FinalProject_Group050_WI24.ipynb

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   "source": [
    "# COGS 108 - Final Project (change this to your project's title)\n",
    "\n",
    "# Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private\n",
    "\n",
    "# Names\n",
    "\n",
    "Inna Amogolonova\\\n",
    "Dawson Tam\\\n",
    "Kate Zhou\\\n",
    "Tyler May\\\n",
    "Charvi Shukla\n",
    "\n",
    "# Abstract\n",
    "\n",
    "\n",
    "As a team of STEM students, we were all very curious about what skills and experiences we need to guarantee a job in data science. We were also wondering if crucial workforce skills can be obtained through means other than college: through MOOCs (Massive Open Online Courses) platforms, Coursera in this case. Therefore, we looked into Coursera’s tech-related courses to see if online classes can cover the most prevalent skills required by the data science job postings.\n",
    "\n",
    "After posing our research question, we found applicable datasets on Kaggle: one detailing data science job listings from LinkedIn and another one that had a large collection of Coursera courses, both from 2024. We then went through cleaning the two datasets to contain standardized relevant variables and went through a series of exploratory data analyses (EDA) to closely investigate our data. We cross referenced the two datasets by using the derived top five skills from job listings to visualize patterns in courses that cover a specific skill among all technical classes on the platform. Some interesting correlations were extracted from EDA: for example, python has consistently come up at the top of relevancy, engagement and prevalence through our analysis. Additionally, we analyzed how the top five in-demand skills were distributed by category of class on Coursera (tagged as “data science”, “computer science”, “math” or “IT”) and how the top five skills were related in engagement based on reviews and ratings for the courses. \n",
    "\n",
    "After doing a significance testing, by conducting a one-tailed z-test, we failed to reject the null hypothesis that less than or equal to 50% of the tech-related courses that Coursera offer to teach the top at least 1 of the top 5 skills demanded by the jobs dataset. We also explored further to find that there is a statistically significant association between the category of a course (tagged by “data science”, “computer science”, “IT”, or “math”) and the inclusion of the top skills which implies that the distribution of skills across the different course categories is not random, and certain skills are more likely to be taught in certain categories.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the degree of alignment between the skills demanded by the Data Science job postings on Linkedin (2024) in the United States, and the educational content of technology-related courses offered on Coursera, measured by the significance of correlation between top 5 data science skills listed in job postings and the frequency of these skills being taught in Coursera's technology courses?** \n",
    "\n",
    "\n",
    "_Note: technology-related courses are defined as courses from Data Science, Computer Science, Mathematics and IT categories._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are approaching graduation, we as undergraduate students are concerned about finding a job after college. Many students worry if the skills and experiences gained in college will be enough to find a job in their desired field. In addition to that, people without a technical degree wonder if it’s possible to learn the required skills through means other than college. According to a blog by Rice University <a name=\"cite_ref-1\"></a>[<sup>1</sup>](#cite_note-1), both technical and non-technical skills are crucial for data scientists in the modern world. For technical skills the most popular ones are advanced math (calculus, linear algebra), programming and machine learning. This begs the question: is it possible to learn those skills independently through online platforms in order to land a job in the field?. \n",
    "\n",
    "According to this article <a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2), \"many [tech] companies have been de-prioritizing their degree requirements, especially if candidates can demonstrate they have the necessary skills to thrive in the role\". With the huge rise of MOOCs (Massive Open Online Courses) over the past few years, it might be possible to learn the necessary skills online in order to get the desired job in the field. \"MOOCs have the potential to be as effective as classroom learning\", according to a meta-analysis of effectiveness of online education <a name=\"cite_ref-3\"></a>[<sup>3</sup>](#cite_note-3), but best results come from blending classroom education with MOOCs. Generally MOOCs are effective for learners because they allow them to access a massive amount of new skills and often are free or much cheaper than college classes are. The rise of MOOCs since 2012 has created an enticing avenue for acquiring education and work-force skills but one that needs further research on how the quality of knowledge compares to more traditional methods. Nonetheless, having those resources might be helpful when looking to apply for jobs, especially in technical fields like data science. In this project we will explore how accessible relevant courses are by looking at how many Coursera data science courses align with current data science job market skill demands.\n",
    "\n",
    "\n",
    "1. <a name=\"cite_note-1\"></a> [^](#cite_ref-1) (22 Dec 2022) 12 Essential Skills Required for Data Scientists in 2023. *RICE University*. https://csweb.rice.edu/academics/graduate-programs/online-mds/blog/data-science-skills\n",
    "2. <a name=\"cite_note-2\"></a> [^](#cite_ref-2) Brian T. Horowitz (28 Aug 2023) Do You Need a Four-Year Degree to Get a Job in Tech? *DICE*. https://www.dice.com/career-advice/do-you-need-a-four-year-degree-to-get-a-job-in-tech\n",
    "3. <a name=\"cite_note-3\"></a> [^](#cite_ref-3) Justin M. Weinhardt, Traci Sitzmann (June 2019) Revolutionizing training and education? Three questions regarding massive open online courses (MOOCs). *Human Resource Management Review*. https://www.sciencedirect.com/science/article/abs/pii/S1053482218303814"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis:** \\\n",
    "We predict that there is a significant correlation between the top five most prevalent skills required for data science jobs (from Linkedin) in 2024 and the educational content of Coursera's tech related courses, with at least 60% of Coursera's tech related (Data Science, Computer Science, IT and Math) courses covering these skills.\n",
    "\n",
    "**Reasoning:** \\\n",
    "Big online educational companies such as Coursera actively keep track of skills that are heavily demanded in industry, and add new courses that train the workforce, and fulfill this high demand. Furthermore, the increase in Industry-academia partnerships (IBM and Coursera, Google and Udacity, AWS and edX), will also contribute to a greater number of courses that align with skill requirements in job listings. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "- Dataset #1\n",
    "  - **Dataset Name:** Data Science Job Postings & Skills (2024)\n",
    "  - **Link to the dataset:** https://www.kaggle.com/datasets/asaniczka/data-science-job-postings-and-skills\n",
    "  - **Number of observations:** 12217 observations\n",
    "  - **Number of variables:** 16 variables \n",
    "- Dataset #2 \n",
    "  - **Dataset Name:** Coursera Dataset\n",
    "  - **Link to the dataset:** https://www.kaggle.com/datasets/elvinrustam/coursera-dataset\n",
    "  - **Number of observations:** 8369 observations (before cleaning)\n",
    "  - **Number of variables:** 12 variables\n",
    "\n",
    "\n",
    "### Dataset #1: Data Science Job Postings & Skills (2024) \n",
    "This is a 20 megabyte dataset consisting of data on data science job listings from Linkedin. Here are some useful variables that we will be utilizing:\n",
    "- `Job Title`: String, title of the job posting\n",
    "- `Company`: String, name of the company offering a job\n",
    "- `Job Level`: String, level of job (associate, junior, mid-senior, etc.)\n",
    "- `Search Country`: String, country where the job is searched\n",
    "- `Job Summary`: String, job summary\n",
    "- `Job Skills`: String, skills extracted using AI from the job description\n",
    "\n",
    "**Cleaning process:** \n",
    "\n",
    "We first extracted all useful columns from the three `.csv` files containing `job_postings`, `summaries` and `skills`. Each of these contained a column for a `job_link` which was used to join the three datasets. We also only kept jobs from the United States, which is what the `country` series was used for. We checked for rows containing null values, but there were none in the dataset. We standardized the column names (so that there are no trailing white spaces and uppercase characters). The `skill` column entries were split into arrays based on a comma delimiter, transforming into a string list of skills. Finally, we wrote a standardize function to standardize the text in the `summary` column. We converted text to lowercase, replaced new lines and tabs with spaces, and removed special characters while preserving basic punctuation - this is essential for doing any form of text analysis.\n",
    "\n",
    "**Concepts:** \n",
    "\n",
    "The `skill` series will be useful for extracting the top five skills in the data science field. Information about what skills are currently trending in the Data Science industry can also be extracted from the `summary` column. The `country` series was used to filter out only the United States positions. The `job_level` can be used towards further research, to see if there are specific skills that \"medium\" or \"senior\" level jobs require. \n",
    "\n",
    "### Dataset #2: Coursera Dataset \n",
    "This is a 4 megabyte dataset of Coursera's course offerings. Here are some useful variables that we will be utilizing:\n",
    "- `Course Title`: String, indicating the name of the course\n",
    "- `Rating`: Float, average rating provided by users that have completed the course.\n",
    "- `Level` String, describing the complexity of the course\n",
    "- `Skill gain`: String, detailing specific skills acquired through the course\n",
    "- `Keyword`: String, a tag used to classify courses based on themes or areas of interest. Eg: \"Arts and Humanities\"\n",
    "- `Number of Reviews`: int, count of number of users that have submitted a review for the course.\n",
    "\n",
    "**Cleaning process:**\n",
    "\n",
    "We first selected all relevant columns from the dataset, and checked if any rows have null values. We removed rows containing non-ASCII characters since they indicate that the entries are in languages other than English or with special characters. Columns were renamed to have standardized names. We then standardized the `level` column to have standardized values such as 'beginner', 'intermediate', 'advanced', and 'N/A' for not specified. Entries in the `rating` and `num_review` columns were converted to numeric types to ensure that only quantitative data was included. The dataset was then filtered to remove courses not related to technology (removed humanities, health, business, etc. related courses). Then, we standardized the keywords to have shorter acronyms and removed white spaces. Similar to the Jobs dataset, the `skill` column entries, initially presented as comma-separated strings, were split into arrays. Finally `jobs_data` was sorted by the number of reviews and then by rating, prioritizing courses with higher engagement and potentially higher relevance or quality. \n",
    "\n",
    "**Concepts:**\n",
    "\n",
    "The `num_reviews` variable and `rating` proxies for engagement and popularity of a course. The `skills` column, containing multiple arrays of skills can be used to determine popularity and availability of skills. The `keyword` variable can be used to see what department offers the most relevant skills needed as a qualification for a Data Science job in 2024.\n",
    "\n",
    "\n",
    "**Combining Datasets:**\n",
    "\n",
    "We combined multiple datasets to form our `jobs_data` dataset by using the `.join()` method. Since the datasets correlated with each other by a unique link, this was a relatively straightforward task. We will cross reference our second dataset (Coursera dataset) with our first dataset (Jobs dataset) by creating a count of how many relevant skills courses there are on Coursera. First we will determine what are the five most prevalent skills in demand for jobs and then we will see how it aligns to the patterns of courses that cover those skills among all technical courses on the platform. "
   ]
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  {
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #1: Data Science Job Postings & Skills (2024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all CSV files in the Job postings dataset\n",
    "job_posting = pd.read_csv('job-postings/job_postings.csv')   \n",
    "job_summaries = pd.read_csv('job-postings/job_summary.csv')   \n",
    "job_skills = pd.read_csv('job-postings/job_skills.csv')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <th></th>\n",
       "      <th>job_title</th>\n",
       "      <th>company</th>\n",
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       "      <td>Mid senior</td>\n",
       "      <td>United States</td>\n",
       "      <td>about the role please note review of applicant...</td>\n",
       "      <td>[investigation, antimoney laundering, fraud, b...</td>\n",
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       "    <tr>\n",
       "      <th>12214</th>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>Highnote</td>\n",
       "      <td>Mid senior</td>\n",
       "      <td>United States</td>\n",
       "      <td>about highnote founded in 2020 by a team of le...</td>\n",
       "      <td>[data science, quantitative modeling, sql, dat...</td>\n",
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       "      <th>12215</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>CompSource Mutual Insurance Company</td>\n",
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       "      <td>United States</td>\n",
       "      <td>are you an experienced data engineer in oklaho...</td>\n",
       "      <td>[data engineering, data quality, sql, python, ...</td>\n",
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       "      <td>Community Health Systems</td>\n",
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       "      <td>job description weekend night shift position f...</td>\n",
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       "                                          job_title  \\\n",
       "0                  Senior Machine Learning Engineer   \n",
       "1      Principal Software Engineer, ML Accelerators   \n",
       "2              Senior ETL Data Warehouse Specialist   \n",
       "3       Senior Data Warehouse Developer / Architect   \n",
       "4                                Lead Data Engineer   \n",
       "...                                             ...   \n",
       "12210                      Data Entry Clerk - Excel   \n",
       "12213  Corporate AML Alert Investigation Specialist   \n",
       "12214                         Senior Data Scientist   \n",
       "12215                          Senior Data Engineer   \n",
       "12216              Medical Technologist, MLS or MLT   \n",
       "\n",
       "                                   company   job_level        country  \\\n",
       "0                        Jobs for Humanity  Mid senior  United States   \n",
       "1                                   Aurora  Mid senior  United States   \n",
       "2                       Adame Services LLC   Associate  United States   \n",
       "3                         Morph Enterprise  Mid senior  United States   \n",
       "4                                     Dice  Mid senior  United States   \n",
       "...                                    ...         ...            ...   \n",
       "12210                   Corestaff Services   Associate  United States   \n",
       "12213                Glacier Bancorp, Inc.  Mid senior  United States   \n",
       "12214                             Highnote  Mid senior  United States   \n",
       "12215  CompSource Mutual Insurance Company  Mid senior  United States   \n",
       "12216             Community Health Systems  Mid senior  United States   \n",
       "\n",
       "                                                 summary  \\\n",
       "0      company description jobs for humanity is partn...   \n",
       "1      who we are aurora nasdaq aur is delivering the...   \n",
       "2      location new york city, ny position summary ou...   \n",
       "3      responsibilities candidate must have significa...   \n",
       "4      dice is the leading career destination for tec...   \n",
       "...                                                  ...   \n",
       "12210  benefits  medical, dental, and vision coverage...   \n",
       "12213  about the role please note review of applicant...   \n",
       "12214  about highnote founded in 2020 by a team of le...   \n",
       "12215  are you an experienced data engineer in oklaho...   \n",
       "12216  job description weekend night shift position f...   \n",
       "\n",
       "                                                   skill  \n",
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       "4      [java, scala, python, rdbms, nosql, redshift, ...  \n",
       "...                                                  ...  \n",
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       "12213  [investigation, antimoney laundering, fraud, b...  \n",
       "12214  [data science, quantitative modeling, sql, dat...  \n",
       "12215  [data engineering, data quality, sql, python, ...  \n",
       "12216  [medical technology, mls, microbiology, clinic...  \n",
       "\n",
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   ],
   "source": [
    "# Get relevant columns from the job postings csv file\n",
    "job_desciptors = ['job_link','job_title', 'company', 'job_level', 'search_country']\n",
    "df1 = job_posting[job_desciptors]\n",
    "\n",
    "# Get relevant columns from the summaries csv file\n",
    "df2 = job_summaries[\"job_summary\"]\n",
    "\n",
    "# Get relevant columns from the skills csv file\n",
    "df3 = job_skills[\"job_skills\"]\n",
    "\n",
    "# Set 'job_link' as the index if it's not already, for the purpose of merging\n",
    "df1.set_index('job_link', inplace=True)\n",
    "df2 = job_summaries.set_index('job_link')\n",
    "df3 = job_skills.set_index('job_link')\n",
    "\n",
    "# Merge the three datasets together\n",
    "jobs_data = df1.join([df2, df3])\n",
    "jobs_data.reset_index(inplace=True)\n",
    "\n",
    "# only keep the data for the United States\n",
    "jobs_data = jobs_data[jobs_data['search_country'] == 'United States']\n",
    "\n",
    "# look for null rows and drop them\n",
    "null_rows = jobs_data[jobs_data.isnull().any(axis=1)] \n",
    "jobs_data.dropna()\n",
    "\n",
    "# the jon_link column was only required for merging. Drop after merging is done \n",
    "jobs_data = jobs_data.drop('job_link', axis=1) \n",
    "\n",
    "# change column names\n",
    "jobs_data.columns = ['job_title', 'company', 'job_level', 'country', 'summary', 'skill']\n",
    "\n",
    "# CLEANING THE 'SKILL' COLUMN\n",
    "jobs_data['skill'] = jobs_data['skill'].str.split(', ')\n",
    "\n",
    "# Define the cleaning function to apply to each skill\n",
    "def clean_skill_name(skill):\n",
    "    skill_cleaned = re.sub(r'[^a-zA-Z0-9\\s]', '', skill)  # Remove special characters\n",
    "    skill_cleaned = skill_cleaned.lower()                # Convert to lowercase\n",
    "    skill_cleaned = skill_cleaned.strip()                # Remove trailing whitespace\n",
    "    return skill_cleaned\n",
    "    \n",
    "# Apply the cleaning function to each skill in each list in the 'skill' column\n",
    "jobs_data['skill'] = jobs_data['skill'].apply(\n",
    "    lambda skills: [clean_skill_name(skill)                  # for every skill in list , (not the entire list)\n",
    "                    for skill in skills] \n",
    "                        if isinstance(skills, list)          # Handling the case for an empty list\n",
    "                            else [])\n",
    "\n",
    "# CLEANING THE \"SUMMARY COLUMN\"\n",
    "def standardize_summary(summary):\n",
    "    # Convert to lowercase\n",
    "    summary = summary.lower()\n",
    "    # Replace new lines and tabs with a space\n",
    "    summary = re.sub(r'\\n', ' ', summary)\n",
    "    summary = re.sub(r'\\t', ' ', summary)\n",
    "    # Remove special characters (keeping only letters, numbers, and basic punctuation)\n",
    "    summary = re.sub(r'[^a-zA-Z0-9 .,!?;]', '', summary)\n",
    "    return summary\n",
    "# Applying the standardization\n",
    "jobs_data['summary'] = jobs_data['summary'].apply(standardize_summary)\n",
    "\n",
    "jobs_data.to_csv('jobs_data.csv', index=False) \n",
    "jobs_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #2: Coursera Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Course Title', 'Rating', 'Level', 'Schedule', 'What you will learn',\n",
       "       'Skill gain', 'Modules', 'Instructor', 'Offered By', 'Keyword',\n",
       "       'Course Url', 'Duration to complete (Approx.)', 'Number of Review'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the Coursera dataset\n",
    "courses = pd.read_csv(\"coursera-courses/CourseraDataset-Clean.csv\")\n",
    "courses.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null rows:  0\n",
      "Different levels of courses in this dataset:  ['Beginner level' 'Intermediate level' 'Not specified' 'Advanced level']\n",
      "Keywords in this dataset are:  ['Arts and Humanities' 'Business' 'Computer Science' 'DataScience'\n",
      " 'Health' 'Information Technology' 'Math and Logic' 'Personal Development'\n",
      " 'Physical Science and Engineering' 'Social Sciences']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>course_title</th>\n",
       "      <th>rating</th>\n",
       "      <th>level</th>\n",
       "      <th>skill</th>\n",
       "      <th>keyword</th>\n",
       "      <th>num_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>[python syntax and semantics, basic programmin...</td>\n",
       "      <td>CS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
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       "      <td>DS</td>\n",
       "      <td>225830</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>[python syntax and semantics, basic programmin...</td>\n",
       "      <td>MATH</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python for Everybody Specialization</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>[json, xml, python, database (dbms)]</td>\n",
       "      <td>CS</td>\n",
       "      <td>209311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Python for Everybody Specialization</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>[json, xml, python, database (dbms)]</td>\n",
       "      <td>DS</td>\n",
       "      <td>209311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2970</th>\n",
       "      <td>Audit System with Spring Boot AOP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>[application design, microservices, spring boo...</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>CompTIA a+ Network</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>[scripting, server administrator, authenticati...</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>MBA Essentials</td>\n",
       "      <td>0.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>[not specified]</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2973</th>\n",
       "      <td>Exam Prep CKA: Certified Kubernetes Administrator</td>\n",
       "      <td>0.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>[docker, kubernetes, aws cloud]</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>Machine Learning in Healthcare: Fundamentals &amp;...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>[not specified]</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2975 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           course_title  rating         level  \\\n",
       "0     Programming for Everybody (Getting Started wit...     4.8      beginner   \n",
       "1     Programming for Everybody (Getting Started wit...     4.8      beginner   \n",
       "2     Programming for Everybody (Getting Started wit...     4.8      beginner   \n",
       "3                   Python for Everybody Specialization     4.8      beginner   \n",
       "4                   Python for Everybody Specialization     4.8      beginner   \n",
       "...                                                 ...     ...           ...   \n",
       "2970                  Audit System with Spring Boot AOP     0.0  intermediate   \n",
       "2971                                 CompTIA a+ Network     0.0      beginner   \n",
       "2972                                     MBA Essentials     0.0  intermediate   \n",
       "2973  Exam Prep CKA: Certified Kubernetes Administrator     0.0  intermediate   \n",
       "2974  Machine Learning in Healthcare: Fundamentals &...     0.0      beginner   \n",
       "\n",
       "                                                  skill keyword  num_review  \n",
       "0     [python syntax and semantics, basic programmin...      CS      225830  \n",
       "1     [python syntax and semantics, basic programmin...      DS      225830  \n",
       "2     [python syntax and semantics, basic programmin...    MATH      225830  \n",
       "3                  [json, xml, python, database (dbms)]      CS      209311  \n",
       "4                  [json, xml, python, database (dbms)]      DS      209311  \n",
       "...                                                 ...     ...         ...  \n",
       "2970  [application design, microservices, spring boo...    MATH           0  \n",
       "2971  [scripting, server administrator, authenticati...    MATH           0  \n",
       "2972                                    [not specified]    MATH           0  \n",
       "2973                    [docker, kubernetes, aws cloud]    MATH           0  \n",
       "2974                                    [not specified]    MATH           0  \n",
       "\n",
       "[2975 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the names of the columns that are needed\n",
    "course_descriptors = ['Course Title', 'Rating', 'Level', 'Skill gain', 'Keyword', 'Number of Review']\n",
    "courses_data = courses[course_descriptors]\n",
    "\n",
    "# look for null rows (if any)\n",
    "null_rows = courses_data[courses_data.isnull().any(axis=1)]    \n",
    "print(\"Number of null rows: \", len(null_rows))    # there are no null rows\n",
    "\n",
    "# Removing rows with non-ASCII characters (there were some rows with chinese letters)\n",
    "mask = pd.Series([False] * len(courses_data))\n",
    "\n",
    "# Check each column for non-ASCII characters\n",
    "for column in courses_data.columns:\n",
    "    # Update the mask to True if any column contains non-ASCII characters\n",
    "    # This regex matches any character not in the ASCII range\n",
    "    mask |= courses_data[column].astype(str).str.contains(r'[^\\x00-\\x7F]+', na=False)\n",
    "\n",
    "# Dropping rows where any column contains non-ASCII characters \n",
    "courses_data = courses_data[~mask]\n",
    "courses_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Renaming the column names\n",
    "courses_data.columns = ['course_title', 'rating', 'level', 'skill', 'keyword', 'num_review']\n",
    "\n",
    "# getting all level values \n",
    "print(\"Different levels of courses in this dataset: \", courses_data['level'].unique())\n",
    "\n",
    "# CLEANING THE \"LEVEL\" COLUMN \n",
    "def standardize_level(level):\n",
    "    # Convert the level to lowercase and strip leading/trailing whitespace\n",
    "    level = level.lower().strip()\n",
    "    # Map each original level to its new standardized form\n",
    "    if 'beginner level' in level:\n",
    "        output = 'beginner'\n",
    "    elif 'intermediate level' in level:\n",
    "        output = 'intermediate'\n",
    "    elif 'not specified' in level:\n",
    "        output = 'N/A'\n",
    "    elif 'advanced level' in level:\n",
    "        output = 'advanced'\n",
    "    # Otherwise, if uncaught - keep as is\n",
    "    else:\n",
    "        output = level\n",
    "    \n",
    "    return output\n",
    "    \n",
    "# Apply the function to the 'level' column\n",
    "courses_data['level'] = courses_data['level'].apply(standardize_level)\n",
    "\n",
    "\n",
    "# CLEANING THE \"RATING\" AND \"NUM_REVIEW\" COLUMN \n",
    "# ensure all numbers are floats or ints \n",
    "courses_data['rating'] = pd.to_numeric(courses_data['rating'], errors='coerce')\n",
    "courses_data['num_review'] = pd.to_numeric(courses_data['num_review'], errors='coerce')\n",
    "\n",
    "# CLEANING THE \"KEYWORDS\" COLUMN\n",
    "unique_keywords = courses_data['keyword'].unique()\n",
    "print(\"Keywords in this dataset are: \", unique_keywords)\n",
    "# removing all courses that are NOT tech related \n",
    "exclude_keywords = ['Arts and Humanities', 'Business', 'Health', 'Personal Development',\n",
    "                    'Physical Science and Engineering', 'Social Sciences']\n",
    "\n",
    "# Corrected to 'keyword' to match the renamed column name\n",
    "courses_data = courses_data[~courses_data['keyword'].isin(exclude_keywords)]\n",
    "\n",
    "def standardize_keyword(keyw):\n",
    "    # Convert the level to lowercase and strip leading/trailing whitespace\n",
    "    keyw = keyw.lower().strip()\n",
    "    # Map each original keyword to its new standardized form\n",
    "    if 'computer science' in keyw:\n",
    "        output = 'CS'\n",
    "    elif 'datascience' in keyw:\n",
    "        output = 'DS'\n",
    "    elif 'information technology' in keyw:\n",
    "        output = 'IT'\n",
    "    elif 'math and logic' in keyw:\n",
    "        output = 'MATH'\n",
    "    # Otherwise, if uncaught - keep as is\n",
    "    else:\n",
    "        output = \"N/A\"\n",
    "    \n",
    "    return output\n",
    "    \n",
    "# Apply the function to the 'level' column\n",
    "courses_data['keyword'] = courses_data['keyword'].apply(standardize_keyword)\n",
    "\n",
    "# sorting the dataset first by num_reviews, then by rating\n",
    "courses_data = courses_data.sort_values([\"num_review\", \"rating\"], ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "# CLEANING THE 'SKILL' COLUMN\n",
    "def clean_skill_name(skill):\n",
    "    \"\"\"\n",
    "    Clean the skill name based on specific rules.\n",
    "    \"\"\"\n",
    "    replacements = {\n",
    "        \"python programming\": \"python\",\n",
    "        \"r programming\": \"r\",\n",
    "        \"cascading style sheets css\": \"css\"\n",
    "    }\n",
    "    return replacements.get(skill.lower(), skill.lower())\n",
    "\n",
    "# Cleaning the 'skill' column\n",
    "courses_data['skill'] = courses_data['skill'].str.split(', ')\n",
    "courses_data['skill'] = courses_data['skill'].apply(\n",
    "    lambda skills: [clean_skill_name(skill) \n",
    "                    for skill in skills] \n",
    "                        if isinstance(skills, list) \n",
    "                            else []\n",
    ")\n",
    "\n",
    "courses_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis and Results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 1: Top Skills required for Data Science Jobs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will extract the top five most wanted skills for Data Science Jobs on Linkedin in 2024. To do that, we will first look at the `skill` column. Since each entry in the `skill` column is a list, we will utilize `pd.explode()`, to get individual skills from the dataset. Then, we will use the `.value_counts()` to count the occurrences of each skill -- the top 50 skills are stored in the `top_job_skills` variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50 different skills needed for various Data Science Jobs. The following are the top 10 skills needed for Data Science Jobs:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "python                4079\n",
       "sql                   3860\n",
       "data analysis         2680\n",
       "machine learning      2314\n",
       "communication         2021\n",
       "data visualization    1893\n",
       "aws                   1563\n",
       "project management    1466\n",
       "data science          1438\n",
       "data engineering      1435\n",
       "Name: skill, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.explode.html\n",
    "exploded_jobs_df = jobs_data.explode('skill')                          # exploding the skill column\n",
    "job_skill_counts = exploded_jobs_df['skill'].value_counts()            # get value counts (num occurance of each skill)\n",
    "top_50_job_skills = job_skill_counts.head(50)                             # Store top 50 skills in \n",
    "\n",
    "\n",
    "# Display the number of skills\n",
    "print(\"There are\", len(top_50_job_skills), \"different skills needed for various Data Science Jobs. The following are the top 10 skills needed for Data Science Jobs:\")\n",
    "# print the top 10 \n",
    "top_50_job_skills.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the frequencies of top Data Science Job Skills\n",
    "top_50_job_skills.plot(kind='bar',figsize=(10, 8), color='#e09fd1')\n",
    "plt.title('Frequencies of the top 50 Skills for Data Science Jobs',fontsize=20)\n",
    "plt.xlabel('Job Skills', fontsize=16)\n",
    "plt.ylabel('Frequency of Jobs', fontsize=16)\n",
    "plt.xticks(rotation=90, fontsize=12) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From our analysis so far, it can be seen that **python, sql, data analysis, machine learning and communication are the top 5 job skills** that are demanded by companies hiring for some form of Data Science related position. Over 4,000 job postings required proficiency in python and 3,860 required proficiency in SQL. Data analysis and machine learning were also popular skills. While it is expected to see `python`, `sql`, `data analysis`, and `machine learning` for such positions, it is surprising to see how many job postings require `communication`. Next, we will be looking at the Coursera Dataset and the skills covered there. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 2: Top Skills Taught in Tech courses on Coursera\n",
    "\n",
    "In this section, we will be looking at the top skills taught in tech courses on coursera. We will follow a similar approach as before: since every entry in the `skill` column in `courses_data` dataframe is a list, we will utilize `pd.explode()`. Next, we will get the `value_counts` of each skill, and store the top 50 in `top_coursera_skills`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50 different skills taught in Coursera's Tech Courses. The following are the top 10 relevant skills:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "not specified           577\n",
       "python                  297\n",
       "data analysis           231\n",
       "machine learning        196\n",
       "data science            154\n",
       "data visualization      140\n",
       "computer programming    126\n",
       "cloud computing         120\n",
       "sql                     104\n",
       "r                        94\n",
       "Name: skill, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exploded_coursera_df = courses_data.explode('skill')                            # exploding the skill column\n",
    "course_skill_counts = exploded_coursera_df['skill'].value_counts()              # get value counts\n",
    "top_coursera_skills = course_skill_counts.head(50)\n",
    "print(\"There are\", len(top_coursera_skills), \"different skills taught in Coursera's Tech Courses. The following are the top 10 relevant skills:\")\n",
    "top_coursera_skills.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are a lot of `Not specified` values in the dataset, and since they don't add any value to our study, we will drop the rows with `Not specified` skills. Then, our data will be ready to plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "python    297\n",
       "Name: skill, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coursera_df =  exploded_coursera_df[exploded_coursera_df['skill']!= 'not specified']    # removing the not specified values\n",
    "course_skill_counts = coursera_df['skill'].value_counts()                               # count the number of each skill\n",
    "top_50_coursera_skills = course_skill_counts.head(50)                                   # get the top 50 skills \n",
    "\n",
    "top_50_coursera_skills.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the courses with `Not specified` skills are gone, we can plot our data. We will plot the names of the `top_50_coursera-skills` on the `x-axis`, and the frequency of courses that teach the specific skill on the `y-axis`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the frequencies of top Coursera skills\n",
    "top_50_coursera_skills.plot(kind='bar',figsize=(10, 8), color='#9fc5e0')\n",
    "plt.title('Frequencies of the top 50 Skills Taught in Coursera Courses in 2024',fontsize=20)\n",
    "plt.xlabel('Course Goals/Skills', fontsize=16)\n",
    "plt.ylabel('Frequency of Courses', fontsize=16)\n",
    "plt.xticks(rotation=90, fontsize=12) \n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this second bar chart, we can see that the top 5 skills taught in coursera courses are, **python, data analysis, machine learning, data science, and data visualization**. We can see that some of the top five skills - `python`, `data analysis` and `machine learning` - between the Coursera dataset and the Jobs dataset overlap. \n",
    "\n",
    "We will now explore the `coursera_data` dataset in greater detail. In particular, we will break the data set down by `keyword`. Keyword is a tag present in the database which classifies a course as being one of: \n",
    "- computer science (`CS`)\n",
    "- data science (`DS`)\n",
    "- information technology (`IT`)\n",
    "-  math and logic (`MATH`).\n",
    "\n",
    "Based on the top 5 skills demanded for Data Science Jobs, we will now explore which of these subcategories teaches a greater proportion of such skills.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3: Exploring `coursera_df`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will be analyzing the Coursera dataset in more detail. We will begin by breaking the dataset down by the `keyword` tag, and analyzing how many courses belong to each subcategory. To do so, we will be creating a bar chart which plots the number of Coursera courses against the `keyword`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.1: Overall Proportion of Courses that Teach Highly Demanded Skills\n",
    "\n",
    "\n",
    "In this code, `top_5_js` is a list containing the names of the top five job skills. `overall_skill_counts` calculates the frequency of these skills in the `coursera_df` DataFrame by filtering the `'skill'` column to only include rows that contain one of the top five skills and then applying `value_counts()`. The variable `total_courses` holds the number of unique courses in the DataFrame, determined by the number of unique entries in the `'course_title'` column. Finally, `overall_skill_proportions` computes the proportion of courses teaching each skill by dividing the counts of each of the top skills by the total number of courses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "python              18.087698\n",
       "data analysis       14.068210\n",
       "machine learning    11.936663\n",
       "sql                  6.333739\n",
       "communication        2.375152\n",
       "Name: skill, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5_js = ['python', 'sql', 'data analysis', 'machine learning', 'communication']    # top 5 job skills \n",
    "overall_skill_counts = coursera_df[coursera_df['skill'].isin(top_5_js)]['skill'].value_counts()\n",
    "total_courses = coursera_df['course_title'].nunique()\n",
    "overall_skill_proportions = overall_skill_counts / total_courses\n",
    "overall_skill_percentages = overall_skill_proportions * 100\n",
    "overall_skill_percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we see that **18%** of Coursera courses in Data Science, Computer Science, MATH and IT, combined, teach **python**. This makes sense because python is heavily used in Data Science applications, and is a very helpful tool. However, it is also interesting to note that only **2.3%** of the Tech courses on Coursera teach **communication** skills. Communication is an important skill, required in jobs from any domain. However, it can be seen that specifically domain related coursework might neglect this \"soft skill\". Following is a visual representation of this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Pastel1.colors[:5]            \n",
    "overall_skill_percentages.plot(kind='bar', color=colors)\n",
    "\n",
    "# Add title and labels to the plot\n",
    "plt.title('Overall Percentage of Coursera Courses Teaching Top 5 Job Skills', fontsize=14)\n",
    "plt.xlabel('Skill', fontsize=12)\n",
    "plt.ylabel('Percentage of Courses (%)', fontsize=12)\n",
    "\n",
    "# Show the values on top of the bars\n",
    "for index, value in enumerate(overall_skill_percentages):\n",
    "    plt.text(index, value, f'{value:.2f}%', ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.xticks(rotation=360)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.2: Proportion of Courses that Teach each Highly Demanded skill by Department. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will plot the frequency of courses taught in each sub-category of \"tech-related\" courses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YsQNv3rzBjBkz0KNHD2RmZuLnn39GUlJSju0VMTRs2FD5/HiX4rnwbgze3t4oUqQIQkJClM8x4G0hZWhoiE6dOilzaN68OTIyMpRzpe7fv4/Hjx/D1dUVjo6OePnyJf777z8Ab0/hZWRkoGXLlgDy9royNTVF1apVVbZduXIFqampsLe3z/EPpDZt2mTb9imvK3VIpVJ4e3vn+PNucfT+bd537tw5yGQyODo65rh8QbNmzWBgYIDz589DJpPh8ePHePjwISwtLWFvb5+tvTqnxj/m33//RVJSEmrUqIHy5ctn2+/g4IAyZcrg7t27yoJUwd7eXqVoAoBy5coBePteoihOFf3s7u6e4+n2TZs2YfPmzcqiTVEYBQUFKds8f/4cp0+fRs2aNVG3bl1N0y00OMeJNGZpaYmbN28iLi7uk2/7/PlzAFD+lZwTxTyV998wNFG8ePFs2/r37487d+5gz5492LBhAzZs2AATExM0atQIHTp0gLe3t7IwefLkCQBg9uzZH7yU/+nTp2rdNwAkJydj27ZtOHnyJO7evYtnz55BLpervLmJ9yaGvq9EiRIoUqQI0tPTER8fr/JXtDrU6YevvvoKL168wPPnz1VGMDSR02MxY8YMDBs2DFeuXMGiRYuwaNEilCxZEi1atEC3bt3QsGFDZVvF4/vuKFROFP2lDkNDQ8yaNQudO3eGl5cXIiIicP/+fUyePDnHCfCKYyueM7l597lQvHhxtG7dGvv378eRI0fQoUMHXL58GXfu3EGLFi1UPjTd3d2xc+dOnD59Gs2aNVMWaq6urihfvjwOHjyIc+fOoUaNGtnmN+XldZVT4aw4Xk4f6u8e612f8rpShybLEZQoUSLbNkW/HT169IPrQqWlpeHVq1e5jpwpVKtW7ZNiyokipps3b350raonT57A0tJS+XtOr6V312eTy+UwMDBQ5lGxYkW1YmrWrBkqVKiAs2fP4tmzZyhfvny2kdEvHQsn0pi9vT3Cw8Nx+fJl5amI3CQlJWHp0qVo1KgRmjZtqiwIPjThWNFG3RXHPzQ6k9NfuMbGxpgzZw4GDx6M0NBQnDx5EhcvXsSJEydw4sQJbN++HWvWrIGJiYnyrzdXV1flX3U5eXeo/UP3ffv2bfj5+SEuLg5lypSBvb092rdvjzp16qBhw4Y5TjLPja2tLS5evIhLly6hcuXKH2wbHR2N06dPw83NTWXysbb64WOnYHJ6LCpUqIAdO3bg/PnzOHz4ME6fPo2bN28iKCgIQUFB+OGHH5RXRSn6uEOHDh8ctahdu/ZHY1X48ccflZOVixYtit9//x29e/dGcHAwmjRpopzk/X6ODg4OH/zwfP8x7datG/bv34+9e/eiQ4cOytN2738YNWnSBMbGxjh9+jSAt6NKxsbGqF+/vrKAiYyMRM+ePXHy5EmUKVNG2Zd5eV3l9Hh+7IKAnAqgT3ld5Zec4lb0m7W1NerUqaPRMd6lySKy779HKWKqVKkSGjRo8MHbvv9HkboXa3zqSLSBgQG6dOmCZcuWYf/+/RgwYACCgoJURka/dCycSGOenp5Yvnw5/vnnH6Snp6NIkSK5tj106BBWr16NPXv2IDw8XFl8fGgej+JSdcXcHcUbe24fzq9fv9Yoj2rVqsHf3x/+/v5IS0vDsWPHMG3aNERFRSEsLAwdOnRQ/qXn7e2d7YNUE9OnT0dcXBx++OEH/PzzzyofWq9evfqkY3l6euLixYsICQlBx44dP9h206ZN2Lt3L65du4bFixdr1A+KN+yc+iExMfGTYn9XgwYNlB8ecXFx2LlzJ+bPn49Vq1bB19cX5cuXR7ly5fDo0SMMHz4822klTb1fCDs5OWHQoEFYsmQJZs6ciXr16qlcPaZ4LjRt2vSDyxW8r1GjRvjqq69w6tQpvH79GmFhYShZsmS2pRvMzc3h4uKCM2fOIC4uDufOnYOjoyPMzMxgbW0NS0tLRERE4M6dO3j06BG6du2q7BNN+vNDFKMtjx49ynG/YkQqJ+q8rj4nRb/VqVNHra+HURQ5jx8/znH/s2fPsm371PcoRUwVKlTQ+lfWKCieEznFC7wtzF++fIlGjRop4+natSuWL1+OkJAQeHh44Pbt22jZsqXKiNeXjHOcSGOOjo5wdnbG8+fPsXz58lzbJSYmYsWKFQCAnj17wtDQEJUqVcJXX32FhISEHOejJCUlKdcSUcyZUMxLyOnU4I0bN5Camqp27HK5HP369YObm5vKnBMzMzO0b98eX3/9NYD/G0pXxHD8+PEcj7dp0yZ06tQJS5cuVev+FZOXBw4cmO0v/VOnTqnE+TFdu3ZFyZIlERoaqnLZ+vsuXbqEQ4cOAXg71wR4W6xIJBKcOnUKycnJ2W5z9uxZxMfHw8rKCpUqVQLwf3/55tQPFy9e/Gi87/rvv//g7e2N77//XmV7mTJl4O/vDxsbG8jlcuWb/sf6YcyYMejRo0eua9moa8iQIXB0dERaWhp++uknpKenK/cpYjh58mSO/XP48GF07NgR06ZNU9kukUjg4+ODzMxMLFy4EM+fP4e3t3eOIy8tWrSAEAKbN2/Gy5cvVebCubq64sWLF8pJ4O8WXpq8rj7Ezs4OJUqUwLVr13IsII4dO6by+6e+rj4nRb7nzp3LtpYV8Ha+Ubt27fDjjz9CCAFLS0tYW1sjLi4O58+fz9b+/dyBT3+PcnBwgKmpKW7cuJFjEfrs2TO0b98e/fv3V66p9akUf4ycPHkyx/3z5s3DqFGjkJCQoNxWuXJlNGzYEJcvX8amTZsAZB8Z/ZKxcKI8mTp1KkxNTbF06VLMmTMn219UsbGxGDRoEGJjY1GtWjX88MMPyn1+fn4AgIkTJyr/CgaAlJQUjB49GsnJyWjZsqVyvkb16tVhYmKC2NhYlQ/GV69eZfuQ+hgDAwNYWFjgxYsXmDdvnsoQemJionLuiOIUiJeXF8qVK4ewsDCsWbNGZe7R5cuXsWDBAty4cSPHSak5UZzSO3LkiMr2c+fO4ddff1X+npGRodaxRo8eDQAYPHgwdu7cqbzSTkGxRk1GRgY6duyonFxduXJleHp6Ijk5GaNHj1Z5Y4+NjcXEiRMBAH379lVuV5wGO3z4sMpIxM2bN7Fy5cqPJ/+OqlWr4uXLlwgPD1cWdQpXr17FnTt3UKxYMdSoUQPA20VFDQ0N8eeff2YrErdu3YqgoCBcv35d5TSkJoyMjPD777/DzMwMt27dwqxZs5T7XF1dYWtri3///Re///67Sh/dv38fM2bMwO3bt3M8jde1a1cYGBh89MNIMdlbURy9XzgBwM6dO2FiYoImTZqo3PZTX1cfYmxsjN69e0Mmk2HMmDEqxXVYWFi2qwQ/9XX1OSme60+fPsUvv/yikktcXBwmTJiAu3fvokKFCsoRvO+++w4AMGnSJJURm0uXLuX4x+KnvkcVLVoUPXr0QGpqKkaPHq1ScKWkpGD8+PH477//ULRo0U+ev6jQqFEj1KpVC9euXcsW86ZNm3D58mXUrl0723tX165dIYTApk2bUKpUKbi7u2t0/4URT9VRntSqVQvr1q3DoEGDsHr1amzZsgX29vYoW7Ysnj59isuXL0Mmk0EqlWLFihUqL35fX19cvHgRBw8eRIcOHeDi4gIzMzNERUUhISEBtWvXVvnAKlq0KPr06YM1a9Zg6NChKgv7lSxZEs7OztlWbf6QsWPHIioqCuvWrcPhw4dRp04dZGRk4MKFC0hOToa3t7dyYrKZmRn+/PNP+Pv747fffsPGjRthY2ODxMREXLhwAUII9OvXD61atVLrvvv374/Zs2dj7NixCAwMhKWlJR48eIDr16+jZMmSsLS0xIsXL/DixQuYm5t/9HjdunVDVlYWpk+fjgkTJmDevHmoXbs2ihUrhjt37iAmJgYA0L59e5XHFHh72vDevXs4evQoPDw84OzsjLS0NERGRiIjIwPe3t4ql6m7urrC3t4eV69ehbe3N1xdXZGamopz586hefPmuHDhgpo98HaOzPTp0xEQEIDhw4fDzs4OVlZWSEhIUF7dNGnSJOVjYG9vjwkTJmDGjBn49ttvYWtrCysrK9y9exe3b9+GoaEh5s6dq5Uvk65evTrGjh2LqVOnYuvWrWjatCnatGkDiUSCefPmwc/PD2vWrMGBAwdgZ2eHN2/eICoqCpmZmWjTpo1KsalQsWJFNGnSBOHh4ahduzZsbW1zvO+qVauiWrVquHfvHkxMTFCvXj3lPsXVcllZWWjevHm2K8Q+9XX1MUOGDMGFCxcQERGBVq1awcXFBS9fvsSFCxdQr169bKOMn/K6+tx+/fVX3L9/HwcOHMCpU6fg4OAAiUSCqKgopKamol69ehgxYoSyfdeuXREVFYVdu3ahXbt2aNSoEdLT0xEREYFy5cplGwXS5D1q5MiRuH79Os6ePYvWrVvDwcEBZmZmuHjxIhITE1GtWrVP/sPwXRKJBP/73//w7bffYv78+QgKCkKtWrVw//593LhxA+bm5jleBNG2bVv8+uuvSEpKynVk9EvFESfKMycnJwQHByMgIAC1atXCjRs3EBoaiv/++w8NGjTAlClTsHPnTuWpHgUDAwPMnz8fs2fPhp2dHS5cuIBTp06hQoUKGD16NLZt25ZtsvWYMWMwYcIE1KxZExcuXMCVK1fg5eWF7du3f/J6UlWqVMHWrVvRuXNnyOVyHDt2DBcuXECtWrUwY8YM/P777yrt69evjz179qBXr14QQuDEiRN48OABXF1dsWTJEvzyyy9q3/e3336LefPmwdHREbdv38bp06chk8ng6+uLvXv3Klf9/eeff9Q+Zq9evbBnzx707t0bJUuWxPnz53HkyBG8evUKrVq1wsqVK7FgwYJsc9HKlCmDwMBABAQEoEyZMjhx4gSuXr2KevXqYf78+fjjjz9UJqIaGBhgzZo18PPzQ/HixXHy5Ek8efIEP/74IxYvXvzJK4y3bt0aq1atQvPmzfH48WMcOXIEMTExaN68OdavX48ePXqotO/bty82bdqE1q1b4+nTp/jnn3+QmpqK9u3bY8eOHSorJufVN998o5yoP3HiROUIW/Xq1bFnzx4MGDAARYsWxalTp3Djxg3Y29tj9uzZmD9/fq5XjtWvXx/Ax099KP7Cd3JyUumzKlWqKEeLchoF0OR19SEmJib4+++/MXLkSJQqVQrHjx/H8+fPMXLkSIwcOTJb+099XX1OZcqUwbZt2zB8+HCUK1cO586dQ3R0NKpXr47x48dj7dq12QrRWbNmYebMmahWrRrCw8Nx+/Zt9O/fP8dV+IFPf48yNTXF6tWr8csvv6BGjRq4fPmysjALCAjA9u3b8/yHgI2NDXbv3o1evXrhzZs3OHr0KJ4/f46OHTti586dyhHd9+Oys7MDwLWb3icRH7vemYiItMbb2xv37t3DiRMntPJ1QqQbERER6NevH+rXr48tW7boOhyte/nyJdzd3WFjY4OdO3fqOhy9whEnIqJ89ubNG8jlcqxZswa3bt1Cx44dWTSR3snKykJWVhbevHmD6dOnIzMzM8dTzl86znEiIspnbdq0QUJCAjIyMlC0aFEMHTpU1yERZRMbG6tc0iQrKwt16tSBt7e3jqPSPxxxIiLKZy4uLjA0NISNjQ1WrFiR44rbRLpWqVIlWFlZwdjYGG5ubli+fLlGC30WdpzjRERERKQmjjgRERERqYmFExEREZGaePIyn8nlcmRlZcHAwOCT17chIiKi/CeEgFwuh5GR0Qe/QBxg4ZTvsrKycOXKFV2HQURERB/h4ODw0VXSWTjlM0XlamtrW2iXrJfJZLhy5QocHBxyXS25oCvsORb2/ADmWBgU9vyAwp+jvuaniOtjo00AC6d8pzg9Z2hoqFdPkvzAHAu+wp4fwBwLg8KeH1D4c9TX/NSZUsPJ4URERERqYuFEREREpCYWTkRERERqYuFEREREpCYWTqQVZmZmug4h3xX2HAt7fgBzLAwKe35A4c+xoOfH76rLZzKZDNHR0XByctLLKwiIiIgKCiFEviwm/Smf1VyO4DN58vohMpGh6zCIiIgKpCKGpqhUvLKuw2Dh9LlkyNORIU/XdRhERESUB5zjRERERKQmFk5EREREamLhRERERKQmFk5EREREamLhRERERKQmFk5EREREamLhRERERKQmFk5EREREamLhRERERKQmFk5EREREamLhRERERKQmFk5EREREamLhRERERKQmFk5EREREajLS1oFkMhnCw8NhaGiIJk2awMCANRkREREVLhoVTkIIzJs3DzExMVi2bBlkMhn69OmDS5cuAQDq1KmD9evXw9zcXKvBEhEREemSRsNCa9euxV9//YWUlBQAQEhICKKjo+Hp6YnBgwfj9u3bWL58uVYDJSIiItI1jUacgoKC0LhxY6xevRoAcPjwYZiYmGDOnDkoVqwYXr16hbCwMIwaNUqrwRIRERHpkkYjTvfv30e7du0gkUgAAGfPnkXdunVRrFgxAEDt2rXx5MkT7UVJREREpAc0KpxMTEwgl8sBANevX0d8fDwaNWqk3P/69WtYWFhoJ0IiIiIiPaFR4VSjRg38888/AIDAwEBIJBK4u7sDAJKTk7Fr1y7UrFlTa0ESERER6QON5jj5+vpi5MiRaNCgAVJSUuDk5AQ7OztcuXIFgwcPRlxcnN7Nb/L19UVkZKTyd4lEAjMzM1SvXh1dunRB7969YWhoqNx/69YtLFu2DJGRkXj16hVKliwJZ2dn+Pv7w9bWVhcpEBERkY5pVDh5eXnByMgIu3btQoUKFRAQEAAAMDU1hbm5OcaPH4+WLVtqNVBtsLW1xZQpUwC8XXfq1atXOH78OGbNmoXz589j/vz5kEgkuH37Nnr27AlHR0f88ssvKFu2LJ4+fYqNGzeiZ8+e2LBhA5ycnHSbDBEREX12Gi+A2aZNG7Rp00ZlW61atXDo0KE8B5VfzM3NsxU8Hh4eqF69OmbPng0PDw98/fXXWLNmDUqWLIm///4bxsbGyratWrVC+/btsXTpUqxcufIzR09ERES6lqflvZ89e4bAwEDMmzcP9+/fx4sXL3D16lVtxfbZ+Pr6oly5cti6dSsA4OXLlwDeLvT5rqJFi2L8+PFo3779Z4+RiIiIdE/jEaf169fjjz/+QEZGBiQSCRo3boz09HQMHjwYffr0wcSJE7UZZ74yNDRE48aNERwcjKysLLi7u+P48ePo1asXunbtikaNGqFGjRqQSCRo166drsMlIiIiHdFoxOmff/7BrFmzUK9ePUybNk05MlOjRg3Uq1cPmzZtwq5du7QaaH4rW7YsMjMzkZiYiN69e2PIkCGIiYnB9OnT4eXlhcaNG2PUqFHKr5UhIiKiL49GhdOqVatga2uL1atXq8xzqlKlCtavXw97e3ts2bJFa0F+TopFPYcPH46TJ0/if//7H7p16wZzc3Ps27cPPXv2xLp163QcJREREemCRoXTv//+iw4dOqhcvq9gZGSETp064d69e3mN7bN69uwZTE1NUbJkSeW2EiVKoGPHjpg5cyYOHz6M3bt3w9raGn/88QcSEhJ0FywRERHphMaTw4sUKZLrvoyMDGRlZWl66M9OJpMhMjIS9evXx8uXL+Hm5obt27dna2dra4sRI0YgIyMDsbGxOoiUiIiIdEmjwkkqlSpXDn+fXC5HcHAwatWqlafAPqetW7fi+fPn+Oabb1C2bFkYGRlh8+bNSE9Pz9b2v//+Q5EiRVC1alUdREpERES6pNFVdX379sXo0aMxY8YM5VVm6enp+Pfff7Fo0SL8+++/+PXXX7UaqDYkJycjOjoawNsCLyEhAeHh4QgMDMTXX3+tnK81depUDB06FF27dkWfPn1Qs2ZNpKWl4dSpU9i0aROGDx+OEiVK6DATIiIi0gWJeH+xIjXNnTsXq1atgkQigRBCOalaCIFu3bphxowZWg00r97/yhUDAwOUKVMG1atXR/fu3eHt7a3MAXg7j2vVqlU4f/484uPjYWJiAltbW/j6+mZb+PNDZDIZoqOjUaqaBTLk2UewiIiI6OOKGJmieqn8OZul+Kx2cnLKcf72uzQunADg8uXLOHDgAO7duweZTAYrKyu0bdsWjRs31vSQhQ4LJyIiorzTl8JJ4wUwAcDR0RGOjo55OQQRERFRgZGnwik2NhbPnj3L9tUkCi4uLnk5PBEREZFe0ahwunfvHgICAhATE/PBdtevX9coKCIiIiJ9pFHhNHPmTNy5cwft27dHlSpVYGSUp4ErIiIiogJBo4onOjoafn5+GDt2rLbjISIiItJbGi2AaWhoyAUgiYiI6IujUeHk4eGR68rhRERERIWVRqfqxowZA19fXwwdOhReXl6wtLRUWTxSgVfVERERUWGiUeH08uVLpKWl4ciRIzh69Giu7XhVHRERERUmGhVO06dPx+PHj9GqVSvUrFkTxsbG2o6LiIiISO9oVDhduXKFV9URERHRF0ejyeFFihRB9erVtR0LERERkV7TqHByd3dHaGiotmMhIiIi0msanar7/vvv8cMPP2DQoEHKq+pyWj2cV9URERFRYaJR4dSxY0cAwJMnT3D8+PFc2/GqOiIiIipMNCqchg4dmuO6TURERESFmUaFU0BAgLbjICIiItJ7Gk0OV8eFCxfy69BEREREOqHRiJNMJsOqVatw4MABpKamQi6Xq+xLSkpCamoq5zgRERFRoaJR4bRixQosXLgQxsbGsLCwQHx8PCpWrIiEhAS8efMGZmZm8Pf313asRERERDql0am6AwcOoFatWjh9+jS2bNkCiUSCjRs3IioqCmPGjMGbN2/g6Oio7ViJiIiIdEqjEaeHDx8iICAAFhYWsLCwQLFixXDx4kV07NgR3333HSIjI7F+/Xp4enpqO94Cy8SgCCQGvBKRiIhIE0UMTXUdAgANCycAKFWqlPL/VapUwa1bt5S/N2vWDCtWrMhbZIVMxeJWMDQ01HUYREREBZYQQufLIWl0qq5ixYq4f/++8ncrKyuVwsnAwACvXr3Ke3SFiEwm03UI+UYmk+HatWvMsQAr7PkBzLEwKOz5AYU/x7zmp+uiCdCwcGrevDm2bNmCw4cPQwgBBwcHRERE4O7du8jMzERwcDAqVKig7VhJj6Wlpek6hHxX2HMs7PkBzLEwKOz5AYU/x4Ken0aF08CBA2FhYYGAgAAkJCSge/fuMDExQceOHdG4cWNERUWhc+fOWg6ViIiISLc0muNUpkwZ7N27F7t370bp0qUBABs2bMCsWbOQmJgIT09PDBw4UKuBEhEREemaxpPDzc3N4evrq/xdKpVi7dq12oiJiIiISC/l21euEBERERU2ao049evX75MPLJFIsG7duk++HREREZG+UqtwioyMVPuAEolEL9ZZICIiItI2tQqnGzdufLRNfHw8pk2bhpCQEJiYmODHH3/Mc3BERERE+kTjyeHvCg0NxbRp0xAfHw8HBwf89ttvqFmzpjYOTURERKQ38lQ4vXr1CtOmTcPBgwdhZGSEn376Cd9//z0MDDjnnIiIiAofjQunw4cPY+rUqXj58iXs7OwwZ84cWFtbazM2IiIiIr3yyYXT69ev8euvv2L//v0wMjLC8OHD4e/vzy+wJSIiokLvkwqnY8eOYfLkyXj+/DlsbW0xe/Zs2NjY5FdsVICYmZnpOoR8V9hzLOz5AcyxMCjs+QFfRo4FmUQIIT7WKDk5GTNnzsSePXtgaGiIwYMHY/DgwZzLpAaZTIbo6Gg4OTlxVI6IiAoldZch0tfPxE+JS60Rp44dO+LZs2cAAHd3dwghsHTp0g/eRiKRYOjQoWqGXPhFPLyK1xmpug6DiIhIq4qbFkOTyo66DuOzUatwevr0qfL/hw8fxuHDhz96GxZOqpLSU5GQnqTrMIiIiCgP1Cqc1q9fn99xEBEREek9tQqnhg0b5nccRERERHqPs7uJiIiI1MTCiYiIiEhNLJyIiIiI1MTCiYiIiEhNLJyIiIiI1KRR4bR48WLcunUr1/2XLl3CxIkTNQ6KiIiISB/lS+F08eJFBAUFaRwUERERkT5Sax2n+/fv44cffoBMJlNumzVrFubPn5+trRACz58/x1dffaW9KImIiIj0gFojTlWrVkXz5s0hhFB+kZ/i/+//GBoawt7eHlOnTs3n0ImIiIg+L7VGnABg4sSJynlLtWvXxoQJE+Dt7Z1vgRERERHpG7ULp3cdOXIEpUuX1nYsRERERHpNo8JJMX8pPT0diYmJKnOf3lWpUiXNIyMiIiLSMxoVTikpKZgxYwb279+PrKysHNtIJBJcu3YtT8ERERER6RONCqf58+dj9+7dqFatGuzs7GBiYqLtuIiIiIj0jkaFU0hICNzc3PDXX39BIpFoOyYiIiIivaTRApivXr1C27ZtWTQRERHRF0Wjwqlq1ap49uyZtmMhIiIi0msaFU59+vTB5s2b8fTpU23HQ0RERKS3NJrjlJaWhqJFi6Jdu3Zo0KABLC0ts522k0gkmDVrllaCJCIiItIHGhVOc+bMUf7/1KlTObbR18LpypUrWL9+Pc6dO4f4+HhYWlqicePGGDhwICpXrqxs9+jRIyxduhTh4eGIi4uDubk56tati/79+6NRo0Y6zICIiIh0ReOVwwuiTZs2YdasWXB1dcXIkSNRrlw5PHjwAH///TdCQ0OxZs0a2NnZ4cWLF+jZsyfKlSuHn376CZUqVUJ8fDy2b9+Ob7/9FgsXLkSbNm10nQ4RERF9ZnlaObwgOX/+PGbOnIk+ffrgl19+UW53dXWFp6cnfHx8MH78eOzduxfbtm3D69evcfDgQVhYWCjbtm7dGt27d8eff/7JwomIiOgLpFHhpHDo0CGEhYXh0aNHmDBhAooWLYoTJ06gd+/eMDU11VaMWrFq1SpYWFjg559/zravdOnSGDduHGJiYpCcnIyXL19CIpFALpertDM0NMTIkSMRExPzucImIiIiPaJR4SSXyzFixAiEhYVBCAGJRIKUlBQ8efIEv//+O0JDQ/H333/D3Nxc2/FqRAiB8PBweHh4wMzMLMc27dq1U/7f3d0dmzdvRo8ePdCjRw80btwYNjY2MDQ0RNOmTdG0adPPFToRERHpEY2WI1i3bh1CQ0MxePBg7N27F0IIAICbmxu+//57REdHY82aNVoNNC8SEhKQnp4OKysrtdq3aNECkydPxsuXL/H777+jS5cuaNiwIYYNG4bw8PB8jpaIiIj0lUaF065du+Dp6Ykff/wRlpaWyu3FihXDqFGj0L59exw8eFBrQeaVgcHbNGUymdq36dOnD8LDw7F48WL06dMHFStWRFhYGAYMGIDffvstv0IlIiIiPaZR4XT//n00adIk1/2NGzfG48ePNQ5K20qWLIlixYp9MKbU1FQkJiaqbDMzM0Pr1q0xefJk7N+/H6GhoXBxccGaNWtw69atfI6aiIiI9I1GhZOZmRlSU1Nz3f/8+XMUKVJE46Dyg5ubGyIiIpCenp7j/l27dqFx48Y4f/48PDw8sHDhwmxtqlatqrwijxPEiYiIvjwaFU716tXD7t27kZWVlW1fYmIiAgMD4eTklNfYtOq7775DYmIi5s+fn21fXFwc/v77b1StWhX169dHuXLlsHPnTiQkJGRre/fuXQCAVCrN95iJiIhIv2h0Vd2QIUPQu3dv9OrVC23atIFEIkF0dDRu3LiBtWvXIj4+Ht9//722Y80TJycnDB8+HAsWLMCdO3fQpUsXlCpVCrdv38bq1auRkpKClStXQiKRYOLEifD19YWPjw/69euHOnXqQC6X49y5c1i7di169eoFa2trXadEREREn5lEKC6J+0RHjhzBxIkTlaMyEokEQggUK1YMkyZNQufOnbUZp9YcP34cmzZtwvXr15GYmIgKFSqgcePGGDRoECpVqqRsd//+faxYsQIRERF48eIFDA0NYW1tjR49eqBbt27ZvpsvNzKZDNHR0XhpkYH49KT8SouIiEgnSplaoF2txmq1VXwmOjk5wdDQMJ8jU9+nxKVx4QQAb968walTp3Dv3j3IZDJYWVmhWbNmKqttf+lYOBERUWH2pRVOeVo53NTUFJ6ennk5BBEREVGBoXHh9PjxY5w5cwbPnj3L9tUkwNtTd0OHDs1TcERERET6RKPC6eTJkwgICEB6ejpyO9PHwomIiIgKG40Kp/nz58PU1BTjxo1D1apV9eo8JREREVF+0ahwunv3LgICAtCrVy9tx0NERESktzRaALNUqVIcZSIiIqIvjkaFU/fu3bFjxw6kpaVpOx4iIiIivaXRqTp/f39cvHgR7dq1Q4sWLVCuXLlsbTg5nIiIiAobjQqno0eP4vTp08jKysK2bdtybMPCiYiIiAobjQqnhQsXolixYhg6dCisra1hZJSndTSJiIiICgSNKp779+9j+PDh6Nevn7bjISIiItJbGk0OL1u2LIyNjbUdCxEREZFe06hw8vHxQWBgIFJTU7UdDxEREZHe0uhUXfXq1ZGamoq2bduiRYsWKFu2bLZ5TpwcTkRERIWNRoXTyJEjlf/fsWNHjm1YOBEREVFho1HhtH79em3HQURERKT3NCqcrKysULp0aZiamua4/9WrV7h+/XqeAiMiIiLSNxpNDvf09MThw4dz3R8WFobBgwdrHBQRERGRPlJrxOnhw4fYs2eP8nchBEJDQ3Hv3r1sbYUQOHr0KJcrICIiokJHrcKpUqVKCA0Nxa1btwC8nfgdGhqK0NDQXG/Tv39/7URIREREpCfUKpwMDAywYsUKxMbGQggBPz8/DBw4EE2bNs2xbdmyZVGtWjVtx0pERESkU2pPDq9YsSIqVqwIAHB3d4eXlxdsbGzyLTAiIiIifaPRVXVXrlxBUFAQxowZo+14Ci2LIkUhJLqOgoiISLuKmxbTdQiflUaFU1JSEqpXr67tWAo1Vyt7GBoa6joMIiIirRNCQCL5MkYHNFqOoFWrVti5cyfi4+O1HU+hJZPJdB1CvpHJZLh27RpzLMAKe34AcywMCnt+QMHN8UspmgANR5ysra1x7NgxtG7dGnXr1kXZsmWzjaZIJBLMmjVLK0GS/ktLS9N1CPmusOdY2PMDmGNhUNjzA76MHAsyjQqnhQsXKv9/+vTpHNuwcCIiIqLCRqPC6ciRI9qOg4iIiEjvaVQ4ffXVV9qOg4iIiEjvaVQ4KVy/fh0pKSkQQii3ZWVlISkpCSdPnsSvv/6a5wCJiIiI9IVGhdODBw8waNAg3L1794PtWDgRERFRYaLRcgR//vkn7t69i7Zt26JTp04AAH9/f3Tt2hUWFhYwNTXF7t27tRooERERka5pVDhFRETAy8sLCxYswIQJEyCEgLu7O2bMmIHt27fD0NDwg18ATERERFQQaVQ4JSYmwtnZGQBQvHhxVKxYEdeuXQMAVK1aFT4+PggLC9NelERERER6QKPCyczMTOV3KysrxMTEKH+XSqV4+vRp3iIjIiIi0jMaFU5SqVRl4csqVarg6tWryt9fvHiR98iIiIiI9IxGhVPnzp0RFhaGoUOHIiUlBe7u7rhy5Qrmz5+P4OBgbNiwAVKpVNuxkh57fxSyMCrsORb2/ADmSER5p9FyBN27d8ft27exefNmGBkZoVWrVmjRogVWrFgBiUQCY2NjBAQEaDvWAu397/IrTAwNDWFra6vrMPJVYc+xsOcHMMdPJRcCBl/QF7cSqUvjBTAnTJiAIUOGoEiRIgCAJUuW4MCBA0hMTISbmxtq1qyptSALg/UXb+FREr+4kYj0X0WLohjgXFvXYRDppTytHF6yZMn/O5CRkXJNJ8ruWVIqYl+l6DoMIiIiygONC6e0tDSsXr0aoaGhePDgAYyMjFCtWjV07NgRffr0gZFRnmoyIiIiIr2jUXWTkJCA3r174+7du7CwsED16tWRlZWFO3fuYPbs2Th48CDWr18PExMTbcdLREREpDMaFU4LFizAvXv3MGHCBPTu3Vs5upSRkYG1a9di3rx5WLZsGYYPH67VYImIiIh0SaPlCI4ePYru3bujX79+KqfkTExM4O/vj86dO2Pfvn1aC5KIiIhIH2hUOL1+/Rq1a+d+xUXdunW5CCYREREVOhoVTnZ2djh58mSu+6Ojo7kAJhERERU6GhVOv/zyC6KiojBlyhTExcUpt6elpWHJkiUIDQ3FtGnTtBYkERERkT7QaHL4Tz/9BIlEgm3btmHbtm0oVaoUTExM8OLFC8jlcggh0LVrV5XbSCQSXLt2TStBExEREemCRoVT+fLlUb58+WzbK1eunOeAiIiIiPSVRoXThg0btB0HERERkd7TaI7Tu9LS0nD37l2kpaVBLpdrIyYiIiIivaRx4RQbG4uBAwfCxcUFXl5eiI6ORmRkJLy9vXH+/HltxkhERESkFzQqnJ48eYIePXrgzJkzqF+/vnK7XC7H3bt38f333+PGjRtaC5KIiIhIH2hUOC1cuBDp6enYvXs3FixYACEEAKBJkybYsWMHTExMsGzZMq0GSkRERKRrGhVOJ0+exDfffIOaNWtCIpGo7KtduzZ69eqF6OhobcRHREREpDc0KpwSExNRtWrVXPdXqlQJCQkJGgdFREREpI80KpwqVKiAmJiYXPdHR0ejXLlyGgdFREREpI80Kpxat26N7du34/Lly8ptilN2wcHB2Lt3L1q2bKmdCImIiIj0hEYLYA4ZMgTHjh1D7969lfOcFi9ejJkzZyImJgYVKlTA4MGDtR0rERERkU5pNOJkYWGBwMBAdO/eHU+fPoUQAlFRUXj06BG8vb0RGBiI0qVLaztWIiIiIp3SaMQJAIoXL44pU6ZgypQpiI+Ph1wuR+nSpWFgkOfFyImIiIj0ksaFk1wuR2JiIgCgRIkSMDQ01FZMOrFo0SIsXrwYR44cgaen50fbz549Gz4+Pp8hMiIiItIXn1Q4JSUlYd26dTh69Chu3LihXPjSyMgINjY2aNeuHXr16gVzc/N8CfZzKFeuHAIDA5W/v3jxAsOGDcPgwYPh7u6u3F6lShUdREdERES6pHbhFB0djcGDByMhIQFGRkaoVq0aSpYsiczMTMTHx+Pq1au4evUq1q5di8WLF8PJySkfw84/JiYmKrE/fPgQwNtCqaDmRERERNqhVuH0+PFjDBgwAEIITJ48GZ07d0bRokVV2iQkJCAoKAhLly7FwIEDERQUhAoVKuRL0ERERES6oNZM7lWrViErKwubN29G7969sxVNAFCqVCl8++232LJlCzIyMrBu3TqtB0tERESkS2oVTuHh4fj6669Ru3btj7atWbMmvLy8EB4enufgiIiIiPSJWoXT8+fP1SqaFGxtbfHo0SONgyIiIiLSR2oVTm/evPmkK+XMzc2RlpamcVBERERE+kitwkkI8UkLWyq+t46IiIioMOEy30RERERqUnsdp7CwMNy/f1+ttjdv3tQ4ICIiIiJ9pXbhFBoaitDQULUPzNN1REREVNioVTjNnj07v+PQuYCAAAQEBGTbbmVlxRE0IiIiAqBm4eTl5YUiRYrkdyxEREREek2twsnFxQUNGzaEu7s7mjdvzi+4JSIioi+SWlfVTZ8+HSVKlMCiRYvQtm1btG3bFjNnzsSpU6eQkZGR3zESERER6QW1Rpw6d+6Mzp07Qy6XIzo6GidOnMCJEyewceNGmJqawtXVFS1atEDz5s3x1Vdf5XfMRERERDqh9lV1AGBgYID69eujfv36GDFiBF6+fInjx4/jxIkTmDdvHqZPn47q1aujRYsWaNGiBRo1apRfcRMRERF9dp9UOL2vbNmy6Nq1K7p27QqZTIYLFy7gxIkTOH78ONauXYvr169rK04iIiIinctT4fQuQ0NDuLi4wMXFBSNHjsSzZ8+0dWgiIiIivaBW4bRnzx6NDt65c2eNbkdERESkj9QqnMaNG6eyErgQItvvQPbVwlk4ERERUWGi0crhqampmDdvHsqWLYtvvvkGtWrVglwux71797Bx40akpKRg8uTJ+RIwERERka6oVTh16dJF5fcZM2bA0tISO3bsgLm5uXJ7s2bN0L17d/To0QPHjh1DmzZttBstERERkQ6ptQDm+w4cOIDu3burFE0Kpqam8PHxweHDh/McHBEREZE+0ahwysjIgFwuz3V/SkqKxgERERER6SuNCic7Ozts2bIFCQkJ2fY9fvwYmzZtQv369fMcHBEREZE+0Wgdp6FDh2LAgAFo164dOnXqhCpVqkAIgZiYGOzduxdCCPz444/ajpWIiIhIpzQqnFxdXbF48WL8+uuvWL9+vcq+6tWr49dff4Wtra1WAiQiIiLSFxoVTs+fP4e7uzvc3d1x/fp1PHjwAABQtWpV1K5dW6sBEhEREekLjQqnHj16oGvXrggICECdOnVQp04dbcdFREREpHc0mhweFxeHChUqaDsWIiIiIr2mUeHUsGFDHDlyBBkZGdqOh4iIiEhvaXSqrlWrVpg7dy7atWuHpk2bomzZsjA0NFRpI5FIMHToUK0ESURERKQPNCqcpk2bBuDtd9Zt3749xzYsnIiIiKiw0ahwen8JAvq48hZFkQWJrsMgIvqoihZFdR0Ckd7SqHBq2LChtuMo9PrVk2Y7nUlEpK/kQsBAwj/2iN6nUeGkEBUVheDgYDx8+BAmJiaoWLEi2rVrhwYNGmgrvkJDJpMV2sJJJpPh5s2bsLGxYY4FVGHPD2COn4pFE1HONC6cZs2ahQ0bNkAIobJ948aN6NOnDyZOnJjn4KjgSEtL03UI+a6w51jY8wOYIxHlnUbLEezduxfr169Hw4YNsWnTJpw7dw5nz57Fhg0blNuCg4O1HSsRERGRTmlUOG3evBm2trZYvXo1GjRoAAsLC5QsWRIuLi5YvXo1bG1tsXnzZm3HSkRERKRTGhVON2/eRMeOHXM8h25oaIgOHTrgxo0beQ6OiIiISJ9oVDipQyaT5dehiYiIiHRCo8LJxsYGBw4cyLE4kslk2L9/P2rVqpXn4IiIiIj0iUaFU+/evfHvv//C398fFy9eRFJSEpKSknDhwgX88MMPuH79Onr16qXtWImIiIh0SqPlCL7++mtcunQJmzZtwunTp1X2CSHQs2dP+Pj4aCVAIiIiIn2h8TpOkyZNQvv27XHw4EHExsZCCIEqVaqgbdu2XFmciIiICiWNCqf9+/fD3d0dzs7OcHZ21nZMRERERHpJo8Jp1KhRMDY2hqurK9q0aQMPDw+ULVtW27ERERER6RWNJocvX74cPj4+iImJweTJk9G8eXP06tULq1evxoMHD7QdIxEREZFe0GjEyd3dHe7u7gCAa9eu4ejRozh+/Djmzp2LuXPnwtraGq1bt8aPP/6ozVhJj5mZmek6hHxX2HMs7PkREWlDnhfAtLW1xbBhwxAYGIg///wTVapUwe3bt7Fs2TJtxFdoFNZvYwfe5mZra8scCzB18pPJRa77iIi+FBpfVQcAt27dwpkzZ3DmzBlERUUhJSUFAFCnTh00adJEKwEWFtN2nsXt5690HQaRRqpblsCsnk11HQYRkc5pVDiNHDkSZ8+eRXx8PIQQqFSpEtq2bYsmTZqgcePGKF26tLbjLPDux73GjccJug6DiIiI8kCjwunAgQMAgEqVKmHgwIHo0qULTExMtBoYERERkb7RaI7TnDlz0KlTJ2RmZmLKlClwcXHBt99+i5UrV+LKlSsQgnMhiIiIqPDRaMSpU6dO6NSpEwDgxo0bCA8Px5kzZ7B06VLMnz8fxYsXh6urKxYuXKjVYImIiIh0KU+TwwGgdu3aqF27Nvr374+zZ89i0aJFiI6ORlhYmDbiIyIiItIbeSqcYmNjER4ejvDwcERERCAlJQVFihRBy5Yt4enpqa0YiYiIiPSCRoXT9OnTER4ervxy31KlSqF169bw9PSEm5sbTE1NtR0nERERkc5pVDht3rwZVatWRf/+/eHp6Yn69etDIpFoOzYiIiIivaJx4VS/fn1tx0JERESk1zRajqB3795o3749Vq5ciWfPnmk7JiIiIiK9pFHh9PPPP8PAwADz5s2Dh4cH/P39cfDgQWRkZGg7PiIiIiK9odGpOn9/f/j7++Py5cvYs2cPgoODceLECZQoUQIdO3ZE586d4eDgoO1YiYiIiHQqT8sRODo6wtHREePHj8c///yDkJAQ7Nu3D5s3b4a1tTW6deuGzp07o0SJEtqKl4iIiEhnNDpV9z5jY2M0bdoUHh4ecHFxgRACt2/fxuzZs+Hu7o4//vgDmZmZ2rgrIiIiIp3J04iTXC7HyZMnsXfvXhw5cgTp6ekoWbIk+vXrh27dukEikWDdunVYtWoVXr9+jenTp2srbiIiIqLPTqPC6erVqwgKCkJwcDDi4+NhYGCApk2bomvXrvDw8ICxsbGy7YwZM3D//n3s37+fhRMREREVaBoVTt26dQMAVK1aFf369UOXLl1Qrly5XNuXKlXqg/uJiIiICgKNCqcuXbqga9eucHZ2Vqv9b7/9hqJFi2pyV0RERER6Q6PCafbs2Sq/Z2RkwMjICAYGOc81Z9FEREREhYHGV9UlJiZi+vTpcHNzg5OTEyIiIhAVFYVBgwbh7t272oyRiIiISC9oVDglJiaiZ8+e2Lx5M8zMzCCEAAC8evUKx44dQ58+fRAbG6vVQImIiIh0TaPCafHixXj06BHWrFmDwMBAZeHk6emJlStXIjU1FUuXLtVqoERERES6plHhdPToUfTo0QONGzeGRCJR2de8eXP07NkTERERn3xcX19f2NjYoFevXrm2+emnn2BjY4Nx48Zl29enTx/Y2NggODhYZbuHhwdsbGw++KM43rhx4+Dh4fHBGH19fT85NyIiIir4NJoc/vz5c9SuXTvX/TVr1sTmzZs1CsjAwADR0dF48uQJKlasqLIvLS0Nx44dy/F29+/fR1RUFKRSKbZs2QIvLy/lvsWLF6t8AfGwYcNga2uLIUOGKLeVLl1ao3iJiIjoy6FR4VSmTBk8evQo1/23bt1CqVKlNArI1tYWMTExOHToEPr376+y7+jRoyhSpAgsLCyy3W7nzp2oUKEChgwZghEjRuDOnTuoWbOm8pjvMjExQenSpeHk5KRRjERERPRl0uhUXfPmzbF161Y8fPgw274LFy5g27ZtcHNz0yigokWLokWLFjh48GC2fcHBwWjXrh2MjFTrPZlMhj179sDd3R0eHh6wsLBAYGCgRvdPRERElBuNCqdhw4bB2NgYXbp0wfjx4yGRSLB161YMGjQIvr6+MDMzUzkN9qm8vLxw6dIlPH78WLktOTkZJ06cQMeOHbO1Dw8Px7Nnz9ClSxcUKVIEXl5e2LNnD968eaNxDFlZWTn+KCbCExER0ZdHo8KpfPny2Lp1K+rVq4cTJ05ACIGQkBAcO3YMTk5O2LBhA6ysrDQOyt3dHUWLFsWhQ4eU28LCwlC6dGk0aNAgW/udO3eiRo0aylNv3bp1w6tXr3IctVLHo0ePYGdnl+PPuXPnNDomERERFXwazXECACsrK6xcuRJJSUm4d+8e5HI5rKysUKZMmTwHZWpqCg8PDxw8eBDfffcdAODAgQPw8vLKdhVfQkICjh49Cn9/f7x+/RoAUK1aNVSvXh1bt25Fly5dPvn+LS0tsWzZshz3TZky5ZOPR0RERIWDWoXT1atXYW9vn+M+CwsLODg4qGzLysrC//73P4wdO1bjwNq3b4+hQ4fi4cOHKFasGM6cOYMRI0ZkaxcUFITMzEwsWbIES5Ysybb/xo0bH7wCMCcmJibZclIoVqzYJx2LiIiICg+1TtUNGDAAN27cUOuAsbGx6NmzJ9auXZuXuNC8eXNYWFggJCQEYWFhsLKyyrF427VrF+rWrYv169er/KxcuRKGhobYsmVLnuIgIiIiUlBrxCkpKQnfffcdNmzYoLzEPyf79u3DtGnTkJycDGtr6zwFZmJiAk9PT4SGhqJo0aLo0KFDtjZXrlzBzZs3MW3aNLi6umbb7+bmhn379mHMmDEcKSIiIqI8U2vEac6cOXj16hX8/Pxw7969bPvT0tIwfvx4jBkzBikpKejXrx927dqV5+AUV9dFRETkWDjt3LkTxsbGaNu2bY6379y5M1JSUrBv3748x0JERESkVuHk7e2NOXPmICEhAX5+fipf4Hvjxg34+Phg9+7dKFeuHFavXo0JEybAxMQkz8E1adIExYsXR61atbKNdKWnp+PAgQNo2rRprotttmrVCsWLF8fWrVvzHAsRERGR2lfVdezYERKJBGPGjIGfnx/Wr1+PY8eOYe7cuUhPT4eXlxemTp2K4sWLaxzMhg0bVH43NjZGZGRktnZHjx5V63gmJiY5Lh/wodv/9ttvnxQjERERfTk+aTmCDh06QCKRYPTo0ejYsSPS09Nhbm6OGTNmwNvbO79iJCIiItILn7yOk+LLc8eMGQOJRILVq1fneuk+ERERUWGi0crhXl5e+P3332FgYIAJEyYgISFB23ERERER6R21RpxymidkaWmJnj17YtOmTfj2228xfvx4GBoaqrRxcXHRTpREREREekCtwsnX1zfbV5286+bNm+jfv3+27devX9c8MiIiIiI9o1bh1Llz5w8WTkRERERfArUKp49dok9ERET0JdBocjgRERHRl4iFExEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFExEREZGajHQdwJeiapniyJAJXYdBpJHqliV0HQIRkV5g4fSZTOnaCIaGhroOg0hjMrmAoYFE12EQEekUT9V9JjKZTNch5BuZTIZr164xxwJMnfxYNBERsXAiLUlLS9N1CPmusOdY2PMjItIGFk5EREREamLhRERERKQmFk5EREREamLhRERERKQmLkeQz4R4u3aTTCYr1FdkvftvYVTYcyzs+QHMsTAo7PkBhT9Hfc1PEY/iM/tDJEKdVqSxjIwMXLlyRddhEBER0Uc4ODjAxMTkg21YOOUzuVyOrKwsGBgYQCLhOjhERET6RggBuVwOIyMjGBh8eBYTCyciIiIiNXFyOBEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFExEREZGaWDgRERERqYmFUz47ceIEfHx8ULduXbRs2RIrVqxQa2VSXUtLS0OdOnVgY2Oj8uPg4KBs899//8Hf3x8NGjSAq6srJkyYgNevX6scJzk5GZMnT0bTpk3h5OSE/v37IyYm5nOno+LJkydwdnZGRESEynZt5rNmzRq0atUKDg4O6NSpEw4fPpyvOb0vtxx79OiRrU9tbGwQHR2tbKOvOQohEBgYCG9vb9SrVw+enp6YOXMmkpOTlW0Keh+qk2NB7kOZTIaVK1eidevWcHR0xNdff42goCCVNgW9D9XJsSD34fuGDRsGDw8PlW0FvQ8/SlC+OX/+vLCzsxOjRo0Sx48fF/PmzRM2NjZi6dKlug7toy5evCikUqnYv3+/uHjxovLn0qVLQgghXr16JZo1aya6du0qDh8+LAIDA4Wzs7Po37+/ynH8/f1Fo0aNxM6dO0VISIjw9vYWTZo0EQkJCTrISoiHDx+Ktm3bCqlUKs6ePavcrs18/vrrL1GnTh2xePFicezYMREQECDq1KkjIiMjdZqjTCYTdevWFXPmzFHp04sXL4rk5GS9z3HlypWiTp064o8//hCnTp0SmzdvFq6ursLPz0/I5fJC0Ycfy7Gg9+Hvv/8u7OzsxIoVK8Tp06fF7NmzhVQqFXv37hVCFI7X4cdyLOh9+K49e/YIqVQqWrZsqdxWGPrwY1g45aPvvvtOdO3aVWXb77//LpycnERaWpqOolLP5s2bhZ2dncjIyMhx//Lly0XdunVFXFycctuxY8eEVCoV586dE0IIceHCBSGVSsWxY8eUbeLi4oSTk5NYsmRJ/ibwHplMJnbs2CEaNmwoGjZsmK2o0FY+aWlpwtnZWcyZM0fZRi6Xix49egg/Pz+d5hgTEyOkUqmIiIjI9Rj6mqNMJhPOzs5i6tSpKtuDg4OFVCoVly9fLvB9qE6OBbkPk5OThaOjo/j9999Vtvft21f06NFDCFHwX4fq5FiQ+/BdT58+FS4uLqJ58+YqhVNB70N18FRdPsnIyEBERATatGmjsr1t27ZITU1FVFSUjiJTz/Xr12FtbQ1jY+Mc94eHh6NBgwYoXbq0cluzZs1QrFgxnDhxQtmmaNGiaNq0qbJN6dKl4eLiomzzudy8eRNTp05F586d8fvvv2fbr618Ll26hNevX6v0u0QiQevWrREZGYk3b97kV4ofzfHGjRsAgNq1a+d6DH3NMTk5GV9//TU6duyosr169eoAgNjY2ALfh+rkWJD7sEiRIggMDET//v1VthsbGyMjI0MZe0HuQ3VyLMh9+K6JEyeiadOmaNy4cbbYC3IfqoOFUz6JjY1FZmYmqlWrprK9atWqAIB79+59/qA+wY0bN2BgYID+/fvDyckJDRs2xOTJk5VzLe7cuaN8Q1cwMDCAlZWVMrc7d+7AysoKRkZGKu2qVKmCu3fvfpY8FCpWrIiwsDCMHz8epqam2fZrK587d+4AQI79LpPJ8ODBAy1llN3Hcrx+/TosLCwwa9YsuLq6wsHBAT/88AP+++8/ZRt9zbF48eKYNGkSGjRooLI9NDQUAFCrVq0C34fq5FiQ+9DIyAi1a9dG2bJlIYTAixcvsGLFCpw+fRq9e/dWxlWQ+1CdHAtyHyps374d//77LyZNmpRtX0HvQ3WwcMoniolw5ubmKtuLFSsGACqTPfWNXC7HrVu3cO/ePbRu3Rp//fUXBg0ahP3798Pf3x9yuRyvX79W5vKuYsWKKXNLSkrKlr+iTUpKSr7n8a6SJUuiQoUKue7XVj5JSUkAdNPvH8vx+vXrSEpKQqlSpbBkyRLMmDED9+/fR58+ffDs2TMA+p/juy5cuIC//voLrVq1Qq1atQpFH77v/RwLSx/u27cPbm5umDdvHpo3bw4vLy8AheN1qJBbjgW9Dx89eoTZs2djypQpKqNKCoWpD3Nj9PEmpAm5XA7g7dBiTj727cu6JITAihUrULZsWdSsWRMA4OLigrJly2L06NE4efIkgJxzE0Iot8vl8lzzz227LmkjH0W/53QcQLf9PmrUKAwZMkQ5ouHs7Iz69eujffv2WL9+PUaPHl1gcoyKisKgQYNQpUoVzJw5M1uM78dVEPswpxwLSx/WrVsXGzduxN27d7Fw4UL06tULO3bsUInx/bgKWh/mlmNB7kMhBCZMmIAWLVqgbdu2ubYrLH2YGxZO+aR48eIAslfFimo6p2pbXxgaGsLV1TXbdnd3dwBv59KYm5vnWPGnpqYqRz0sLCwQFxeXrU1KSgosLCy0G3QeaSsfRb+npKSgRIkSKsdRHENX6tSpk21b5cqVUbNmTeW8i4KQ44EDBzBu3DhUr14dq1atQsmSJQEUrj7MLcfC0odVq1ZF1apV4eLigsqVK+Pbb79FSEhIoerD3HL8+uuvs7UtKH24adMm3Lx5E/v27UNWVhaA/ytksrKyYGBgUKj6MDf6O+xRwFWpUgWGhoa4f/++ynbF79bW1roISy3Pnj3Dtm3b8PTpU5Xtisl4pUqVQvXq1bOdY5bL5Xj48KEyt+rVq+Phw4fZ/nJ48OCBciRLX2grH8W5/Zz63cTEBJUrV86vFD4oMzMTu3btUlknRuHNmzcoVaoUAP3P8e+//8bIkSPh5OSETZs2wdLSUrmvsPRhbjkW9D6Mi4vD7t27s31gKtaGe/r0aYHvw4/lGBsbW6D7MCQkBAkJCXBzc4OdnR3s7OywZ88ePHr0CHZ2dliyZEmB70N1sHDKJ0WKFIGzszPCwsJUFrwMCQlB8eLF4ejoqMPoPiwjIwOTJk1CYGCgyvbg4GAYGBigQYMGaNq0Kc6dO4f4+Hjl/pMnTyIlJUV5pYSbmxtSUlKUp/YAID4+HufOnYObm9vnSUZN2sqnXr16KFq0KEJCQpRthBAICwtDw4YNYWJi8pkyUmVsbIxFixZh7ty5Ktv//fdfPHjwQDnCqM85bt26FXPnzkW7du2watWqbH9xFoY+/FCOBb0PU1NTMW7cOGzfvl1luyJOGxubAt+HH8vR3t6+QPfhtGnTsGPHDpWfli1bwtLSEjt27ECPHj0KfB+q5bMtfPAFOn36tLCxsREBAQHi2LFjYv78+cLGxkb89ddfug7to0aPHi3s7OzE0qVLxenTp8WiRYuEnZ2dmD59uhDi7Zobrq6u4uuvvxahoaFi27ZtwsXFRXz//fcqx+nbt69wcXER27ZtE6GhocLb21s0a9ZMJCYm6iItIYQQZ8+ezbbGkTbzWbhwobCxsRHz589XLtpmZ2cnoqKidJrj9u3bhVQqFWPHjhXh4eEiMDBQNG3aVHTq1ElkZmbqdY7Pnz8Xjo6OomXLluLcuXPZFg6Mi4sr8H2oTo4FuQ+FEGLMmDHC3t5euTjkypUrRb169cR3330n5HJ5ge9DdXIs6H34vrFjx6qs41QY+vBjWDjls9DQUNGxY0dhZ2cnPDw8xKpVq3QdklrevHkjFi9eLNq0aSPs7e2Fp6enWLFihcjKylK2uXnzpvDz8xOOjo6icePGYtKkSSIpKUnlOImJiWLcuHHC2dlZ1K9fX3z//ffizp07nzsdFTkVFUJoLx+5XC6WLFkiWrRoIRwcHESXLl3E8ePH8z2vd+WW4/79+0WXLl1E3bp1RaNGjcSkSZOyreKujzkqPmxy+9m5c6cQomD3obo5FtQ+FEKI9PR0sXTpUuX7SsuWLcX8+fNFenq6sk1B7kN1cyzIffi+9wsnIQp+H36MRIgC8MVpRERERHqAc5yIiIiI1MTCiYiIiEhNLJyIiIiI1MTCiYiIiEhNLJyIiIiI1MTCiYiIiEhNLJyIiIiI1MTCiYiIiEhNRroOgIg0t2vXLowfP/6j7W7evPkZoiF13Lp1C5MnT8a1a9dgamqK5cuXo379+h+8TVRUFLZt24aLFy/i2bNnMDExQZ06ddC1a1d06tQJEonkM0VPRCyciAqB1q1bo3Xr1roOg9Qwbtw4XLt2DYMHD0bFihVRq1atXNtmZmZi1qxZ2Lx5M6pVq4YOHTqgUqVKiI+Px4EDBzB27FicOHECc+fOhaGh4WfMgujLxcKJqBCwsbFBp06ddB0GqeHWrVuoXbs2hg8f/tG28+fPx+bNm+Hr64sJEybAwOD/Zlf4+/tj9OjR2L9/P6pUqYIRI0bkY9REpMA5TkREn1FmZiYsLCw+2u6///7D6tWr4eDggIkTJ6oUTQBgYGCAadOmwcLCAoGBgUhPT8+vkInoHSyciL4gHh4eGDhwIJYtWwZnZ2fUr18fmzZtAgDI5XJs2LABnTp1gqOjI5ydnfH999/j/Pnz2Y4TGxuLkSNHolGjRqhXrx4GDRqE2NhY1KtXD+PGjVO28/X1hY2NTbbbR0REwMbGBosWLVLZ/t9//+Hnn39G48aNYW9vjzZt2mDBggV48+aNSjsbGxtMnz4dISEh8PHxgaOjIxo1aoRx48bhxYsX2e4vODgYffr0QYMGDeDq6go/Pz+cOXNGmUvt2rXh7++f7XYZGRlwcXFB//79P/i4ymQylceufv366NevH44fP65ss2jRIuVjERkZCRsbG5XH6n1BQUEQQqBv3765tjE3N8f27dtx7NgxFClSRLn96dOnmDhxIpo3bw57e3s0b94cEydOxNOnT1Vu/yn986HnTnBwMHr16oWGDRvCyckJnTp1wurVqyGXy1WO+/r1a8yZMweenp6wt7eHm5sbxo8fj8ePH6u0S0tLw+zZs9GuXTs4OjrC1dUVAwcOzPG5SPS58VQdUSGQlpaG+Pj4HPeVLl1a5ffIyEj8+++/+PHHH5GQkIDGjRsDAEaNGoUDBw6gbdu26NGjB169eoVdu3bB19cX8+bNQ7t27QAAjx49Qrdu3ZCWloY+ffqgUqVKOHToEHx9fZGZmalxDpcvX8a3334Lc3Nz9OnTB6VLl0Z0dDSWL1+OM2fOYP369SrFwbFjx7B792706tULvXr1wtmzZ7F79248fPgQGzduVLZbuHAhlixZgtq1a2PQoEEwMTHB5s2bMWDAACxbtgwtWrRAw4YNcerUKcTFxaFMmTLK2x4+fBivX7+Gj49PrnHL5XIMGzYMR48ehaurK0aOHImUlBTs3r0b/v7+GDduHPr374/WrVujSpUqGDNmDGrUqIFBgwahSpUqH3w8AKBBgwYffNyqV6+u8vudO3fQp08fJCcno0ePHqhVqxZu3bqF7du348iRI9i8eXO226grp+dOaGgofv75ZzRt2hTDhw+HgYEBDh06hDlz5iAuLg6jR48GALx69Qq9evXC48eP0b17d1hbW+P+/fvYunUr/vnnHwQGBqJq1aoAgJ9//hmnTp1Cnz59UKNGDbx8+RKbNm2Cn58fduzYgdq1a2sUP5FWCCIqsHbu3CmkUukHf97VsmVLIZVKxbFjx1S2BwcHC6lUKv766y+V7cnJyaJdu3bC1dVVpKamCiGE+Pnnn7MdIysrS/zwww9CKpWKsWPHKrf37ds3WwxCCHH27FkhlUrFwoULhRBCyOVy0aFDB9GiRQuRkJCg0nbbtm1CKpWKlStXKrcpcouOjlZp6+fnJ6RSqbh7964QQoh79+6JOnXqiL59+4r09HRlu5cvX4oGDRoIHx8fIYQQe/bsEVKpVKxdu1bleD/88IOoX7++SEtLy5aDwu7du5V5y+Vy5fakpCTRrl07YWtrK+7fv68Se9++fXM9noKXl5eQSqXizZs3H237rn79+gmpVCpOnz6tsj08PDzbfavbP0Lk/tzx9/cXTk5OQiaTKbdlZWWJvn37ioEDByq3TZ06Vdja2ooLFy6o3P7WrVvC3t5efP/990IIIeLi4oRUKhVTpkxRaRcdHS3atGkjtm3bpuYjQZQ/eKqOqBDo1KkT1qxZk+PP+0xMTNC0aVOVbQcOHAAAtG3bFvHx8cqf9PR0tGnTBgkJCTh37hzkcjmOHj2KWrVqoUWLFsrbGxoaYtiwYRrHf/PmTdy+fRstWrSAXC5XiaFly5YoUqQIwsLCVG5TrVo11K1bV2Wbg4MDAODly5cAgKNHj0Imk8HPzw8mJibKdmXKlMHmzZuVp6Latm0LCwsLBAUFKdu8ePEC4eHh6NChA0xNTXON/dChQwCAESNGqCwLYG5ujkGDBiErKwshISGf/JgorpLLyspS+zbx8fGIiIhA48aNlSOJCk2bNkXjxo0RGRmJuLi4T44HyPm5U6FCBaSmpmLatGm4du0ahBAwNDTEhg0bsHz5cgCAEALBwcGoUaMGqlatqtK/ZcqUgZOTE06dOoWUlBSYm5vDwsICBw8exLZt25SnXuvWrYuQkBB0795do9iJtIWn6ogKgcqVK6NJkyZqtS1VqhSMjFRf+nfv3gUAtGrVKtfbPXr0CAkJCUhNTVWeUnmXVCr9hIhVKe5/69at2Lp1a673/66yZctma6MojmQyGQDg4cOHAIAaNWp8MF5TU1N4eXkhMDAQd+7cQc2aNbFv3z7IZLIPnqYDgAcPHsDc3BwVKlTI9T4UcXyK8uXL4+bNm3j58iWKFSum1m0ePnwIIQSsra1z3F+rVi2cOXMGDx8+VDklqa6cnjsBAQG4fv26su9Kly6NRo0aoVWrVmjbti2MjIyQkJCAxMREJCYmZivo3vX06VPUrFkTv/32G8aPH49JkyYBePs4urm5wdvbG7a2tp8cN5E2sXAi+sK8/8EHvC00ihUrhsWLF+d6u4/NizE2NlZ7LSFFYaOgmETcp0+fXIu39+N+/yqznCjmXKmzQGS3bt0QGBiIvXv34qeffsLu3btRvXp1ODk5ffB2Qohcj6/I893RLnW5uLjgxIkTiIqKyrFQVZg/fz7u3r2LgIAACCEA5J6v4nH+WDzv949CTs+dsmXLYtu2bbhy5QqOHz+Os2fPIiwsDMHBwXBycsLGjRuVx2vQoMEHRyYVxWerVq3QtGlTnDx5EuHh4YiIiMDq1auxZs0aTJgwAf369ftg/ET5iYUTEcHKygp3795F7dq1s00mv379Op4/fw4zMzOUKFECJUuWRExMTLZjxMbGZvvAVRRSb968UTnd9f6Vb1ZWVsr/vz9yJpfLERISgsqVK2uUF/B2ROv9wm/t2rWIiYnBxIkTYWpqCkdHR0ilUhw6dAidO3fGrVu3MHLkyI/eR5UqVfDff//h6dOn2UadFI9TpUqVPjn29u3b488//8SWLVvg4+OTYzGUkpKCrVu3IiUlBZMnT1aOIt2+fTvHY96+fRsSiQTly5cHoH7/5EYIgdu3b+PNmzdwdHSEg4MDhg0bhuTkZIwdOxaHDx9GeHg4mjdvjqJFi+L169c5joyeOnUKBgYGKFKkCJKTk3Hz5k1YWVmhTZs2aNOmDQDgxo0b8PPzw5IlS1g4kU5xjhMRoW3btgCABQsWqGxPTk7GiBEjMHToUKSnp0MikaB9+/a4d+8egoODVdquWrUq23HLlSsHALh69apym1wux969e1Xa2dvb46uvvsLu3bvx4MEDlX2BgYEYMWIEdu7c+cl5eXp6QiKRYNOmTSpzhRITE/HXX3/h8uXLKgWDj48P7t27h+XLl8PQ0FCtRUXffewUIz4AkJqaipUrV8LQ0PCDp0BzU7lyZfTt2xdXrlzBrFmzsl3an56ejtGjRyMxMRH9+vVD2bJlUbp0aTRs2BBnzpxRLregcObMGURERKBhw4bK4ljd/smNRCJBQEAABg8ejKSkJOV2c3Nz5TIHhoaGysfg9u3byvl0Cjdu3MDAgQMxc+ZMGBkZ4datW+jduzeWLl2q0s7a2hoWFhY5jnoRfU58BhIRfHx8cOjQIQQGBuLBgwfw8PBAVlYWtm/fjnv37mH06NHKUYoRI0bgzJkzGD16NKKiomBtbY3w8HCEh4dnO27Xrl0RFBSEn376CX5+fjAzM8OBAweQmJio0s7Q0BAzZszAwIED4ePjg169eqFKlSq4cuUKdu7ciSpVqmDIkCGfnFfNmjXh7++PFStW4JtvvkGHDh0gl8uxbds2vHr1Cn/++adK+06dOuF///sf9uzZg+bNmytz/pBOnTrh0KFD2L17Nx4/fgxPT0+kpaVh9+7duHfvHkaNGqXRaBkAjBw5Es+ePcP69etx8uRJdOjQAeXLl8fjx4+xd+9ePHr0CK1bt1ZZNXzKlCno3bs3fvjhB/Ts2RPW1taIiYlBYGAgSpYsiSlTpijbqts/HxIQEICRI0eiZ8+e8PHxQYkSJXDjxg0EBgaiTp06yhGmUaNG4dy5cxg1ahTCw8NRt25dPHnyBFu3boWhoaEyrvr168PNzQ1bt27F69ev0bBhQ8hkMoSEhCA2NhZjx47V6LEk0hYWTkQEQ0NDLF++HOvWrUNQUBD++OMPmJmZoWbNmli0aJHydAkAlCxZElu2bMGCBQtw6NAhJCcnw9nZGQsXLsTAgQNVjuvq6or//e9/WLVqFf78808UL14cbdq0wYABA+Dp6anStkmTJti2bRuWLVuGnTt3IikpCRUqVEDv3r0xcOBAWFpaapTbzz//jBo1amDjxo2YP38+zMzM4ODggLlz5yqvwlMoXbo0WrZsidDQ0I9OCn/3sVu6dCnWrVuHPXv2KB87BwcH/PLLL2jevLlGcQNv5yLNnz8fHTp0wPbt27F7927laVNbW1v8/PPP6Nixo8ptrK2tsWvXLixZsgQhISEIDAyEpaUlunXrhsGDB6sUg5/SP7np2LEjzMzMsHbtWqxatQpJSUmoWLEifH19MXjwYOUIUfny5bFz504sW7YMR48exb59+1CqVCk0bNgQgwcPVpn0vXDhQqxevRrBwcE4duwYAKB27dr4448/4O3trfHjSaQNEvHu2DIRkYYePnwIT09PdOnSBb/99puuw9HYjz/+iMjISJw4cUKjSd1EVLhxjhMR0f8XGxuLo0ePwsfHh0UTEeWIp+qI6Iu3detWnD17FpGRkShSpAj8/Px0HRIR6SmOOBHRF8/Q0BDHjx9H8eLFsWTJErUmhRPRl4lznIiIiIjUxBEnIiIiIjWxcCIiIiJSEwsnIiIiIjWxcCIiIiJSEwsnIiIiIjWxcCIiIiJSEwsnIiIiIjWxcCIiIiJSEwsnIiIiIjX9P1QvXzHMA0n/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(y='keyword', data=coursera_df, order=coursera_df['keyword'].value_counts().index, palette='GnBu')\n",
    "plt.title('Coursera Course Keywords Frequency', fontsize=16)  \n",
    "plt.xlabel('Frequency of Courses', fontsize=14)\n",
    "plt.ylabel('Keyword/Department Names', fontsize=14) \n",
    "plt.xticks(fontsize=12) \n",
    "plt.yticks(fontsize=12) \n",
    "plt.tight_layout() \n",
    "f1 = plt.gcf()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot, we can see that there are a larger number of Data Science (>4,000) courses in our dataset. This is closely followed by Computer Science courses at about 3900 courses. Furthermore, Mathematics has the lowest number of courses in the dataset. This is important to note when we look at `num_reviews` and `ratings` of courses, so that we appropriately _normalize the data_ before blunt comparisions. In the next section, we will analyse the overall proportion of Coursera classes that teach each individual (highly demanded) skill. \n",
    "\n",
    "_Note: a single course can be tagged with multiple keywords._\n",
    "\n",
    "Based on this information, we wil now split up our DataFrame `coursera_df` into subsets. This classifying information is stored in the `keyword` column, which will be used to split the dataframe in the following cell.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>course_title</th>\n",
       "      <th>rating</th>\n",
       "      <th>level</th>\n",
       "      <th>skill</th>\n",
       "      <th>keyword</th>\n",
       "      <th>num_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python syntax and semantics</td>\n",
       "      <td>CS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>basic programming language</td>\n",
       "      <td>CS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>computer programming</td>\n",
       "      <td>CS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>CS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python for Everybody Specialization</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>json</td>\n",
       "      <td>CS</td>\n",
       "      <td>209311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2736</th>\n",
       "      <td>Product Management: Foundations &amp; Stakeholder ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>product planning</td>\n",
       "      <td>CS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2736</th>\n",
       "      <td>Product Management: Foundations &amp; Stakeholder ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>communication</td>\n",
       "      <td>CS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2736</th>\n",
       "      <td>Product Management: Foundations &amp; Stakeholder ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>leadership</td>\n",
       "      <td>CS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2736</th>\n",
       "      <td>Product Management: Foundations &amp; Stakeholder ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>product management</td>\n",
       "      <td>CS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2736</th>\n",
       "      <td>Product Management: Foundations &amp; Stakeholder ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>stakeholder management</td>\n",
       "      <td>CS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3901 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           course_title  rating     level  \\\n",
       "0     Programming for Everybody (Getting Started wit...     4.8  beginner   \n",
       "0     Programming for Everybody (Getting Started wit...     4.8  beginner   \n",
       "0     Programming for Everybody (Getting Started wit...     4.8  beginner   \n",
       "0     Programming for Everybody (Getting Started wit...     4.8  beginner   \n",
       "3                   Python for Everybody Specialization     4.8  beginner   \n",
       "...                                                 ...     ...       ...   \n",
       "2736  Product Management: Foundations & Stakeholder ...     0.0  beginner   \n",
       "2736  Product Management: Foundations & Stakeholder ...     0.0  beginner   \n",
       "2736  Product Management: Foundations & Stakeholder ...     0.0  beginner   \n",
       "2736  Product Management: Foundations & Stakeholder ...     0.0  beginner   \n",
       "2736  Product Management: Foundations & Stakeholder ...     0.0  beginner   \n",
       "\n",
       "                            skill keyword  num_review  \n",
       "0     python syntax and semantics      CS      225830  \n",
       "0      basic programming language      CS      225830  \n",
       "0            computer programming      CS      225830  \n",
       "0                          python      CS      225830  \n",
       "3                            json      CS      209311  \n",
       "...                           ...     ...         ...  \n",
       "2736             product planning      CS           0  \n",
       "2736                communication      CS           0  \n",
       "2736                   leadership      CS           0  \n",
       "2736           product management      CS           0  \n",
       "2736       stakeholder management      CS           0  \n",
       "\n",
       "[3901 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CS_courses = coursera_df[coursera_df['keyword'] == 'CS']     # CS subset\n",
    "DS_courses = coursera_df[coursera_df['keyword'] == 'DS']     # DS subset \n",
    "IT_courses = coursera_df[coursera_df['keyword'] == 'IT']     # IT subset\n",
    "MATH_courses = coursera_df[coursera_df['keyword'] == 'MATH'] # Math subset\n",
    "CS_courses # see what it looks like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now define our function `calculate_skill_proportions` which takes in a dataframe of courses from the same sub category. This function aims to analyze how the teaching of the top 5 job skills (`python`, `sql`, `data analysis`, `machine learning`, `communication`) is distributed across different course subcategories within our Coursera dataset. In this function, we will filter the `sub_categories` DataFrame to only include courses that teach the top 5 skills. We count the numbers of such courses and store the value in `skill_counts`. Next, the total number of unique courses is stored in the `total_courses` variable. Finally, we calculate the ratio of _courses teaching each skill_.\n",
    "\n",
    "This function is applied to each of the `subcategory_courses` dataframes - namely, `CS_courses`, `DS_courses`, `IT_courses`, and `MATH_courses` dataframes that we created in the previous cell.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "python              0.031669\n",
       "data analysis       0.019488\n",
       "machine learning    0.019488\n",
       "sql                 0.006699\n",
       "communication       0.003654\n",
       "Name: skill, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_skill_proportions(subcategory_courses):\n",
    "    skill_counts = subcategory_courses[subcategory_courses['skill'].isin(top_5_js)]['skill'].value_counts()\n",
    "    total_courses = coursera_df['course_title'].nunique()\n",
    "    skill_proportions = skill_counts / total_courses\n",
    "    return skill_proportions\n",
    "\n",
    "# applying the function to all subcategories \n",
    "CS_skill_proportions = calculate_skill_proportions(CS_courses)\n",
    "DS_skill_proportions = calculate_skill_proportions(DS_courses)\n",
    "IT_skill_proportions = calculate_skill_proportions(IT_courses)\n",
    "MATH_skill_proportions = calculate_skill_proportions(MATH_courses)\n",
    "MATH_skill_proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example above is of the `MATH_skill_proportions` dataframe. We now have the proportions of `MATH`-tagged courses that teach each of the top 5 skills. Similarly, we also have data for `DS`, `CS`, and `IT`. We will combine them into one DataFrame, called `skill_proprtions_df`, and use it for visualization.  If the skills don't match up exactly across categories (i.e., some skills might not be present in all categories), we fill in missing values with 0, so that the plot can be generated without errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>CS</th>\n",
       "      <th>DS</th>\n",
       "      <th>IT</th>\n",
       "      <th>MATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>communication</th>\n",
       "      <td>0.006090</td>\n",
       "      <td>0.010353</td>\n",
       "      <td>0.003654</td>\n",
       "      <td>0.003654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data analysis</th>\n",
       "      <td>0.037759</td>\n",
       "      <td>0.070646</td>\n",
       "      <td>0.012789</td>\n",
       "      <td>0.019488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine learning</th>\n",
       "      <td>0.042631</td>\n",
       "      <td>0.051766</td>\n",
       "      <td>0.005481</td>\n",
       "      <td>0.019488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>0.068819</td>\n",
       "      <td>0.064555</td>\n",
       "      <td>0.015834</td>\n",
       "      <td>0.031669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sql</th>\n",
       "      <td>0.020706</td>\n",
       "      <td>0.025579</td>\n",
       "      <td>0.010353</td>\n",
       "      <td>0.006699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CS        DS        IT      MATH\n",
       "communication     0.006090  0.010353  0.003654  0.003654\n",
       "data analysis     0.037759  0.070646  0.012789  0.019488\n",
       "machine learning  0.042631  0.051766  0.005481  0.019488\n",
       "python            0.068819  0.064555  0.015834  0.031669\n",
       "sql               0.020706  0.025579  0.010353  0.006699"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skill_proportions_df = pd.DataFrame({\n",
    "    'CS': CS_skill_proportions,\n",
    "    'DS': DS_skill_proportions,\n",
    "    'IT': IT_skill_proportions,\n",
    "    'MATH': MATH_skill_proportions\n",
    "}).fillna(0)\n",
    "\n",
    "skill_proportions_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now visualize this data using a _stacked_ bar chart. This will help us visualize the proportion of each `skill` within each `keyword` category (CS, DS, IT, MATH) in a way that's easy to compare both across keywords and within skills. Here each of the five skills will be represented by a separate bar. Furthermore, within each bar, the proportions of the categories (acquired by `keyoword` tag) teaching each of the top skills would be stacked on top of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_colors = plt.cm.Pastel2.colors[:4] \n",
    "skill_proportions_df.plot(kind='bar', stacked=True, figsize=(6, 4), color=plot_colors)\n",
    "plt.title('Proportion of Courses Teaching Top Skills by Category')\n",
    "plt.xlabel('Skill')\n",
    "plt.ylabel('Proportion of Courses')\n",
    "plt.xticks(rotation=360)\n",
    "plt.legend(title='Category')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's break this down:\n",
    "1. The length of the bar is the overall proportion of courses that teach a certain skill. This is consistent with the plot we created earlier. **Python** is taught in about **18%** of the courses, **data analysis** in about **14%**, **machine learning** in about **11%**, **sql** in about **6%** and **communication** in about **2.3%** of the courses.\n",
    "2. Now, within each bar, the height of each sub-section represents which department a certain proportion of courses come from. So, for instance, in the bar for python, most of the courses come from `Computer Science` or `Data Science`. Only a very small amount of `IT` and `MATH` courses teach python.\n",
    "3. As expected, a large chunk of `DS` courses teach **data analysis**, and considerable amount of **machine learning** is taught through `DS` and `CS` classes. **SQL** is taught in more data science and CS courses as well. \n",
    "4. Unfortunately, a very small number of courses from each of the departments teach **communication** skills, which seems to be important for working in a Data Science Job in 2024.\n",
    "\n",
    "\n",
    "From this chart, we can also notice that to apply/land a Data Science job, coursework not only from the Data Science department is needed. In fact, a significant amount of Computer Science courses teach relevant, and essential skills. However, a relatively smaller amount of Mathematics and Information Technology courses teach the highly demanded skills. \n",
    "\n",
    "In the next section, we will explore how students interact with these courses that teach a highly demanded skill based on the number of reviews the course gets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.4: Popularity of Courses that Teach Highly Demanded Skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will look at the relationship between the number of reviews a course gets, and whether or not it is teaching a highly demanded data science job skill. To do so, we will first create a subset of our dataset that contains only those courses that teach a top-5-skill. We will use `.isin()` to filter the DataFrame by `top_5_js`. We will store this in `courses_with_top_skills`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>course_title</th>\n",
       "      <th>rating</th>\n",
       "      <th>level</th>\n",
       "      <th>skill</th>\n",
       "      <th>keyword</th>\n",
       "      <th>num_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>CS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>DS</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Programming for Everybody (Getting Started wit...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>MATH</td>\n",
       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python for Everybody Specialization</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>CS</td>\n",
       "      <td>209311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Python for Everybody Specialization</td>\n",
       "      <td>4.8</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>DS</td>\n",
       "      <td>209311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>Predict Ad Clicks Using Logistic Regression an...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>python</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>Predict Ad Clicks Using Logistic Regression an...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>Train Machine Learning Models</td>\n",
       "      <td>0.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966</th>\n",
       "      <td>Logistic Regression for Classification using J...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>beginner</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>Data Analysis with Python Project</td>\n",
       "      <td>0.0</td>\n",
       "      <td>intermediate</td>\n",
       "      <td>machine learning</td>\n",
       "      <td>MATH</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>867 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           course_title  rating         level  \\\n",
       "0     Programming for Everybody (Getting Started wit...     4.8      beginner   \n",
       "1     Programming for Everybody (Getting Started wit...     4.8      beginner   \n",
       "2     Programming for Everybody (Getting Started wit...     4.8      beginner   \n",
       "3                   Python for Everybody Specialization     4.8      beginner   \n",
       "4                   Python for Everybody Specialization     4.8      beginner   \n",
       "...                                                 ...     ...           ...   \n",
       "2962  Predict Ad Clicks Using Logistic Regression an...     0.0      beginner   \n",
       "2962  Predict Ad Clicks Using Logistic Regression an...     0.0      beginner   \n",
       "2964                      Train Machine Learning Models     0.0  intermediate   \n",
       "2966  Logistic Regression for Classification using J...     0.0      beginner   \n",
       "2969                 Data Analysis with Python Project      0.0  intermediate   \n",
       "\n",
       "                 skill keyword  num_review  \n",
       "0               python      CS      225830  \n",
       "1               python      DS      225830  \n",
       "2               python    MATH      225830  \n",
       "3               python      CS      209311  \n",
       "4               python      DS      209311  \n",
       "...                ...     ...         ...  \n",
       "2962            python    MATH           0  \n",
       "2962  machine learning    MATH           0  \n",
       "2964  machine learning    MATH           0  \n",
       "2966  machine learning    MATH           0  \n",
       "2969  machine learning    MATH           0  \n",
       "\n",
       "[867 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "courses_with_top_skills = coursera_df[coursera_df['skill'].isin(top_5_js)]\n",
    "courses_with_top_skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will count the number of courses that teach each of the top skills. We will use `.groupby()`, to accumulate the number of unique courses (using `.nunique()`) that teach each of the individual skills. We re-indexed the skills so they appear in the same order as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skill\n",
       "python              160\n",
       "sql                  60\n",
       "data analysis       143\n",
       "machine learning    117\n",
       "communication        27\n",
       "Name: course_title, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of courses teaching each of the top 5 job skills\n",
    "courses_per_skill = coursera_df[coursera_df['skill'].isin(top_5_js)].groupby('skill')['course_title'].nunique()\n",
    "courses_per_skill = courses_per_skill.reindex(top_5_js, fill_value=0)  # Ensure all top 5 skills are included, even if they have 0 courses\n",
    "courses_per_skill"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will group courses in the DataFrame `courses_with_top_skills` by the `skill` column. Furthermore, for every unique skill,  we will sum up the values in the `num_review` column.  This will result is a Series where each index is a unique skill and each value is the total number of reviews for courses related to that skill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skill\n",
       "python              3453777\n",
       "sql                 1069662\n",
       "data analysis       1373949\n",
       "machine learning     897939\n",
       "communication         50002\n",
       "Name: num_review, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the number of reviews per skills for the top 5 skills\n",
    "reviews_per_skill = courses_with_top_skills.groupby('skill')['num_review'].sum()\n",
    "reviews_per_skill = reviews_per_skill.reindex(top_5_js, fill_value=0)\n",
    "reviews_per_skill"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that the total number of reviews on courses teaching python is the greatest. However, we cannot directly use this data for inference since the number of courses teaching each skill is different. Therefore, we need to see courses teaching which exact skill have proportionally higher number of reviews. To achieve this, we will first average the number of reviews by dividing `reviews_per_skill`  by `courses_per_skill`. We will store the result in `average_reviews_per_course`. \n",
    "\n",
    "Furthermore, we will combine `reviews_per_skill`, `courses_per_skill` and `average_reviews_per_course` into one DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Total Reviews</th>\n",
       "      <th>Number of Courses</th>\n",
       "      <th>Average Reviews per Course</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skill</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>3453777</td>\n",
       "      <td>160</td>\n",
       "      <td>21586.106250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sql</th>\n",
       "      <td>1069662</td>\n",
       "      <td>60</td>\n",
       "      <td>17827.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data analysis</th>\n",
       "      <td>1373949</td>\n",
       "      <td>143</td>\n",
       "      <td>9608.034965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine learning</th>\n",
       "      <td>897939</td>\n",
       "      <td>117</td>\n",
       "      <td>7674.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>communication</th>\n",
       "      <td>50002</td>\n",
       "      <td>27</td>\n",
       "      <td>1851.925926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Total Reviews  Number of Courses  Average Reviews per Course\n",
       "skill                                                                         \n",
       "python                  3453777                160                21586.106250\n",
       "sql                     1069662                 60                17827.700000\n",
       "data analysis           1373949                143                 9608.034965\n",
       "machine learning         897939                117                 7674.692308\n",
       "communication             50002                 27                 1851.925926"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average number of reviews per course for each skill\n",
    "average_reviews_per_course = reviews_per_skill / courses_per_skill    \n",
    "# combining into one dataframe for visualization\n",
    "combined_data = pd.DataFrame({\n",
    "    'Total Reviews': reviews_per_skill,\n",
    "    'Number of Courses': courses_per_skill,\n",
    "    'Average Reviews per Course': average_reviews_per_course\n",
    "})\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we must normalize the data before we visualize it so that we are able to compare all of them on the same scale. We will rescale the values in the dataset so that values are bound between 0 and 1, allowing for easier comparison between variables that may originally have different scales.\n",
    "\n",
    "We shift every value in the DataFrame by doing `(combined_data - combined_data.min())` This shifts the values so that the lowest value is 0. Then, we divide by the range, which is given by `(combined_data.max() - combined_data.min())`  This step essentially does the re scaling. After this, the smallest value in each column will be 0, and the biggest value will be 1. \n",
    "\n",
    "The result, `combined_data_normalized`, is a new DataFrame where each value indicates the relative position of the original value within its column's range, rescaled to be between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize the data for visualization to compare the metrics on the same scale\n",
    "combined_data_normalized = (combined_data - combined_data.min()) / (combined_data.max() - combined_data.min())\n",
    "combined_data_normalized\n",
    "\n",
    "palette = plt.cm.Pastel2.colors[:3]\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "combined_data_normalized.plot(kind='bar', ax=ax, width=0.7, color=palette )\n",
    "\n",
    "# Add title and labels to the plot\n",
    "ax.set_title('Comparison of Total Reviews, Number of Courses, and Average Reviews per Course for Top 5 Job Skills', fontsize=14)\n",
    "ax.set_xlabel('Skill', fontsize=12)\n",
    "ax.set_ylabel('Normalized Score', fontsize=12)\n",
    "ax.legend(loc='upper right', fontsize=10)\n",
    "plt.xticks(rotation=360)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph, we can learn about the popularity of courses teaching each of the highly demanded skills:\n",
    "\n",
    "- `python`: Has the highest number of total reviews and courses, Its reviews per skill is also high indicating a very high demand for python courses.\n",
    "- `sql`: Has lower total reviews and courses than Python, and also a lower average reviews per course.\n",
    "- `data analysis`: Has moderate total reviews and a higher average reviews per course, indicating that while there may be fewer courses, they are well-reviewed.\n",
    "- `machine learning`: Has a very high average number of reviews per course, suggesting that while there may be fewer courses, they are very actively reviewed, possibly indicating high engagement or popularity of these courses.\n",
    "- `communication`: Has the lowest normalized score across all three metrics, suggesting fewer courses, total reviews, and average reviews per course in comparison to the technical skills listed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 4: Significance Testing --  Are at least half of Coursera's Tech courses Industry-relevant?\n",
    "Do 50% of the tech related courses that coursera offer teach at least the top 1 of the top 5 skills demanded by the jobs dataset?\n",
    "\n",
    "- **Null hypothesis:** Proportion of coursera courses teaching top skills is less than or equal to 50%\n",
    "- **Alternative hypothesis:** Proportion of coursera courses teaching top skills is greater than 50%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin with getting the number of tech-related Coursera courses that teach one of the top five skills. We also get the total number of tech-related Coursera courses. Using these two values, we get the proportion of Coursera tech courses that are \"industry relevant\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of tech-related courses that Coursera offers is 1642\n",
      "The total number of tech-related courses that Coursera offers, which also teach a top 5 skills is 680\n",
      "41% of Coursera's Tech courses teach one of the top five skills!\n"
     ]
    }
   ],
   "source": [
    "#get total number of tech-related cousera courses\n",
    "total_courses_num = coursera_df['course_title'].nunique()\n",
    "#get the courses that teach at least 1 of the top 5 skills\n",
    "courses_that_teach_top_5 = courses_data[courses_data[\"skill\"]\n",
    "                                        .apply(lambda x: any(skill in x for skill in top_5_js))]\n",
    "print(\"The total number of tech-related courses that Coursera offers is \" + str(total_courses_num))\n",
    "print(\"The total number of tech-related courses that Coursera offers, which also teach a top 5 skills is \" \n",
    "                      + str(courses_that_teach_top_5.shape[0]))\n",
    "print(str(int((courses_that_teach_top_5.shape[0]/total_courses_num)*100)) + \"% of Coursera's Tech courses teach one of the top five skills!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "41% is a little bit lower than 50%. However, before we draw an conclusions we have to run our signficance test below. We used an alpha value of 0.05 as well as a one-sided z-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail to reject the null hypothesis\n",
      "Proportion of courses teaching top skills: 0.41412911084043846\n",
      "Z-score: -15.374193118596795\n",
      "P-value: 1.0\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "p_hat = courses_that_teach_top_5.shape[0] / total_courses_num        # Calculate the proportion of courses teaching top skills\n",
    "p_null = 0.6                                                         # Perform one-sample proportion z-test\n",
    "standard_error = np.sqrt(p_null * (1 - p_null) / total_courses)      # Calculate standard error\n",
    "z_score = (p_hat - p_null) / standard_error                          # Calculate z-score\n",
    "\n",
    "# Calculate p-value\n",
    "p_value = 1 - stats.norm.cdf(z_score)                                # One-tailed test (greater than)\n",
    "\n",
    "# Compare p-value with alpha\n",
    "if p_value < alpha:\n",
    "    print(\"Reject the null hypothesis\")\n",
    "else:\n",
    "    print(\"Fail to reject the null hypothesis\")\n",
    "\n",
    "print(\"Proportion of courses teaching top skills:\", p_hat)\n",
    "print(\"Z-score:\", z_score)\n",
    "print(\"P-value:\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To reiterate, the null hypothesis stated that less than or equal to 50% of tech-related Coursera courses taught at least one of the top five job skills. The alternative hypothesis states that over 50% of tech-related Coursera courses taught at least one of the top five job skills.\n",
    "\n",
    "This significance test aimed to see whether or not over 50% of tech-related cousera courses taught 1 of the top 5 skills that data science jobs required. Having performed a one-tailed test we failed to reject our null hypothesis that the percentages of tech-related courses that teach at least one top 5 skill is less than or equal to 50%.\n",
    "\n",
    "Given a significance level (alpha) of 0.05, we compare the p-value to this threshold. Since the p-value of 1.0 is greater than the alpha value, we fail to reject the null hypothesis.\n",
    "\n",
    "However, it is important to consider that not all tech-related courses are beginner level courses. Some are targeted at more advanced programmers and thus would not include labels such as \"python programming\" or \"data visualization\" as those skills are most likely deemed as pre-requisites for higher complexity courses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring further: Are Data Science Skills taught only in Data Science Classes?\n",
    "\n",
    "From the Distribution shown above, we are able to visualize the proportion of the most demanded skills taught across different sub-categories of tech courses - Data Science, IT, Computer Science, and Math. We will now explore whether people should only stick to a \"Data Science\" curriculum to land a Data Science Job, based on the 2024 Job postings from Linkedin. This will further give us insights into the **significance of having an interdisciplinary education**, for working in the industry. \n",
    "\n",
    "We will explore, to what extent are the most demanded skills for data science positions (on Linkedin), represented within the curricula of courses labeled as \"Data Science\" compared to their representations across a broader spectrum of _technology-related_ courses on Coursera. \n",
    "\n",
    "To achieve this, we will conduct a **Chi Squared Contingency Test**. It will help us **determine whether there is a significant association between the type of course (Data Science vs. other tech-related courses) and the inclusion of the top skills**.\n",
    "\n",
    "We will begin by accumulating the counts of the skills taught in each of the categories. For example, how many CS courses teach python, how many DS courses teach python and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>CS</th>\n",
       "      <th>DS</th>\n",
       "      <th>IT</th>\n",
       "      <th>MATH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>python</th>\n",
       "      <td>113.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>52.0</td>\n",
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       "    <tr>\n",
       "      <th>sql</th>\n",
       "      <td>34.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>data analysis</th>\n",
       "      <td>62.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine learning</th>\n",
       "      <td>70.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>32.0</td>\n",
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       "      <th>communication</th>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "                     CS     DS    IT  MATH\n",
       "python            113.0  106.0  26.0  52.0\n",
       "sql                34.0   42.0  17.0  11.0\n",
       "data analysis      62.0  116.0  21.0  32.0\n",
       "machine learning   70.0   85.0   9.0  32.0\n",
       "communication      10.0   17.0   6.0   6.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting skills taught in each sub-category\n",
    "CS_skill_counts = CS_courses['skill'].value_counts()\n",
    "DS_skill_counts = DS_courses['skill'].value_counts()\n",
    "IT_skill_counts = IT_courses['skill'].value_counts()\n",
    "MATH_skill_counts = MATH_courses['skill'].value_counts()\n",
    "\n",
    "# a new data frame that combines these counts into a table\n",
    "course_skills_table = pd.DataFrame({\n",
    "    'CS': CS_skill_counts,\n",
    "    'DS': DS_skill_counts,\n",
    "    'IT': IT_skill_counts,\n",
    "    'MATH': MATH_skill_counts\n",
    "}).reindex(top_5_js).fillna(0)  # use reindex to ensure the skills are in the order of the list\n",
    "\n",
    "course_skills_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now conduct a `chi2_congtingency` test. \n",
    "\n",
    "Let's break this down:\n",
    "1. `chi2` is the Chi-square statistic, a measure that tells how expectations compare to actual observed data. \n",
    "2. `p` stores the p-value, which tells you the probability of observing the data assuming that the null hypothesis is true.\n",
    "3. `dof` is the degrees of freedom. We have 12 degrees of freedom because we have 5 skills and 4 keywords. So, dof = (5-1)(4-1) = 12/\n",
    "4. `expected` contains the expected frequencies of each cell in the table if the null hypothesis were true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic is:  27.600405611498783 \n",
      "\n",
      "The p-value is:  0.006326447516603454 \n",
      "\n",
      "The Degrees of freedom are:  12 \n",
      "\n",
      "Expected frequencies:\n",
      "  [[ 99.         125.37716263  27.06228374  45.56055363]\n",
      " [ 34.66666667  43.90311419   9.47635525  15.9538639 ]\n",
      " [ 77.          97.51557093  21.04844291  35.43598616]\n",
      " [ 65.33333333  82.74048443  17.85928489  30.06689735]\n",
      " [ 13.          16.46366782   3.55363322   5.98269896]] \n",
      "\n",
      "There is a significant difference in the distribution of top skills across course categories!\n"
     ]
    }
   ],
   "source": [
    "chi2, p, dof, expected = chi2_contingency(course_skills_table)\n",
    "\n",
    "print(\"Chi-Square Statistic is: \", chi2, \"\\n\")\n",
    "print(\"The p-value is: \", p, \"\\n\")\n",
    "print(\"The Degrees of freedom are: \", dof, \"\\n\")\n",
    "print(\"Expected frequencies:\\n \", expected, \"\\n\")\n",
    "\n",
    "if p < 0.01:\n",
    "    print('There is a significant difference in the distribution of top skills across course categories!')\n",
    "else:\n",
    "    print('There is no significant difference in the distribution of top skills across course categories.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is less than 0.01, we can reject the null hypothesis. This implies that there **is a statistically significant association between the type of course (Data Science, Computer Science, IT, Math) and the inclusion of the top skills**. That is, the distribution of skills across the different course categories is not random, and certain skills are more likely to be taught in certain categories. \n",
    "\n",
    "For example, the skill \"data analysis\" appears to be more prevalent in DS courses than in other categories, which could be due to the nature of data science, which heavily relies on analyzing data. This significant difference indicates that **Coursera's course offerings are tailored to the specific skill demands** of each tech field. People interested in acquiring a particular skill -- maybe to land a Data Science Job --  would enroll in courses from a specific department. Such insights help us in evaluating the **alignment of course content with market demands**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations\n",
    "\n",
    "Some limitations of our data analysis are the following:\n",
    "\n",
    "**Data Cleaning issues:** \\\n",
    "The way in which skills are described in the `skills` column is not very consistent. For isntance, in our dataset, the skill of knowing how to use PyTorch is described in the following ways: `PyTorch (488), Pytorch (40), Libraries (Tensorflow PyTorch)(3) , Pytorch Lightning(2), pytorch(1) * Pytorch(1), Libraries: Tensorflow PyTorch(1)`\n",
    "\n",
    "All of these imply the same thing. However, since `jobs_df_exploded` has more than 30000 rows, it is very difficult to manually clean, and rename the skills via a simple function. The numbers of the duplicated skills is very small, and would not realy affect the counts of the top 5 most demanded skills. Furthermore, some of the top skills in the `coursera_df` DataFrame were `python programming`, and `r programming`. We renamed these so that they would match with the way these skills are described in the `jobs_df` DataFrame.\n",
    "\n",
    "\n",
    "**Geographical Location issues:** \\\n",
    "In our dataset, we do not have information about where the students of such courses are from. We also don't know if these Courses are taught by institutions within the United States. This means that the reviews in a certian course might be higher because students from other countires are also enrolling in the same online course. It is also possible that the only enrollments to a specific course are from students that will not end up joining the workforce in the United States. This would make our analysis less accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we are not dealing with individuals’ data, only with open source information like LinkedIn postings and Coursera courses, which are readily available online, eliminating our concern for personal privacy violations. However, even a data set without people’s information can have pre-existing biases. For example, most MOOCs users, including on Coursera, are from the United States. This means that we should narrow down our job postings dataset to only display jobs searched in the United States, in order to keep the data aligned with each other. We acknowledge that this creates a bias for data science job posting for a single country, which is not representative of the whole world. \n",
    "\n",
    "Before starting our analysis we should ensure that our dataset is large enough and covers a diverse range of listings to try to extract the most reliable patterns out of the data. During our analysis we will pay close attention to the patterns in our data and how the biases in our dataset might have had an effect on them. Also during our analysis we will pick techniques to represent the data accurately to the best of our ability. For example we did this by proportionally scaling reviews in Section 3.4 instead of just plotting the mere count.  We have also described how the limitations of our data, this project or our techniques might have had an effect on our analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discusison and Conclusion\n",
    "\n",
    "\n",
    "In this project we aimed to explore how courses on Coursera align with the skills demanded by data science jobs, according to 2024 LinkedIn postings. We explored different patterns derived from our data on how the top five prevalent skills align with Coursera’s offerings. Some results were expected, for example python being the most abundant in many areas of exploratory analysis like number of courses, highest levels of engagement and greatest proportion of all tech related Coursera classes. On the other hand, communication was an unexpected skill that made the top five most in-demand skills for data science jobs. Even though it is considered crucial to the field, it came last on multiple metrics of our analysis. This could be attributed to the little importance that the tech industry places on teaching “soft” non-technical skills or due to communication being more popular among other categories of Coursera (humanities related ones) that were not explored in this project. Either way this opens up a number of questions for exploration in the future. \n",
    "\n",
    "The skills taught on Coursera and in demand for data science jobs largely overlapped (as seen in graphs from sections 1 and 2) which could signify that the demand for technical skills drives the types of courses MOOC platforms offer. This also makes us hopeful that courses offered by Coursera might be useful to supplement skills taught in college or are possibly sufficient alone to teach the top required skills for a job. An insight into how MOOC courses compare to standard educational approaches or how skills learned through MOOCs compare to skills learned in college in data science majors would be worth conducting further research on. \n",
    "\n",
    "For the analysis portion of our project, we looked at a various number of questions such as: \"Do 50% of Coursera tech courses teach the top 5 skills?\" and \"Are Data Science skills taught only in Data Science courses?\".The first question we will be examining is \"Whether or not 50% of Coursera tech courses teach the top 5 skills\". To reiterate the top 5 skills that we found from the jobs dataset were python, sql, data analysis, machine learning, and communication. From there we can set up our null and alternative hypothesis for this question. The null hypothesis would be that the proportion of coursera courses teaching top skills is less than or equal to 50%. While the alternative hypothesis states that the proportion of coursera courses teaching top skills is greater than 50%. After calculating our observed test statistic we found that only 41% of Coursera tech related courses taught the top 5 skills. Finally, we can perform our hypothesis test. By setting our alpha to 0.05, we got a p-value of 1.0. Having a p-value larger than alpha means that we fail to reject our null hypothesis that 50% of Coursera tech related courses teach the top 5 job skills. \n",
    "\n",
    "The important question to ask however is why do less than 50% of these courses teach the top 5 job skills. Well it's crucial to consider that not all tech courses are equal. Some courses are targeted more towards beginners while others are targeted towards more fluent programmers. This is a large consideration when looking at which skills are listed in that course’s description. For example, more complicated courses would not list \"python\" or \"data analysis\" as a skill because those are most likely prerequisites for that course. On the other hand, it would not be appropriate for job postings to not list skills like python or data visualization as required skills as that would increase the number of unqualified candidates. \n",
    "\n",
    "To address the second question -  \"Are Data Science skills taught only in Data Science courses?\" - we conducted a chi squared contingency test. The results of this test concluded that there is a statistically significant association between the category of course (data science, computer science, math and it) and the inclusion of the top five skills from the jobs dataset. Based on our p-value of less than 0.01, we rejected the null hypothesis of “there is no significant difference in the distribution of top skills across the four categories”. This means that the distribution of skills taught is not random and certain skills are more likely to be taught among certain categories. This could represent that Coursera’s course offerings are representative of specific skill demands of the industry. This also shows that an interdisciplinary education - taking courses from different categories like data science, computer science, math and it - is important in acquiring the top five in-demand skills for data science jobs. \n",
    "\n",
    "Some limitations of this project include, but are not limited to: geographical, dataset used, and logistical scope of this project. As detailed above, this project was limited by the geographical influence of only the United States and some limiting factors of the data set. Since we did not collect the data ourselves, and couldn’t control for regularity and thoroughness of the data, factors like inconsistent labeling and possibly incomplete data collection could not be avoided. Additionally, this project only explored Coursera which is one of many MOOC platforms and generalizations about MOOCs as a whole cannot be made from just this data. Lastly, this project had unavoidable time and logistical constraints - since we only had a few weeks to complete the project and we are all full time students, not industry professionals - which accounts for a possibly incomplete and less in-depth analysis than this topic demands. Nonetheless, this contribution is significant to the continued exploration in the relatively new field of MOOCs and how they reflect the current world. \n",
    "\n",
    "\n",
    "# Team Contributions\n",
    "\n",
    "Charvi Shukla:\n",
    "- Data Cleaning for the Coursera Dataset and Linkedin Jobs Dataset + Explanation of variables\n",
    "- Exploratory Data Analysis + Data Visualizations and Inference \n",
    "- Chi Square Contingency testing and corresponding test results  \n",
    "\n",
    "Inna Amogolonova:\n",
    "- Background, ethics and discussion/conclusion sections\n",
    "- Completely created the slides for the video\n",
    "\n",
    "Dawson Tam:\n",
    "- Significance testing\n",
    "- Conclusions\n",
    "- Presentation\n",
    "\n",
    "Kate Zhou:\n",
    "- Abstract and discussion/conclusion sections\n",
    "\n",
    "Tyler May:\n",
    "- Analysis\n",
    "- Overall revision\n",
    "- Video organization\n",
    "\n"
   ]
  }
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