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Welcome to Introduction to Earth Data Analytics! This is the first course of three in the Earth Data Analytics -- Foundations professional graduate certificate program. Over the course of the semester, you will learn the basics of writing open, reproducible earth data science workflows using the Python programming language.
- One-click join: https://cuboulder.zoom.us/j/91586919508?pwd=V0wvSjNtKzZ6TW0zL2FQYTRUYkhOZz09)
- Meeting ID: 915 8691 9508
- Passcode: 103322
This class is fully remote, with synchronous (e.g. interactive and held at a particular time) discussion sections.
Section times:
- Wednesdays 1:00-2:30 US Mountain Time
- Wednesdays 4:00-5:30 US Mountain Time
- Thursdays 1:00-2:30 US Mountain Time
- Thursdays 4:00-5:30 US Mountain Time
- Asynchronous
Each week you will be required to either:
- attend one 90 minute synchronous remote section
- or complete an additional asynchronous post on GitHub by replying to the relevant
discussionIssue.
If you are unable to attend the section you registered for, you may attend another one, provided you contact the teaching team. The sections will be small (4-8 students) and we need to ensure that there is enough attendance at each one to have a good discussion.
Email: [email protected]
I work Tuesday-Saturday so that I can meet with students on weekends. I will do my best to respond to emails by my next working day.
Office Hours:
- Tuesdays 1-3pm in SEEC S346 (Earthlab Education Director office) or virtually on zoom
- Sunday 3-5pm US Mountain Time virtually on zoom only
- Schedule an appointment on zoom
Make sure that you have access to the following items. More details about how to complete these steps are available below.
- ✅ Working computer with a reliable internet connection
- ✅ University of Colorado email/identikey
- ✅ Online Earth Data Science Textbook
- ✅ GitHub
If you are reading this page, you probably already have an Identikey and GitHub account! You may also wish to check that you have access to the following before the course begins:
- ✅ Customize your University of Colorado Zoom profile
- ✅ Canvas Learning Management System
Why do I need this? Access all course materials online, including live discussion sessions, video content, assignments, discussions, and our custom Python programming environment.
If your internet access is limited, you may wish to set up your own coding environment on your computer so you won't need consistent internet. Follow the instructions on Setting up Git, Bash, and Miniconda on your computer to install the necessary tools for your operating system (Windows, Mac, Linux).
Why do I need this? Use your University of Colorado email to contact the teaching team and access University of Colorado services like Zoom and Canvas.
If your email is not personal in nature, but rather a question about the homework, issues with the course website, or issues with Python, please:
- See if the question has already been asked on GitHub.
- If the question hasn’t already been asked, please [post your question](###
Why do I need this? The textbook contains lessons related to each week's topic.
The textbook for this course - online textbook for Intro to Earth Data Science is free and available for you to use at any time.
Why do I need this? You will need a GitHub account to access course materials. Use GitHub to access, complete, and submit your assignments and discussion posts.
We will be using the class GitHub repository to host class discussions and technical Q&A.
It is important that you follow the course guidelines when posting questions to GitHub.
Do:
- Post questions that you have about homework assignments.
- Post issues that you find with the website – typos, errors, points of confusion.
- Answer other questions if you can (you will receive participation points for posting, so use it!)
- Upvote questions that you have as well or that you think are written well.
- Be respectful of your peers. All questions related to data and earth analytics are good questions.
Do not:
- Post any content that is in any way offensive or that violates University codes of conduct (see links below).
- Post explicit solutions to homework assignments (help each other out but don’t post the answer).
- Post an entire homework document - rather post a small snippet of code that is causing problems and the associated code error.
When posting questions about coding issues, please do your best to provide an example of the specific code error that you are encountering. An example of an appropriate way to post code errors is provided below.
The Code:
import pandas as pd
pd.read_cs("filename")returns this error:
AttributeError: module 'pandas' has no attribute 'read_cs'
In the example above, the fix would be to make sure your function name is correct (code should be read_csv, rather than read_cs).
There are several ways that you can get help when you get stuck in this class.
- Ask questions during hands-on exercises in class.
- Attend office hours or make an appointment.
- Post your question on GitHUB.
Zoom info: Zoom information
On zoom, it will be important to participate actively each week either by speaking or typing in the chat. We understand you may not be able to have video on all the time due to internet limitations. Please use a profile picture showing your face and change your screen name to the name and pronouns you would like to use in class so that we can identify you even if the video feed needs to be turned off. Technical details about how to change identifying information are listed below.
You can access Zoom via any of these methods:
- On your computer
- Call (646) 568-7788 and enter the meeting id and password
- Find an international number at https://zoom.us/u/bFvTUzUz
To update name, pronouns, and profile picture:
- If you are not logged in, click the ‘SSO’ button, enter ‘cuboulder’ as the domain, and sign in with your IdentiKey
- Click on the profile photo box in the upper left to change your profile picture to one showing your face.
- Edit your Display Name and Pronouns
- Select either ‘Always share in meetings and webinars’ or ‘Ask me every time after joining meetings and webinars’ for ‘How would you like to share your pronouns.
If you need assistance with running Zoom, please contact IT.
Why do I need this? If you would like to see your current calculated grade, it will be available on Canvas as we assess your work throughout the semester.
You can log into the Canvas page for this course with your Identikey.
This introductory, multidisciplinary course will provide core scientific programming skills required to efficiently work with a suite of earth systems data in Python.
In this course, you will learn how to:
- Write modular code using Python to process and visualize different types of data.
- Use version control to backup and track changes to code.
- Use Shell to access Git (version control) at the command line and to create and organize directories.
- How to work collaboratively with colleagues using Git and GitHub.
This course is technical. You will use the Python programming environment and the Jupyter Notebook interface to work with data. You will code every day! No programming experience is required to take this course.
At the end of this course, you will learn to program in Python and be able to:
- Write code to process data using the Python programming language and Jupyter notebooks.
- Design and write a basic algorithm to solve a scientific challenge or process data.
- Write modular efficient code in Python using functions and loops.
- Write pseudocode or flow diagrams to organize and outline workflows.
You will also learn principles and tools for developing open reproducible science workflows, and be able to:
- Organize documents and directories using sound data management approaches.
- Write well documented, modular code to support open science workflows.
- Use Git/GitHub.com to track chances in your code and collaborate with interdisciplinary colleagues.
- Use Shell to interact with Git and to create and interact with directories and files.
Course participation will be evaluated as a part of this course. Your course participation grade is calculated using a combination of:
- Answering questions posted by students on GitHub
- Responding to questions on GitHub
- Completing peer reviews of other students' code
- Identifying issues with the course website or materials and posting these issues on GitHub
Each week there will be an assignment due by Tuesday evening. There will be a GitHub Milestone with the assignment link . Follow the instructions for submitting your assignment.
Each week you will need to contribute to a discussion post on GitHub by Tuesday evening. If you a zoom discussion section you will complete the discussion during class.
Assignments that you complete will provide you with the skills and resources needed to complete the final assignment. Graduate students will be assigned a more intensive notebook to complete by default. Graduate students may also choose to complete a final project in which they retrieve their own data. If you plan to bring your own data, you will still need to demonstrate the key skills of writing functions, for loops, and plotting.
Important: Please note that the course schedule and content on Canvas is subject to change. This course content schedule is not designed as a contract. Rather, it is an overview guide to the materials that you will review during the semester. Please make sure to attend class and/or check Canvas regularly for final information on deadlines and course content.
All grading for this course will follow the CU grading policies.
Late assignments will cost 10% of your score on that assignment unless you discuss this with the teaching team in advance of the due date. We will happily make accommodations for late work from any student, regardless of the circumstances. If we think you are getting too far behind to reasonable finish the course on time, however, we will ask you to meet with us to come up with a plan and get the support you need to finish.
Grades are calculated using the following percentages:
- Weekly Assignments 50%
- Weekly Discussions 15%
- Final Assignment or Project 20%
- Participation 15%
Please use the table below to understand how your numeric grade listed on Canvas relates to your final letter grade in this course.
A | 93-100 A- | 90-92 B+ | 87-89 B | 83-86 B- | 80-82 C+ | 77-79 C | 73-76 C- | 70-72 D+ | 67-69 D | 63-66 D- | 60-62 F | 0-59
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