",
+    "\n",
+    "Let's plot the noisy data as well. In comparison to the data visualized in Lesson 3, it is impossible (thanks to differential privacy) to tell which country is which - but the machine learning properties of the data remain the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c8e687fb-9a07-425d-9ccb-64cd5d6a2d79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<AxesSubplot:>,\n",
+       "           0          1         2          3          4          5          6   \\\n",
+       " 0  20.336905  -8.964656 -2.982325  10.859373   5.490088  -1.173325   2.693699   \n",
+       " 1  -6.128690 -13.911575  2.626617  12.566412   4.911579  -3.845937   9.437008   \n",
+       " 2   1.371150  -3.700766  8.279892  -8.007940  -0.912172   0.194758   3.440402   \n",
+       " 3  24.991676  -4.794710 -0.667047   0.743543   6.049031  13.066630  14.961670   \n",
+       " 4  -1.400285  -7.923442 -8.091082   3.726426  34.372218  -8.666324   4.028763   \n",
+       " 5   9.683693   1.340415  2.241332  -3.487355  10.334584   1.303773 -12.435732   \n",
+       " 6  -4.006095   8.412063 -1.585492   5.913141  16.689908  -8.757812 -14.523390   \n",
+       " \n",
+       "           7         8          9   ...        44         45        46  \\\n",
+       " 0  -9.795669 -5.619013  -3.486675  ...  8.072695  -2.876978 -7.555695   \n",
+       " 1   8.366968 -2.278678   7.517804  ... -7.568879   5.685375  3.052952   \n",
+       " 2  -7.312792 -6.719416  -7.905397  ...  9.455157  22.467539  5.925469   \n",
+       " 3  20.595809 -6.638701  -1.254496  ...  3.641747  13.607257  4.160950   \n",
+       " 4  -3.296568  8.948958   4.032766  ... -9.105121  -5.144218  1.019476   \n",
+       " 5  -7.018836  2.712449 -14.644314  ...  8.092646  -2.664616  0.421414   \n",
+       " 6  11.204030  6.967486  17.121681  ... -3.987955   6.548862  3.968059   \n",
+       " \n",
+       "          47         48         49         50         51         52         53  \n",
+       " 0  6.163197   0.657244   6.995522   8.539384   7.510291   6.401523   9.131963  \n",
+       " 1 -5.662325   3.755135   5.259989   4.081813   2.294437  -1.378383   2.477861  \n",
+       " 2  6.255276 -18.542731   7.226785   1.458280 -15.884218   9.274611 -13.721612  \n",
+       " 3  8.728846  15.814909  13.126770  15.093443  11.260606   3.461493  -0.786025  \n",
+       " 4  8.247414   5.094191  -0.036105 -15.232789  -7.120206   8.294720 -15.700254  \n",
+       " 5 -0.187400   5.944428 -13.169132  -1.339957  -1.416009   9.221461 -13.140464  \n",
+       " 6  5.439363 -14.932491 -14.367184  17.864755   2.847410 -16.724201   6.841550  \n",
+       " \n",
+       " [7 rows x 54 columns])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_df = pd.DataFrame(published_data)\n",
+    "data_df.plot(legend=False), data_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ffded385-2d2f-4cd1-adc2-3284acc7d154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data_df[\"total_cases_per_week\"] = data_df.sum(1)\n",
+    "# data_df.plot(legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42a2d945-b120-49bf-87ec-e3f8ccdc2f96",
+   "metadata": {},
+   "source": [
+    "#### Let's focus on two countries\n",
+    "\n",
+    "One had the best (lowest) infection rate and one had the worst (highest) infection rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a09108b5-1792-4353-b53f-9725b7bbb958",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_df.loc[1].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8cea0971-8fa6-4281-85e8-2be130addd1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_df.loc[0].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd73585-51b7-40a1-b542-bfeee8c11f81",
+   "metadata": {},
+   "source": [
+    "#### And extrapolate out 3 more months"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d67247e6-0654-4248-aa0e-4b45d1ff0014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_extrapolated_country(idx):\n",
+    "    x = list(range(53))\n",
+    "    y = data_df.loc[idx].values\n",
+    "\n",
+    "    plt.plot(y)\n",
+    "    \n",
+    "    z = np.polyfit(x, y, 2)\n",
+    "    f = np.poly1d(z)\n",
+    "\n",
+    "    new_points = range(12)\n",
+    "    new_y = []\n",
+    "    for x2 in new_points:\n",
+    "        new_y.append(f(53+x2))\n",
+    "        \n",
+    "    plt.plot(range(53, 65), new_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a1e0c97c-4d6c-4278-ba0c-19f36ef00228",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e669q+hqdiqNEXlTydjz5xVQOnCsd+QJeNzYPRbDPYb58OldAwOvuCJHnnOPAxBJ2qlaNoCsgmebkF1MyuoNS2RAMO1xJdk2hyJHI5ssOQglKfeWbn7D56pOn8eTJWfzlndvx1mtH8IVHjuNTDxyuuY/yrWfOYdNgGNeto1bSl4ujRF7z5PO1I5RC0HpCSoJD2XxdslVddAqpXAFBrxsRv2SakxeHwHweV0tF/sJ8GktpGTtHussu7w5KpnbNfDKH3qAXYZ8HXQHJdlZ+Jp7FkLpX4cRDcHqSOdG3plrkh7v8AICLS83dizkwvoTPPXQMt21fhffesBZ//fZr8P4b1+HrT5/FR7+7z/QQ3oHxJewbX8J7rr8K7Aro+94sHCXy9aRrhKD1qSK/a7QbS2kZ5+Y6NDdsQKqOSn6sL9RSkReH0XaNllfyZnZNRi4gLRe0F+mR7kAdlXwGGwbCAEodGp1K5VQoPQMRHxhDUwMHyWwe/+Xbz6M/7MNn3rYTjDG4XAx/esfV+OhrNuPe5yfwoX9+1nAf7VvPnENAcuMtL6YN1+XgKJHXcvI2PHmtkg8KkVc3Xx1k2aRlUcl7zCt5VeTX9YdauvF6YHwJXrcLm4fKc/rdQWO7RmTkxd9vpCdg21ueiWcx3BVQ+9U7u5IXf/ewr9qTl9wu9IV8mI43T+T/4v5DODuXxBfe+SJ0q38rQMnp/+dbN+Ev37wDjx6dxt0VQh/LyLjvhUm86ZrVWsqKuDwcJfKaJ29D5IWg9YWVB56y4edy1OZrKpdH0OtR7RrjilUI6Nr+IBaS5h0fm83+8SVsHY7A6yl/SHYFvFhKyVXrEpn+3pA6nFqt5Gutn3OOmUQWAxEfogHJ8SKfsKjkAWAo6sOlWHPsmgf2TeK7z47j927eiBvW9xne5jdvWIvPvm0nnjgxgw/+372a0N/73ATScgHvuYE2XJeLs0S+jpz8QkUlL7ld2LY66phDUcUiR0YuIiBZ2zWxtIyI34OBsA/5Im+ZfXEpnsGa3mDV5d1BCblCsaoTpajkRXU42hNAMleoKdqLKRlygWMw4kNXQHL8YSgruwYAhqL+ptk1X/n5KVw9HMVHbt1kebt37rkKn33rLjx5clYT+m89cw67Rruwa7S7KWu7knCUyIuNV7uVfMTnKascrxntxsGJmO3eN+2M+B2IjddENm84OGQpLaMrIKFX9bZb5ctn5aIWgdXTY9KJUqxTrHvUZsOtGfXAlJMq+VhGxl89eMQw8x8z6CWvp1mVfL5QxInpBF6xqR8eGy1G3rFnDT77NkXo7/zSUzh+KUGxyQbhKJH3eVxgDLamQy2kcugNe8suu2ZNF9JyASdnEs1a4oohKt+A160lK4wGRFSLfGtOvSo9ZKofjqITZWXCZlGr5IVdYy8rL+KTAxEfon5niPyXHjuJf/j5aTxxfLbqOvE3N8rJA8BgxI+5ZLbhbabPziWRyxexZch+L6R37F6Dz/2Ha3B8Oo6I34M3XrO6oWu6UnHUwEPGGPw2e8rPJ3OaVSMQbw33X1jC1lXRZixxxRAJo4Dkhs8j+tfIVZtY1SLfGtEzaxSm9a9JVVbyytf6jVegdlZeiLywa45MdXa6ZmIxja8/fVb9vDoZZseu4Vxp2jbcFbB1n8lsHr/x1Wfwl3duN7VTjl6MA1D2uurhP7xkFP1hLziAoNdR8tQyHFXJA6KnfO2qZC6R0+KTgnV9IUR8no5I2BSLHCen46bXp2TlyR30erTZnka+fNtU8vmiYSVvJvILqRwifo82CKMnKCEguWtW8iJJMqCKfKdX8v/r4WMAlBdzoxe4eEaG28W0/apKxHmBeiybs3NJ7LuwiIcOXTS9zbGLcbhdDBsHw7a/r+BVWwZx8xbD8RPEZeA4kfd7XLYq+YVUThM2gcvFsHO0qyNE/ufHZ/CaL/wCF+aNc/3CrhGePGAt8n0h5cleT4zyzGyyvDPgZSIXiigUuYknr9o1FZ0oF1Ll78RE61w7lbxfciHs8yAa8CCRzXfsHszhyRjufX4Cv/WyMYz1hwz3IxJqczKzw0RDUeVAVD2br2I/xCqJdvRiHGN9Qce3ce4EnCfy3tp2Deccc8lqkQcUy+boVBzZOgaCt4LpeAacwzTjnNZ58uKtulFWfiktoysoIeB1IyC5TacwVZKRC/jAN36NP/zu8uekZtUTykaCYNaJUmkuV/73G+kOYLzGqdeZeBaDET8YY9r3tmrD3M78z4eOIuLz4HdetcG0o2TcoJe8HtGldPoyRP7AhPkJ8WMX4x1veToFx4l8QHIjW0PkUzmlva6RyF89HEG+yHFmNtmsJTaEZFb5Gc0ij+mySl6IfPltRZthIXa9Ia/tSv5vHz2B0zNJpOoYt2iGiLz6DOwas06UiylZS94I7FTy03ElIw+UXkA60bJ5+tQsfnZsBh++eSO6g15V5FNVohszaTMs6Av54HaxuuwaIfKLKdnwhSWZzeP8fKpuP55oDo4U+VqVfGXfGj2bBpUH5olL7Z2wSak9Scyq0JRsZNeUi5mojvUibydCeXBiCff84jQCkhuFIl92MkOIvJFdAxi3NhB9a/SMdAewkJK1340RM/EsBtSWtWKvotNEvljk+MyDR7G6y4+7XjYGQPnZjc4JJLKy9nMa4XYxDIR9l2XXAKUZAHqOX7q8TVeiOThO5P2Su2bvmrmKvjV61g+E4GLKNJp2Jqn+jGbtCsTov4DXo1XylVW/EIR6RF4uFPGH39uPvpAXH3zFOgD2+/ebITbKjSp5sb7KnPxiqtquGbWRsJlJZDWLokt9J9BpPeV/dGAK+8eX8Aev3aJZXKWOkuU/ey27BlCz8nH7lfycOpFLchu35z6mJmu2ksi3Bc4U+RrpmgWLSt4vubG2L4TjbV7Ji0EPppW8sGskN/yS23BwSKXI99kQ+a/87BSOTMXw39+8AwPqpp2dNJMVWiVvsknXHZTKukVm8wUkc4Vqu6ZbPRBlkrDJ5gtYTMlaJd+pds0XHzuBrasieMu1I9plpcNg5XsS8Yxxm2E9g1F/fZ58IoehqB9bV0VxQG0sp+foxTiCXjfW9FSfYCZWHseJfMBb25O3quQBYNNgGMct4ontgPDkzSp5/WEoAIatDSpFvqeGyB+/FMcXHzuJO3YN47XbV2n5++VW8mKT21TkA16tjQFQspmqNl5rVPKzCeV7dLInn5ELODGdwO07huF2lRIza0wqebOpUHqUU6/12TW9IS92jHThgEF77mMX49g0FIHLRe2B2wHnibxUO0K5UHEkvpLNQxGcm0u1dcJG+M4Ji41XF4MmxHZEvjfkRSpXMBTtQpHjj763HyGfG59603YAJVHO2ujfb4V4J+D3GD8ce0Lldk1lB1HBYMQPyc1Ms/LaQaho53ryF+ZT4BwY6y+vkqMBD8I+T5nIc85Nh3jrGYr4sZCSbT/e51M59IW92DXahVhG2WTV3+exS3FsreOkK9FcHCfyfhsbr3PJHCQ3M00dbBoKo9DmCZuSJ29u1wS9pXy00eAQI7sGMM7KP3NmDi9cWMR/e/3V6FPtDv8KVfKVnSgrm8sJ3C6G4S7zhI2wJAbCfvX+XIqNle6cCOVZdd7B2r5Q2eWMsaoYZTZfhFzglukaoJSVn7aZsBGVvJjipfflZxJZzCdztOnaRjhO5AM2Nl7nk1n0hrymB0RET/N29uVTqidvGqGU85pVA5hX8oyV+pqIdzZGWfmT08rv4qbNA9plpUq+uRuvohOlePFe0Oya6gp1pNu8r7y+ORmgCGOnNSk7N6cUHmN91X63iFEKxN/baCqUHi0rb6OvfKHI1YOEPmweUlpD6xM2tOnafjhO5P2SG9l80XJ25HxSrqoC9azrb/+ETULbeDX35INVIl9+21haRsTn0bzdXotK/vRMEiGvG4OqQAK6cYuN2ng1iVB2q+80hLjPq/58ZYQSsM7Kz8SzYKw0QwBQbI5Oajd8di6JroBUNoBDUPkCF9c6UNawa7RTr7UreTEbuTcowetx4erh8vbcxy6zZw3RPBwn8qJ6tfKJ55PZsid6JX7JjbG+UFtn5VM27Bp9vxKjwSGLqZwWIwRg2b/m1EwCGwbDZe9+6pnEZYXmyZuma8o7US4my3vJ6xnpDuBSPIOcwd9/Op5Fb9Cr9bsBFKuqkyKU5+ZShlU8oMQo45m89s5E/L3t2jV2Nl+1Fs+qZbdrpAsHJ2JaUXXsYhz9YZ9m6RGtx3EiL3xiK19+ISWjN2T9INw0ZJ2wOTixZCgkK4V2GCprlpM3quSr7Rp9V0qtf03CuJJf31/uA/s8Da7kLewaoDR0ez6VQ7hiFoBgpCcAzoGLS9WCNaM77SrotCZlZ+eSVX68oDJGWWozbC3yPUEJXrfLViVfORt550gXEtk8zqg20rFLcbJq2gzHibyo5K2qy7lEFr1B67ewVgmbk9Nx3PHFJ/HAvsnlLXYZlCKUZpV8vqxVq9HgkEqRj/gV62ahooVAOlfAxGIa6wfKOwoKUV62J18rQhkst2sWU7KhHw8Ao1pWvrqHjZHId1JP+Vy+iImFtGUlD5RilHbtGsYYBqM+W1n5ymEtO9XZyAcnllAochy/FCerps1wnMjXmg4lF4qIZfI2KvkICkWO0zPVCZtHj0wDqD2golkUilz7+eKZvGGTqFSuULbxajQ4pFLkXS6GnmB1Vl6kjNYPlFeQjfLks2Lj1SxCWdGJ0mgWgEAI3fk5eyLfSSMAxxdSKPLqZI2g8pxArEYveT1DUT8u2dh4nasQ+U2DYW028vn5FDJykUS+zXCuyJskbESV2mtSCQo2qX2wjxtsvj5+TBH52URreq8Lq6Y/7C0TfD1pudKTr+5EuZTOVw0R6Qt5q+yaU+qkrA2Vlbyn9rsmO2TyBXg9LtO0U2Unyso2w3pGewLoD/vw1Km5sss55+Yib/JC2W6cU1+4KjPygp6ghKDXrVXytYZ467E7BrDyjILH7cL21VEcGF/CsYsxAJSsaTccJ/K1hnmX3m5aV/LrB0Jwu5gWHRTEMzL2nl0A0EqRV342sWFmZNlUp2vK2+pyzhFLy4hWiLxR/5rTM0kwpqSO9IjIY8bCrknl8vgv//q8pRWgzHc1fyiKTpTCVlFE3vhF2uVieNWWAfz82HRZn/hYOo9coai1NBBEAx4UitxwNGK7cVaLTxpX8qWsvPJiYHfjFVAOkl0y2MeoZD6pDGvR74fsGu3GwcklHJ6Kg7FSkz+iPXCeyHutLYRKT9EMn8eNtX3Bqkr+qZOzyBc5gl43ZuOtGXot+tasshD5dIVdU9luOCMXkSsUqyr53pBXiygKTs8msLorUOWZl9oamNs1xy7Gcf++SfxafWE0wmz0n57ugFfL7y8kZcO+Q4Jbtg4ilsnjufOL2mUiAz6o/s4E4uc3O2/QTpybSyHi81g+dke6A2WefEBy2xqkPRT1I57Na48tM+aT1RPVdo50IZUr4McHpjDWFyp73BGtx3kiX8OTtyvyALB5MFIVo3z86Awifg9evrEfsy0aladV8l1C5Ms9Zc65uvFqVMkrtxVVcXeg/PdgVMmfmklU+fGAUjn6PC7LXkHi72CVdrIl8kGltUEuX0QimzfMyAtevqkfHhfDY0entcu0Ad7harsGQFkDtHbl7FwSa/uDprYWoOxJiL0iO31rBEPagSjrx7TRsBax+XpyOlHX4G5iZXCcyIvERyNEftNQGGfnkpr1wznHz45P46ZNA1jV5cdsHe1ZG0miRiWfKxRR5KhI15RX8pUtDQS9IaV3u7A6OOc4M5Os8uMFfslt6cmL69IWPd4zsvF8Vz2iE6XIyndb/P2ifgl7xnrxuF7kK0676m8LGPeveeHCIm77m1+0Tfrm3FzKdNNVMNoTwFJaRiwj22ozLLCblZ8zqOQ3DIS14oo2XdsPB4q8PU++u0aEElASNkVeSpccmYrjUiyLV20ZQH/Yh1gm35ImZmLj1UzktdF/FhuvQiyrNl7DIsmi3O5SLItkroANBpU8oLyoWh08S+eU6ywr+bw9u2YxnbM87arnlq2DOHYprlW1oi+LOMIviGp2TbWQP3VyFkcvxvHChUXL+1oJ8oUiLsybH4QSiHTRxEIasYyMcI34pKA00Nta5EVLED1uF8OOEWXUH226th+OE3k7G69dAans1KMZm4fKEzYiVfNKVeQB44NDzUZk5M3sGv0Qb4GoWGM1KnmRmhAvhqfVZE1lRl5Qq5IX4p6y6CeUlYum8UlBd1DCQkrGQlLtW1PjRfrmrYMAoFk2M4ksfB4XIhWbkFbthkWfmEOT5gOrl8NHvv087nthwtZtJxczyBd5zUpeH6NMZPM1+9YIBm00KeOcYyFpfJBw50g3AKrk25Gmizxj7DbG2DHG2EnG2MebfX9i08csQmm0cWTGun4lYSN8+Z8dm8aOkSgGI370qxVvKxI2tSr5yl7ygLJJKrlZTbtG60SpvniZxScFfo/bcuPVlidvp5IPKp0oRQTWauNVWW8IV/UGNctGxCcr/WxtOpSByIuOj4cnY5b3dTlMxzO474VJfOqBw6b9h8rXYp2sEehPvdZj10R8HgQkt2Uln8gqCSWj58879ozit24cq7k+YuVpqsgzxtwA/h7A7QC2AXg3Y2xbM+9TZLetPPlaAiHwedwYUxM2SykZz55bwM1blApR9OZohciLSn4w4gNj1ZV8aYh36QnOGCtrN2zqyYfLK/lTamOyoahx5NQnuSwjlJmc8OStfPui1iLBDNGJclK1X2rtqTDGcMvWQTx9ahYZuYDpeKasuZog7PWAMetKvhkif2hC+Z7zyRz+8YkzNW9v1X1ST1/IC7/kwvhCGvGMbCs+CSi/r1pjAK1mI29dFcWfv3E7DQppQ5pdyV8H4CTn/DTnPAfg2wDubOYdulwMXo/LMkJpZ9NVsGkwgpPTCfzixAyKHHiVKvIDmsi3wq5R889+D8JeT1X8T1T6wYoom75/TUxrM1wuAlqTMrViPj2bxLqBkGmiQ6nkbaRrLO2aQu2NV/XF6LS6P2JnT+XmrYPIyEX88tSc4UEoQHm8RP3Vp17TuQIuxbIIed04M5esGS2sl4Nqe95XbRnAV584raV/zDg7l0JAchv+DHoYY1qMMpHJ12xpoGcw6res5GtNVCPak2aL/AiAC7qvx9XLNBhjdzPG9jLG9s7MzDTkTgMWPvF8Mldz007PZjVh89Chi+gOSnjRmm4AQH+kdXZNMqecEJXcLsPGYym52q4BytsNL6lthisrL82TF3bNdALr+42tGkCt5G3YNallRyiVdZ1R31nUqvwB4Pp1vQhIbjx2dNpU5AHjJmVi2tGtVw+Bc2VuaSM5MLGE9f0h/Nkd25DNF/HFx05Y3v7cXBJr+6zjk4LRniDOz6eQzBVs2zWAkrCxOrQmHhP1FElE62m2yBs9IsvOj3PO7+Gc7+ac7x4YGDC4ef34JZdh5ci5OvDAos1wJSJh8+DBi7hp04DWez3o9bTsQFQql0dIm91aPfEpbbDxCgARn1TmyXcZVMOS24Wo34P5ZBYZuYDJpbSpHw+U+vebkbZj1+TtRSgBJelk1GLYbG03buzHI0cuYSElYzDiN7xdNOCpEnnhgb9+5zAA4HCDN18PTcawfaQL6wfCeNeeNfiXZ85rlowRZ+dStv3u0Z6AdlLbrl0DAEMRpbWBWYsHLdlEIt9RNFvkxwGs0X09CqDprRsDJiMA49k85AKv6+2mmBJVKHLcvLX8Rag/7GuZJy/89ojfU3UkX0vXSOVPcH3Vv5SWqw5CCfrCPswlczgzmwTn1Y3J9Pgl68HpGZt2jR1PHgAuxjJ1icwtWwcxpR7Xt6rkKy0vIbgvXd+H7qCEw1ON8+XnkzlMLKaxU40dfuTWTZDcLvz1w8cNb18ocpyfS2GtSc+aSkZ7gsip5xyiddg1Q1E/0nIBcRNrqp4zJkT70GyR/zWATYyxdYwxL4B3Abi/yfdpGusTbzetpkJVIhI2jAE3baoUeW+LRD6PkE9U8tV2jTh4VG3XlG+8Vm66CnpDXiykcloHTkuR97jsRSgt0zU2KnndC5IdP16gf2GuPO0qMLJrzs6l0B2U0BWUsH11FIcauPkq/Pgdq5WTooNRPz7w8nV4YN+kdp2ei7EMcoWi7UpexCgBe83JBNoYQBPLZj6Zg8/jqnqHSLQ3TRV5znkewO8BeAjAEQDf4ZwfauZ9Aoq4GVXyWpvUOuwar8eFDQMh7Brtrpp207JKPpdHSH0bHjawa4xy8kD5C8Kihcj3BJVOlCI+WdmYTI+Srqlt12RMKnm5UEShyE1H/wm6DSZY2WG4K4Crh5WKufIglMCop/w53XCObcNRHL0YL2t4thwOqtbPdlXkAeDuV65HT1DCZ39ytOr259TN5rU1kjWCUZ3Ih+v05AHzMYBzCSV+bGdfgGgfmp6T55z/mHO+mXO+gXP+6WbfH2Ce+BANrurZeAWAv33XtfjCO66purwv7Fv2YahikWsHjuySyhUQ0tk1pjn5is3MqN+DRC6PYtG4A6WgT+1fc3omgZHuQFkUsxK76ZqUbGwBlKZCWYu86EQJ1PdODABuVQ9GmXnyRj3l9WP2tq2OIpcvasme5XJwYglX9QbL9kSifgkfvnkjnjgxiydOlAcQRF6/Hk9eUE+6plZrg3r3s4j2wHEnXgGlkjdKfFyup3j1cNTwxOdAWOnYuJwK728ePYFXf/7ntuZrCpLZUvMxQ7tGjSRWJmcifgmcA4lc3tquCSt2zamZpKVVA5SsMbPNupInb/w7Ks13rf1QFJZNvSL/wZvW4+/efS1WdZltvErI5ovaWrP5AiYX01olLyruRp18PTgR09oA6PnNl67Fmt4APv2jI2UTvM7NJeH1uLTDb7XoD/m0VsB12TUR0drApJK3GNZCtC/OFHmTjVeRDrAa4l0P/REfOEdVa167nJtL4is/P4Uih+EEKjNSuYJm10T9yiEhfTVdOfpPIJ7w07Es5AI3Ffm+kBdygePoxVjVXNdK/JILRQ7ki8YiX8rJW1fyvhqVPFCybGoNfKmkKyDhTdesNr1e61+jVvPjC2llAlOvUsmv7w/B53E15FDUUkrG+fkUdox0VV3n87jxx7dtxdGLcXzv2VLy+OxcEmt7g7YPGrlcTBuDWNnGwYqQz4OIz2NacMwns5SR70AcKfJ+yW2Y5hAbR5U2xuUi+tdcbozyL394WJtyLwY92KGykgfKWxukcgXDn1G8dRf3ZeXJA4Bc4KY9awS1GsJpEUqTaj9bY76rHiHydiOUdumqaFKmnS5V0ywetwtbV0Vsb75m5AK+9+x4WTUuEO8GdqyuFnkAeMPOYbz4qm789cPHtQNYZ2drd5+sRGy+1mPXAEo/pGmTMYDziVzNYTtE++FQkTdOfDR646h/Ga0NHj86jUeOTOMjt24CY/XNi03m8lr+2WisX+XAEIG4rRgqYWXXCKwy8kCpAjc7ECUuL3IY5ukzNea76hF2TaMjfJVNys7OKi+CemHdtjqKw1MxW2MCH9g3iY99dx9+fGCq6jqx6WpUyQPKidVPvmEbZuJZ/MMvTqNY5Dg3n6zZzqCS0Z4gPC5mywbTYzYGMCMXkMwVGvYumFg5HCnyZideF1L2+9bY4XKblGXzBXzqgUNYPxDCb79yA4Yifk14a1EocmTkYikn7ysf6wdUj/4TVIq8WRRR/5a8lidfmg5lUsnrLjd6d3U5lXyjfWHRqVGI/Pn5FMI+T9nvYdtwFIspWcvcWyEq/m/++7mq6w5MxDDSHbB8oXrJ2h68Ydcw7vnFKeyfWEJGLmJtDduskvdcfxX+9I5tdRc0QxHj1gYLdBCqY3GmyKsRysqqa67OvjW16LvMdsNffeIMzs6l8Bdv3A6vx1U2l7MWoi+NPicPlIt8epl2jfgdBSR3zc0+Ic5mffXTuYImokb7JNrGq51KXhX3njo9+Vpodk1atUcMWghs0zZfa1s2Iuv+zJl5nKgYH3loYgnbV1dvulby8du2olgEPvbdfQBqNyarZMdIF+562Vhd/wdQMvvTsWyV1TR3GWdMiPbAkSLvl9wocmin/gSTi2nTGN3lEPV74HW76qrkp5bS+NJjJ/G67UO4abNyUGekJ2C7kk9VdJgUwp3I6uwa2biSjwaU/3Ohll2jivz6gVDNzT6/xZxXzjnSckH7fkY95e1GKAFlA7QrIDXdrlEmMJWL6tZVETBWuyNlschxZCqGO3YNw+t24VvPnNeui2dknJ5NYqeJVaNnTW8Q77txTGtPsFItfLesCiNXKOJYRa8ekUwju6bzcKzIA0BGF9ubTWQxE8/i6uHGDTVgjKE/7NVGy9nh0z86giLn+JM3lDouj/YEMLWUsRXFFC0MKiv5WJldY5yuEUfcJ9RK3iwnH/R64JdcNTddAetKXnjwQpSNLJ1ShLK2yL/tJaN44o9vttWcrB6iOpEXE5gqNzpDPg/W9YdqxijPziWRzBVw06YBvH7nKnz/2XHt3Zd4gTDz4yv58M0b0ROUILkZVncHav+HBrBnrBcA8Ouz82WXU0uDzsWRIq9Nh9IJzxG198g2G2+V66E/4rPdbvj58wv44f4pfOiVG7Cmt1QpjvYEUShyXLSRlU+pveTFYaiov9qTN9t4FYNDZhM5pc2wRbzu47dtxftetrbmevwWG6/CgxeJDOtKvvZD0a22BW40kls5qr+UVjz3fJEb2iPbhqM1e9gIO2fb6ijee8NaxLN53P+C0q7poHrddoOMvBFdAQl/9dZd+O2bNmiN8ZrNaE8Qq7v8+FWFyFOb4c7FmSLvVYd560RFVFHbhhss8mGf7YHef/3wMfSHvbj7pvVll4/qRrbVIil6xauVvKjo9emalIldIwaHAMqLg5UV874b1+Ela3trrkeIs1GVLjx4kWs38uRFtd/o6rxexKlX0X3SKLK4bXUU4wtpLKXMJzkdmoxBcjNsHorgJWt7sHVVBN985hw45zg4sYShqK8uy/C2Havwsddtqf8HWgZ71vXi12fmy/a0FpK5pr3IEs3FkSJvNB3q8FQMq7v8Dc9Y94e9mEvWFvmnTs7iqZNz+N1XbdQOMgnE8GU7vry28apW8h61Cq3KyZs0kRL2jpkfXy9CnA0rebm8kjc6EFVPJd9MRJMyqxYC4uSrVTV/aHIJmwYj8HpcYIzhPTesxcGJGPaNL+HgxJJpPr6d2DPWi+l4VuupD4jTrtaFAdGeOFPkvdUHdI5MxbRGVY1E9K8pmpz4BJQNyM89dAyru/z4jeuvqrp+dbdS2dkR+YSwa3wlEdcPAykUOXL5YlWbYf1tgcaJvGUln6tdyWfqiFA2E9Gk7NxsEj6Py3BUoHgXaCbynHMcnoyVpWfecu0IQl437vnFKZyaSdj241vJdeuUd3C/OlOybOaTWfLjOxRHirzw5IWoZOQCTs0kG+7HA4pdky9ywxmhgkeOTOOFC4v4yKs3GYqZz6PMULUTo0xlxWi/kogrLYSVy81G/2m3VXP1jRP56v0PQUa248nbPwzVTKJqT/mzarLGqGIdiPgwEPGZbr5eimUxl8yViXzY58Gbrx3Bjw9cRJHb33RtJRsHwugOSmWbr/WOzSTaB0eLvBCZ45fiKBR5w/14oPaBqGKR4389fAzr+kN424tHTb+PmMtZi2ROVPJ6kS81KRPVc027po6e7FYIayxraddIZWvTowwMcbW8fa3w5M/PJy1bCOxYHcUL5xcNrxPiv71CyN97Q2kD26gxWbvhcjHsXtuLX59d0C5r9BkTYuVwpMiL6lJ0PhTJmmbYNWIQhVmM8oH9kzh6MY7/+prN8LjNf92jPUGML9ZTyevtmlJPebNe8vrbAg305IVdY1DJV6ZrjETeznzXlSAa8GAxlStrMWzELVsHcXo2ieMVh5wAZdOVserH2dXDUexe24OBiM92J8lWs2esB2dmk1ofmwUS+Y7FkSJfWckfnowh5HXjqt76Tg3aoT8i+tdUxyjlQhFf+OlxbF0VwR3qrFAzRnsCmFrMGDa10qMf4i3QV/K1Rb7RG6/mh6FEJR/xe+BxMcPpUNl8seVWDaD8PpK5ArL5omUl/7odq8AY8KP91X1pDk0uYawvZDhX9QvvfBG+dtfulr9jscse1Zffe3YBhSLHYlqm5mQdSuufXU3ALyKUQuTVTddmJAP6tdYG1ZX8958dx9m5FD722i0173u0J4h8kdfsK5/MloZ4C6J+jzaXMy2L0X/GG6/RBos8Yww+j8twzqt4kQ1IbqXVRBtX8vrfh9UEpsGIH3vGevHgQSORj5nu+6zpDWLXaPey17lS7FjdBb/kwq/OzGMhlQPnlJHvVBwp8vpKXjlmHm+KVQMA3QEJbhcz9OS/8ctz2DnShVuvHqz5fURWvpYvnzQ4zdpKuwYwn6mb1k2oCpqKfO35riuBPv9dq4XAG3YO4/ilBE5OlyybpZSM8YW0rb40nYDX48K1a3rw67PzdNq1w2n9s6sJlDz5AsYX0khk801J1gDKJlVvyFvVU342kcWRqRhu27HK1lv0kshb+/KpbKHKDoj4PMjIRciFounoP+22Da7kAdHa2ciuUS4LeN2mg1wy+faq5CU3w7DJBCnB7Zplc1G77NCUdZ/4TmTPul4cmYrhvHp2gES+M3GkyEtuFzwuhky+gMPqk68ZyRqB0UDvp0/NAQBevrHf1vcQvUlsVfK+clHUd6JMt6qSN9p4FVOfPC4EvB7Ttga1hnivBCJttKYnaLlBDiidGves7S3rFy9OVDulkgeA68Z6UeTAI0cuASCR71QcKfKAOgIwV8ThqThcDNiyqnGNySrpD3urRP7JEzOI+j22c9F+yY3BSO2svH6ItyCs9a+RS5W8icjvGIliy1AEm4ca9/vwe9yGEcqMrLQ8ZowhYDLIJSMXtYROKxF2zVU2W/revnMVjl2Ka10iD04sYVXUr7WfdgLXXtUNt4vhp4cVkSdPvjNp/bOrSfhUe+DwZAzrB8JNtQQGwuVNyjjnePLELF62ob+uxlJ2Wg7rR/8J9JW8dhjK5MTr2r4QHvqvN2HA4ETn5eKXXKYRSvFiE/R6tLXpabeNV7stfW/foaSlHlSr+UMVJ12dQMjnwY7VUa05WSMH7hArh2NFPuBVEh/NamegR+lEmdUaOp2dS2FyKYMbN9mzagSjPcGaYwD1o/8EpXbDcs3DUM3A5zHZeJVLw0v8klvz6PXk2iRC2RvyYk1vANevq92UDQBWdfmxe20PfnRgCulcAadmEo4TeaDUejji95TFdonOwbF/tYDkxsVYBhOL6ab68YBi12TzRa3X+5MnZwEAr7DpxwtGewKYXExbZuVT2UKVJ69vN5yWC/C4GLwrKJw+043XgpacUdI17VvJez0uPPFHt+D2GucZ9Ny+cxhHL8bx4MEpFHlpepSTEHl5smo6F0eL/L4LiwAa30O+kr5Q+YGoJ0/MYKQ7YJm3NmK0JwC5wLVThkYkc/kqT77crjHvQNkszCKUGd1aApLbeOM13x4Rysvh9TtXAQD+5pETAJy16SoQlTxtunYunfnssoFPcmt9Xho5DcqI0qlXZTbm06fm8PKN/XWfbqzVcjhfKJYN8RZEdBuvaZMh3s3EL7m1vvB69HaNmLtbSbukay6H4a4AXnxVN87Pp9AVkLQYrJPoDXmxfXW0bMgN0VmYjwbqcIS49IfrG9JwOWhNyuJZHJhYQjyTr9uPB8qz8qKC0iPaAoRMIpSJTF4dGLKyf1a/yYnXtFzK9BsdhuKct41dc7m8fucwnju/iG3D0Y5pWVAv//f919WMlRLti2P/ckLkm23VAKUmZbOJLJ5S/fgbN/TV/X1GRFZ+3riS10b/VWy8Sm4X/JIL8Wwe6Vze9CBUs/BJLmSMKvmcrpKX3MgXOWTdHFu5wFHkrR8YshyEh79z1Hl+vKAv7GvouQpiZXFuJa9aFs22agDlLS1jiif/zJk5bBuOXlZe2i+50R/2mdo1SYte8aK1QaoVdo1JuiYj6zx59WMqV0BXQBF1Mfy71aP/lsNIdwDfeP91jvTjCWfQuSVUDUR12OxkDaCM4OsJenFhIYXnzi3i5Zdh1QhGewKmMcrKId56In4PYi3eeNXPBAWqPXmgfIKUSOR0ciUPAK/cPKA1qiOIdqOzn10WCJ93pSqsvpAXjxy+hFyhiBvrjE7qGe0JmJ56FRHNygglUJoO1ZqNVxeKXLFf9KRzJb89qKvkBULwfR3syRNEu+NYkR/pDqA/7LN9gnG59Id9iGXy8LpduM5g09Qu4kCU0czYyiHeeqLqnNeUXN2lstmYjQDMyMWyCCVQPjgk2ybzXQnCyThW5N/3sjE89rFXrlgqQMQoX7K2Z1l2SSkrX9262Gj0nyDs82iV/ErbNaIS1/evyReKyBWKOrtGWbPodw/o7Jo2OPFKEE5lWc8uxtjbGWOHGGNFxtjuius+wRg7yRg7xhh73fKWWT8et6usR3izETHK5fjxgHXLYTH6rzJCCYjpUOrG60qna7TpUDorRk3b6NM1gLFdQ5U8QTSP5ZZQBwG8FcAv9BcyxrYBeBeA7QBuA/Blxpijn8li4205fjxgfSAqqbURNtp4lRBLK20NWnEYCijZL0DJlvF7yz35dK5647UdetcQhFNZlnnLOT8CwOgQyJ0Avs05zwI4wxg7CeA6AL9czv21M2/YOYx0roCdNlsLmyEqeaOETdJgiLcg4vdoJ0rNRv81C7/BnFf96D9lTarIy+TJE8RK0qwSagTABd3X4+plVTDG7maM7WWM7Z2ZmWnScprPWH8IH3vdlrpaCxuhZOW9hnZNMpevGuItiOisqVZV8nq7Jq1ZMcpajTZeSxFKEnmCaBY1Sz7G2CMAVhlc9UnO+X1m/83gMsPWipzzewDcAwC7d+82b794BTHSEzS0a4xG/wlEawNgZdsMA3qRL1Xy6YoxhFYRyk7PyRNEO1NT5Dnnr76M7zsOYI3u61EAk5fxfa5I1vYG8dz5harLlSHexgIe1Yl8K3LyQIUnX2HXaHN3yzZnya4hiGbTrBLqfgDvYoz5GGPrAGwC8Ksm3Zfj2DAQxsRiuqqhVzJb3WZYoLdrVrx3jcegkpfLN159HhdczMSu6eC2BgTR7iw3QvkWxtg4gJcC+BFj7CEA4JwfAvAdAIcB/ATAhznn1c1NCEM2DobBOXB6NlF2eSpXPTBE0Fq7xiBCWWHXKHNey9sNl068kl1DEM1iuemaewHca3LdpwF8ejnf/0plw6BySvfkdALbddOG7Fby7XDitdKuAZTUT6rsxCtFKAmi2dCzqw1Z1x+CiwGnZpJll6dyBcODUEB5Jd+KLpSAsV2jf1cR8LrKRgBm5QJ8Hpdj+7ATRDtAIt+G+DxuXNUbxKnpcrvGaPSfQJ+6aUU/eaAiQpmr3lQNSp4qu4Y2XQmiuZDItykbBsI4NVMh8gZDvAV+yQ2vuzQ0eyURdot+BGDlYShA2YRNVWy8UnySIJoLPcPalI2DYZyeTaKg60Zp5ckDJctmpT15xhh8FSMA03IBbheD5C5ZMcGKgd+ZPFXyBNFsSOTblA0DYeTyRVyYV06+5gtFZPPVQ7z1RPweMNaaw0X+CgFP55QOlHq/PVhVyXfuEG+C6BRI5NuUDYNhANAsG7Mh3noifqlKWFcKv+Sq2nitrNL93soIZZHikwTRZOgZ1qZsHFBE/qS6+ZrU2gxbV/Ir7ccL/JK7LEKpzHctf3gFJXfV0BCq5AmiuZDItyldQQn9YZ9O5EWbYatK3rPiB6EElcO807lCVconQJU8Qaw4K7tDR9TFxsFQya6xGP0neO8NazFpMgS82RjZNUYiX+nJD0RoADZBNBMS+TZm42AY978wCc55qZK38ORfsWlgpZZWhc/jrmpQVunJByQ3cvkiCkUOt4shmy9SuoYgmgy9V25jNgyEEcvkMZPIapW8WavhVuOrqOQVT75cwIMVg0OUdA09BAmimdAzrI3ZKBI200kktKlQ7Sny1RFKA7tGm/Oq/CwZuUCePEE0mfZUDAJASeRPziTgUSdOWUUoW4lfcpedeDX25JWHWyan3C6bL1K6hiCaDJVRbcyqqB8hrxunphO6+a7t+brs97jKT7PKBa2XvECbDiXnwTmn3jUEsQKQyLcxjDFsGFR62IhUSqhFEcla1GPXpHMFyAWOIqfRfwTRbOgZ1uZsHAjj5HQCyVwePo8LHoMh3u2Az+PS7BrOuWmEElBEnkb/EcTK0J6KQWhsGAxjaimDmVjW8rRrqxGVPOccuUIRRV49oUqr5OWCbioUiTxBNBMS+TZng9reYP/EUstaFtjBL7lQ5IBc4NrGamWVrnnyuQKyMk2FIoiVgJ5hbc5GdRTgqZmE5WnXVqMfAWg0+k9/m7Rc0A5OkV1DEM2FRL7NWdsXgsfFwLn1addWI2yXjKwT+coGZXpPXq3k6TAUQTQXeoa1OZLbhbV9QQDte9oVKIl1Vi5qnSbNNl5TuZInT5U8QTQXEvkOQPjy7ezJi0o+q7NrqvrJe/Qbr8a+PUEQjYVEvgMQJ1/b2pP3iGHeRcP5rgDgcjEEJDfSubyukqeHIEE0E3qGdQBC5NvZk/frPXlh1xi88xA95SknTxArA4l8ByDsmrau5DWRL5qma8RlKd3GK0UoCaK50DOsA9gwGIbkZugNeVu9FFOE7aJP1xhV6QGvcmiKIpQEsTK0b2lIaIR9Htz7uzdirD/U6qWYos/Ja568gV0T9JZX8tSFkiCaC4l8h7BjpKvVS7DEZyNCCSgvBumcvq0BvZkkiGZCzzCiIRideDWyYoLqxmtWLoAx8uQJotnQM4xoCMJ2ERuvXo8LbnXQiR5t4zVfhM/jAmPVtyEIonGQyBMNwafbeM0Y9JIXBLwlu8ZHfjxBNB0SeaIhlDz5gmEveUHJrinSQSiCWAHoWUY0BMYYfB4XMvkiMnLRMFkDQD3xqhyGovgkQTSfZYk8Y+xzjLGjjLH9jLF7GWPduus+wRg7yRg7xhh73bJXSrQ9fsmtVfJmAh7wepBWT8VSfJIgms9yK/mfAtjBOd8F4DiATwAAY2wbgHcB2A7gNgBfZozRM9rh+CWX1rsmYGLFCBtnMS2TXUMQK8CynmWc84c553n1y38HMKp+fieAb3POs5zzMwBOArhuOfdFtD9+ya1EKHMFU7tGdNJcTOVo9B9BrACNLKXeD+BB9fMRABd0142rl1XBGLubMbaXMbZ3ZmamgcshVhq/x621NTBN16iXzydlysgTxApQ88QrY+wRAKsMrvok5/w+9TafBJAH8C3x3wxuz42+P+f8HgD3AMDu3bsNb0N0BsKusfbkS5U8bbwSRPOpKfKc81dbXc8YuwvAHQBu5ZwLkR4HsEZ3s1EAk5e7SKIz8KmVvGVOXr08X+Qk8gSxAiw3XXMbgD8G8CbOeUp31f0A3sUY8zHG1gHYBOBXy7kvov3xSS5k80olX8uTB2i+K0GsBMttUPYlAD4AP1WPp/875/xDnPNDjLHvADgMxcb5MOe8sMz7Itocv+TGTDxr7cnrRZ4qeYJoOssSec75RovrPg3g08v5/kRn4ZdUu0Yu1vTkldtTJU8QzYaeZUTD8HtcWEzLAIx7yQNAUCrVFdS7hiCaD4k80TD8khtLQuRNKnm/16W7PT38CKLZ0LOMaBg+jwsiX2XeoKxUyZMnTxDNh0SeaBh60fZbNCgT0IlXgmg+JPJEw9DbL2aVvNvF4FWjkxShJIjmQ88yomHoK3kzkddfR3YNQTQfEnmiYejtl4DX/KElDkSRyBNE8yGRJxqG3n6xEnARr6QGZQTRfOhZRjQMH9k1BNF2kMgTDUNfyZsdhgL0dg09/Aii2dCzjGgYdjde/VTJE8SKQSJPNIyynLyFgGuVPLU1IIimQyJPNAxhvzBmvala8uTp4UcQzYaeZUTDENV7QHJDbT1tSEBtbUANygii+ZDIEw1DVO9Wfrz+eh9V8gTRdOhZRjQMuxuqfWEvgl435eQJYgVY7mQogtAQG6lW8UkAuOtlY3jttiFLS4cgiMZApRTRMIT9UsuuCfs82DQUWYklEcQVD4k80TB8HhcYqy3yBEGsHCTyRMNgjMHncZn2kicIYuUhkScais/jRoBSMwTRNtCzkWgoAclN7QoIoo2gdA3RUP7gNZsx1h9q9TIIglAhkScayjv2rGn1EgiC0EF2DUEQhIMhkScIgnAwJPIEQRAOhkSeIAjCwZDIEwRBOBgSeYIgCAdDIk8QBOFgSOQJgiAcDOOct3oNGoyxGQDnlvEt+gHMNmg5K0mnrhugtbcKWvvK087rXss5HzC6oq1EfrkwxvZyzne3eh310qnrBmjtrYLWvvJ06rrJriEIgnAwJPIEQRAOxmkif0+rF3CZdOq6AVp7q6C1rzwduW5HefIEQRBEOU6r5AmCIAgdJPIEQRAOxhEizxi7jTF2jDF2kjH28VavxwrG2D8xxqYZYwd1l/Uyxn7KGDuhfuxp5RrNYIytYYw9zhg7whg7xBj7iHp5W6+fMeZnjP2KMbZPXfen1Mvbet16GGNuxtjzjLEfql93xNoZY2cZYwcYYy8wxvaql3XK2rsZY99jjB1VH/Mv7ZS16+l4kWeMuQH8PYDbAWwD8G7G2LbWrsqSrwO4reKyjwN4lHO+CcCj6tftSB7ARznnVwO4AcCH1d91u68/C+AWzvk1AF4E4DbG2A1o/3Xr+QiAI7qvO2ntN3POX6TLmHfK2v8WwE8451sBXAPl998pay/BOe/ofwBeCuAh3defAPCJVq+rxprHABzUfX0MwLD6+TCAY61eo82f4z4Ar+mk9QMIAngOwPWdsm4Ao1AE5RYAP+ykxwyAswD6Ky5r+7UDiAI4AzWc0klrr/zX8ZU8gBEAF3Rfj6uXdRJDnPMpAFA/DrZ4PTVhjI0BuBbAM+iA9at2xwsApgH8lHPeEetW+RsAfwSgqLusU9bOATzMGHuWMXa3elknrH09gBkA/0e1yb7KGAuhM9ZehhNEnhlcRrnQJsIYCwP4PoDf55zHWr0eO3DOC5zzF0Gpiq9jjO1o8ZJswRi7A8A05/zZVq/lMrmRc/5iKHbqhxljN7V6QTbxAHgxgP/NOb8WQBKdYM0Y4ASRHwewRvf1KIDJFq3lcrnEGBsGAPXjdIvXYwpjTIIi8N/inP+benHHrJ9zvgjgZ1D2RTph3TcCeBNj7CyAbwO4hTH2TXTG2sE5n1Q/TgO4F8B16Iy1jwMYV9/xAcD3oIh+J6y9DCeI/K8BbGKMrWOMeQG8C8D9LV5TvdwP4C7187ugeN1tB2OMAfgagCOc88/rrmrr9TPGBhhj3ernAQCvBnAUbb5uAOCcf4JzPso5H4Py2H6Mc/5edMDaGWMhxlhEfA7gtQAOogPWzjm/COACY2yLetGtAA6jA9ZeRas3BRq0SfJ6AMcBnALwyVavp8Za/xXAFAAZSrXwAQB9UDbWTqgfe1u9TpO1vxyKFbYfwAvqv9e3+/oB7ALwvLrugwD+TL28rddt8HO8CqWN17ZfOxRfe5/675B4bnbC2tV1vgjAXvVx8wMAPZ2ydv0/amtAEAThYJxg1xAEQRAmkMgTBEE4GBJ5giAIB0MiTxAE4WBI5AmCIBwMiTxBEISDIZEnCIJwMP8fgGIeqbYsOQIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_extrapolated_country(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "fa728248-8a71-47cb-8796-b9aa1b8f3343",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_extrapolated_country(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2154a1e0-1a54-4915-82d2-0b7b80a873d0",
+   "metadata": {},
+   "source": [
+    "#### We've done some data science!\n",
+    "\n",
+    "As you can see above, again, the data is obscured by noise, but the trends / modeling move in the expected direction for each set of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "436d3908-115d-4206-8ddf-7363aa01093d",
+   "metadata": {},
+   "source": [
+    "### Congratulations!\n",
+    "\n",
+    "We've done data science work with a dataset on your node - a dataset that's protected by privacy enhancing technologies!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f121c968-5dc9-4e37-96c2-b0b0c0c4d724",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 9e3edda..b01d85f 100644
--- a/README.md
+++ b/README.md
@@ -9,3 +9,5 @@ If you have technical issues with the website itself, or want to provide feedbac
 Notebooks, code, and other resources associated with our courses are kept on separate branches in the repo. Here's a list for easy access:
 
 [Foundations of Private Computation](https://github.com/OpenMined/courses/tree/foundations-of-private-computation)
+
+[Introduction to Remote Data Science](https://github.com/OpenMined/courses/tree/introduction-to-remote-data-science)
diff --git a/dataset/L3_data.csv b/dataset/L3_data.csv
new file mode 100644
index 0000000..cc86083
--- /dev/null
+++ b/dataset/L3_data.csv
@@ -0,0 +1,55 @@
+0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174
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diff --git a/dataset/L3_raw_data.csv b/dataset/L3_raw_data.csv
new file mode 100644
index 0000000..f4a565b
--- /dev/null
+++ b/dataset/L3_raw_data.csv
@@ -0,0 +1,55 @@
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