EDACheckpoint_Group050_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If you lost points on the last checkpoint you can get them back by responding to TA/IA feedback**  \n",
    "\n",
    "Update/change the relevant sections where you lost those points, make sure you respond on GitHub Issues to your TA/IA to call their attention to the changes you made here.\n",
    "\n",
    "Please update your Timeline... no battle plan survives contact with the enemy, so make sure we understand how your plans have changed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - EDA Checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "Inna Amogolonova\\\n",
    "Dawson Tam\\\n",
    "Kate Zhou\\\n",
    "Tyler May\\\n",
    "Charvi Shukla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  Include a specific, clear data science question.\n",
    "-  Make sure what you're measuring (variables) to answer the question is clear\n",
    "\n",
    "What is your research question? Include the specific question you're setting out to answer. This question should be specific, answerable with data, and clear. A general question with specific subquestions is permitted. (1-2 sentences)\n",
    "\n"
   ]
  },
  {
   "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 means couurses from Data Science, Computer Science, Mathematics and IT._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Include a general introduction to your topic\n",
    "- Include explanation of what work has been done previously\n",
    "- Include citations or links to previous work\n",
    "\n",
    "This section will present the background and context of your topic and question in a few paragraphs. Include a general introduction to your topic and then describe what information you currently know about the topic after doing your initial research. Include references to other projects who have asked similar questions or approached similar problems. Explain what others have learned in their projects.\n",
    "\n",
    "Find some relevant prior work, and reference those sources, summarizing what each did and what they learned. Even if you think you have a totally novel question, find the most similar prior work that you can and discuss how it relates to your project.\n",
    "\n",
    "References can be research publications, but they need not be. Blogs, GitHub repositories, company websites, etc., are all viable references if they are relevant to your project. It must be clear which information comes from which references. (2-3 paragraphs, including at least 2 references)\n",
    "\n",
    " **Use inline citation through HTML footnotes to specify which references support which statements** \n",
    "\n",
    "For example: After government genocide in the 20th century, real birds were replaced with surveillance drones designed to look just like birds.<a name=\"cite_ref-1\"></a>[<sup>1</sup>](#cite_note-1) Use a minimum of 2 or 3 citations, but we prefer more.<a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2) You need enough to fully explain and back up important facts. \n",
    "\n",
    "Note that if you click a footnote number in the paragraph above it will transport you to the proper entry in the footnotes list below.  And if you click the ^ in the footnote entry, it will return you to the place in the main text where the footnote is made.\n",
    "\n",
    "To understand the HTML here, `<a name=\"#...\"> </a>` is a tag that allows you produce a named reference for a given location.  Markdown has the construciton `[text with hyperlink](#named reference)` that will produce a clickable link that transports you the named reference.\n",
    "\n",
    "1. <a name=\"cite_note-1\"></a> [^](#cite_ref-1) Lorenz, T. (9 Dec 2021) Birds Aren’t Real, or Are They? Inside a Gen Z Conspiracy Theory. *The New York Times*. https://www.nytimes.com/2021/12/09/technology/birds-arent-real-gen-z-misinformation.html \n",
    "2. <a name=\"cite_note-2\"></a> [^](#cite_ref-2) Also refs should be important to the background, not some randomly chosen vaguely related stuff. Include a web link if possible in refs as above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are approaching graduation, many of us undergraduate students are concerned about finding a job after we graduate. 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 listed on there 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 a new interesting avenue for acquiring education and work-force skills and needs to be further researched 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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Include your team's hypothesis\n",
    "- Ensure that this hypothesis is clear to readers\n",
    "- Explain why you think this will be the outcome (what was your thinking?)\n",
    "\n",
    "What is your main hypothesis/predictions about what the answer to your question is? Briefly explain your thinking. (2-3 sentences)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis:** \\\n",
    "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",
    "For each dataset include the following information\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 \n",
    "  - **Number of variables:** 12 variables\n",
    "\n",
    "Now write 2 - 5 sentences describing each dataset here. Include a short description of the important variables in the dataset; what the metrics and datatypes are, what concepts they may be proxies for. Include information about how you would need to wrangle/clean/preprocess the dataset\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",
    "- `Search Country`: String, country where the job is searched\n",
    "- `Job Summary`: String, job summary\n",
    "- `Job Type`: String, nature of the job (on-site, remote, etc)\n",
    "- `Job Skills`: String, skills extracted using AI from the job description\n",
    "\n",
    "**Cleaning process:** \n",
    "\n",
    "We first exracted 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 columns. We also only kept jobs from the United States. 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 upper case characters). The `skill` column entries were split into arrays based on a comma delimiter, transforming 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 removing special characters while preserving basic punctuation -- this is essential for doing any form of text analysis.\n",
    "\n",
    "**Concepts:** \n",
    "\n",
    "The skills series will be useful for extracting the top three 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",
    "- `What you will learn:` String, describing the objective of the coure\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 checkef if any rows have null values. We removed reows 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 standardize `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 qunatitative 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",
    "**If you plan to use multiple datasets, add a few sentences about how you plan to combine these 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 out 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 three most prevalent skills and then, one by one, we will cross reference what is the percentage of courses that cover a speecific skill among all technical courses on the platform. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "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": 55,
   "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": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>job_title</th>\n",
       "      <th>company</th>\n",
       "      <th>job_level</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Senior Machine Learning Engineer</td>\n",
       "      <td>Jobs for Humanity</td>\n",
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       "      <th>1</th>\n",
       "      <td>Principal Software Engineer, ML Accelerators</td>\n",
       "      <td>Aurora</td>\n",
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       "      <td>United States</td>\n",
       "      <td>who we are aurora nasdaq aur is delivering the...</td>\n",
       "      <td>[c, python, pytorch, tensorflow, mxnet, cuda, ...</td>\n",
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       "      <td>Adame Services LLC</td>\n",
       "      <td>Associate</td>\n",
       "      <td>United States</td>\n",
       "      <td>location new york city, ny position summary ou...</td>\n",
       "      <td>[etl, data integration, data transformation, d...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Senior Data Warehouse Developer / Architect</td>\n",
       "      <td>Morph Enterprise</td>\n",
       "      <td>Mid senior</td>\n",
       "      <td>United States</td>\n",
       "      <td>responsibilities candidate must have significa...</td>\n",
       "      <td>[data lakes, data bricks, azure data factory p...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lead Data Engineer</td>\n",
       "      <td>Dice</td>\n",
       "      <td>Mid senior</td>\n",
       "      <td>United States</td>\n",
       "      <td>dice is the leading career destination for tec...</td>\n",
       "      <td>[java, scala, python, rdbms, nosql, redshift, ...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>12210</th>\n",
       "      <td>Data Entry Clerk - Excel</td>\n",
       "      <td>Corestaff Services</td>\n",
       "      <td>Associate</td>\n",
       "      <td>United States</td>\n",
       "      <td>benefits  medical, dental, and vision coverage...</td>\n",
       "      <td>[data entry, microsoft excel, vlookup, pivotta...</td>\n",
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       "    <tr>\n",
       "      <th>12213</th>\n",
       "      <td>Corporate AML Alert Investigation Specialist</td>\n",
       "      <td>Glacier Bancorp, Inc.</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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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       "    <tr>\n",
       "      <th>12215</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>CompSource Mutual Insurance Company</td>\n",
       "      <td>Mid senior</td>\n",
       "      <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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       "      <th>12216</th>\n",
       "      <td>Medical Technologist, MLS or MLT</td>\n",
       "      <td>Community Health Systems</td>\n",
       "      <td>Mid senior</td>\n",
       "      <td>United States</td>\n",
       "      <td>job description weekend night shift position f...</td>\n",
       "      <td>[medical technology, mls, microbiology, clinic...</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",
       "0      [machine learning, programming, python, scala,...  \n",
       "1      [c, python, pytorch, tensorflow, mxnet, cuda, ...  \n",
       "2      [etl, data integration, data transformation, d...  \n",
       "3      [data lakes, data bricks, azure data factory p...  \n",
       "4      [java, scala, python, rdbms, nosql, redshift, ...  \n",
       "...                                                  ...  \n",
       "12210  [data entry, microsoft excel, vlookup, pivotta...  \n",
       "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",
       "[10291 rows x 6 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 57,
   "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": 57,
     "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": 58,
   "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",
       "    }\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, 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",
       "      <td>beginner</td>\n",
       "      <td>[python syntax and semantics, basic programmin...</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 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",
       "    </tr>\n",
       "    <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": 58,
     "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"
   ]
  },
  {
   "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 three 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 occurances of each skill -- the top 50 skills are stored in the `top_job_skills` variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 59,
     "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": 60,
   "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 profieiceny 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 required `communication`. Next, we will be looking at the Coursera Dataset and the skills taught in that dataset. \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 follow a similar approach as before: since every entry in the `skill` column in `courses_data` dataframe, is a list, we wil utilise `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": 61,
   "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": 61,
     "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "python    297\n",
       "Name: skill, dtype: int64"
      ]
     },
     "execution_count": 62,
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 63,
     "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**. Sematically, _python_ and _python programming_ are talking about the same skill, so we will rename \"python programming\" to python (done in the data cleaning section). \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 sub categories 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 analysing the Coursera dataset in more detail. We will begin by breaking the dataset down by the `keyword` tag, and analysing, how many courses belong to each sub-category. To do so, we will be creating a bar chart which plots the nymber 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": 64,
   "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": 64,
     "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": 65,
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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 for when we look at `num_reviews` and `ratings` of courses, so that we appropriately _normalize the data_ before making any comparisions. In the next section, we will analyse the overall proportion of Coursera Coursera that teach each individual (highly demanded) skill. \n",
    "\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": 67,
   "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": 67,
     "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, as input, 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 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": 68,
   "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": 68,
     "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`-labelled 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 uthe se 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": 69,
   "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": 69,
     "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 show 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 `keyword` (CS, DS, IT, MATH) would be represented by a separate bar. Furthermore, within each bar, the proportions of the courses teaching each of the top skills would be stacked on top of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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 just the overall proprtion of courses that teach a certian 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 comsiderable 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 amount of courses from each of the departments teach **communication** skills, which seem to be very 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 interract 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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "<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",
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       "    <tr>\n",
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       "      <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",
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       "      <td>225830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python for Everybody Specialization</td>\n",
       "      <td>4.8</td>\n",
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       "      <td>python</td>\n",
       "      <td>CS</td>\n",
       "      <td>209311</td>\n",
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       "    <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",
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       "      <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": 71,
     "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 skill. We re-indexed the skills so they appear in the same order as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 72,
     "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 group courses in the DataFrame `courses_with_top_skills` by the `skill` column. Furthermore, for every unique skill,  we sum up the values in the `num_review` column.  The 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": 73,
   "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": 73,
     "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": 74,
   "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": 74,
     "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 valus 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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5cwYbN26Eg4MDhgwZAhsbG6xfvx7NmjVDs2bNMu1jkV/n55xI75x54sQJeHh4wMnJCWPGjIFEIsGxY8fg7e2Njx8/4s8//wTwLRnv1q0b3rx5g86dO8PY2BgvX77Ezp07cfbsWezatQuGhoYAAA8PD1y6dAk9e/ZEtWrV8OHDB2zfvh19+vTB3r17030KkpXRo0fj7Nmz6NChA7p06YKEhAT8888/2LNnDx4/foxdu3bJLb9s2TIIgoCuXbuiePHi8Pf3x9q1awEAY8eOBfDtorlbt24ICwsT9/XVq1dz9B05fPgw4uPjxcSjZcuW8PLygr+/P0aPHi2eVzp27IjNmzfj4MGDqFWrltw69u/fD319ffHCPTvnnO9vTPz9998wNTXF1KlT8eLFC5ibm+foPH39+nX0798fenp66NevH3R0dMTfvh/lNLbcOH36NADAwsJCnBYaGoru3bsjMTERXbt2RcWKFREcHIydO3fin3/+wc6dO2FgYICOHTvi1KlTOHTokFySk5KSgkOHDsHU1BQWFhbpDjCS3TJcXFywa9cuvHnzBhUqVAAAXLp0CYIg4PPnz3j48KEY+6VLl5CQkIBmzZoB+PnvhiAI6N27N8qVK4exY8fi9evX2L59O65cuYK9e/eiYsWKAL6df/r27QtdXV307NkTBgYGCAoKwtq1a3HlyhVs2bIFmpqa4noPHz6M8uXLY8KECQgNDU1zjMqEhIQgPj4+04trANDV1ZU7zwOAr68vvL29YWhoiIEDB0JbWxunTp2Cl5cXrl27Bh8fH6ip5W5MzKVLl6JWrVqYNm0a3r59i/Lly2Po0KHZ2tc/c0x7enri7Nmz2Lt3r9jv2M7OLlfXlNn9zcqunO7vvXv34uPHj+jZsyfKli2Lw4cPY8aMGQgNDRV/BzISHByMYsWKYeHChTh69Ci+fPkCPT09/P777+K4BoqQnWvn723btg1aWlro2bMndHR0sHfvXowfPx4fP37M8jzfq1cv3LlzB5MmTcKmTZvQsGFD8SZt8eLFUaRIkUw/n5ycDA8Pjwy7z+REaGgo+vTpg9KlS6Nfv34oVqwY7t69i23btuHevXti7pUtQg5YW1sL9erVy8lHBEEQhN69ewsmJibC5cuX5aafP39eMDExEXr16iVO69Wrl2BiYiKsXbtWnJaYmCg4OjoKJiYmwv/+9z9xenJyslCvXj2hQYMG4rQVK1YIJiYmQtu2bYXY2Fhx+oMHDwQzMzOhe/fu4jQ3NzfByclJ+Pr1q1xc27ZtE0xMTISNGzeK0xo3bpzuNuzbt08wMTERdu/eLQiCIBw9elQwMTERjhw5Irecr6+v0LZtW+H+/fuCIAjC/v37BRMTE2HChAlCamqquFx0dLTQvHlzoUaNGsLLly/lymjTpo2QkJAgLhsaGiqYmJgIXbt2FTJz48YNwcTEROjdu7eQmJgoTk9ISBB69uwpmJiYCNeuXROn169fX2jcuHGm60yPbB99Lzo6WnBwcBBq164tfPjwQW7eokWLBBMTE8HHx0ecNm7cOMHExEQIDQ0Vpw0fPlywsLAQ3r17J/f5c+fOCSYmJsKsWbPEabLjJykpKdNYZcv9+++/wsePH8V/7969E27cuCEMHjxYMDExEby8vMTPxMbGCrVr1xbat28vVw+CIAh//fWXXL0/fvxYMDExEaZPny633MKFCwUTExPh0aNHgiCkPX5yUoa3t7dgYmIiPHv2TFymQYMGQsOGDQUTExPh7t274vSmTZsK3bp1EwQh+8doTsiOxXHjxgmCIAjv378XHB0dBSsrK+H58+ficj8eW1evXhVMTEyEpUuXpllnt27d5I4n2b5q3bq13HE8Z84cwcTERGjfvr2QkpIiTl+wYIHcd1YWo6mpqRAUFCQul5iYKHTu3FkwNTUVnj59KgiCINy9e1cwMTERZs6cKRdTYmKi4O7uLtSsWVN4+/atXFw9e/bM1r6aPHmyYGJiIuzZs0du+qNHj4SaNWsKTZs2FZKTkwVBEIRLly4JJiYmwooVK7Jcb36en3/8fqUXZ0bnzEGDBgk2NjZydZWcnCz07NlTGDx4sDhtxowZgrm5uXD79m25z//333+ChYWFMGDAAEEQBOHjx4+CiYmJMGPGDLnlbt++LTRv3lz8buXEo0ePBBMTE2H27Nlp5nXq1EkwMTERPn78KAjC/x3DdevWFaKiosTlYmJiBBsbG8HZ2Vmctnz5csHExETYv3+/3Dplx+r3+zojsvIfPHggTvvjjz8EExMT4dChQ3LLduzYUXBwcJA7lzx58kTu3JaTc47sO2RjYyNER0fLLZuT83Tr1q0FGxsb4fXr1+K0uLg4oV27dnLHUU5iy4jsmmDr1q1y5/qIiAjh4cOHgre3t2Bqaiq0b99e/N4JgiAMGDBAsLOzE3+HZWTHuux4S05OFpycnISWLVumu92ya5Yfz5E5KUO2rp07d4rLjBs3TjzXr1+/Xpzu6ekpWFhYCDExMT/93Zg4caJgYmIi9O/fX27fnDx5UjAxMRHGjx8vCIIgpKamCq1btxYaNmwoREZGyq1j9+7daWI0MTERTExMhFevXmVaviAIQmBgoGBiYiIsWrQoy2W/9+rVK8Hc3Fxo1aqV3DVeamqq4OHhIZiYmAj+/v6CIKRfNzI/XpPIvu8uLi5y+yS7+1oRx7Rsn+7bt0+clptryuz+Zv3IxMQkzTVibva3qampcOPGDXHZhIQEoVOnToKZmZncdc2PUlJSBGtra8HExEQYOnSoEBgYKBw6dEgYNWqUYGJiInTp0iXNvs0O2e+brK5zcu0sOy6sra3lrl9jYmKExo0bC9bW1sLnz5+zjGHPnj1CrVq1xO+IiYmJYGZmJnTu3FnYvXu33O+mIHyri27duglJSUnCqFGjBFNTU2Hz5s1yy6R3ndW4cWOhfv364t+y8+SlS5cEQRCEjRs3prmOFARBmDdvntC+ffs05/nM5OhWjlQqzXFTlU+fPuHatWtwdHRMM3JVgwYN4OjoiOvXr8s1RQIgN4pUkSJFxDvX3zcfkUqlqFSpEsLDw9OUO3jwYLm7MzVr1oSzszNu376NDx8+AAD27NmDgwcPyt35SExMFO+4/DjceZEiRTK86yZTvnx5AMDGjRtx+vRpcWCbfv364eDBg+Jdx2PHjgEARo0aJXdXQFdXF4MHD0ZKSopcsyYAaNGihdxTsEqVKkFfX1/cnowcPXoUwLcmPt/fEdHQ0MDIkSMBIM+aoF66dAlfvnwRn2R+b/jw4dDS0sqy7BUrVuDcuXMoW7asOC05OVlsivEzw9K3b98edevWFf81aNAAPXv2FJuATZw4UW5bIiMj0aJFC8TExODTp0/iP9nxevLkSQCAsbExrK2tcezYMbGfXWpqKg4dOoSaNWtmeBc5J2U0adIEAMSnmk+ePBEHTQC+NZcFvj2xevXqlXiHO7vH6M8oXbo0pkyZgvj4+Gw1oc2J5s2byx3Hsid0LVq0kLtbKjtn/Hh+kDVZlClSpAj69esHQRDE138cPnwYwLfz0Pd1EB0dDVdXVyQlJeHs2bNy661Tp06WsaempuLEiROoXLkyOnbsKDfPzMwMbdq0watXr/Dvv/9mua4f5ef5ObvSO2eWK1cOsbGx8PLywr///gtBECCVSrFt2zbxCaAgCDh69CiqVasGQ0NDuTooWbIkbGxscOnSJXz9+hW6urooVqwYjh8/jj179iAiIgLAt0Esjh8/nmVrj/SYmZnh1q1bGDdunNz0jx8/iv2ivn79KjevYcOGKF68uPh30aJFxacaMqdOnUKJEiXQrl07uc8OHDgwW3E9fvwY9+7dQ9WqVVGzZk1xeps2bQAAfn5+cst37NgRX758kWvuLWtiLTv+cnLOkbG0tEzz9Ci75+nHjx/j8ePHaNu2rfh0DgC0tLQwYMAAuXXmJraMzJkzR+5c7+TkBDc3N/j5+aFz587YtGmT2ALk8+fPuHjxIhwcHKCrqytXrpmZGSpXriyWK5VK4ebmhmfPnuH+/ftieQcOHBBbt6QnJ2XUrVsXOjo64rleEARcvnwZrq6uqFSpEq5evSpOP3/+PJycnFC0aFGFfTdGjBghNxpv06ZNUb16dZw+fRopKSkICQnB48eP0bBhQ6SmpsptS+PGjaGpqZmmnipVqpStgfDU1b81vMvpue3kyZNITk7GwIED5a7xJBKJ2MLgZ657HB0d5fZJdve1Io/p7+XmmjI7v1nZlZv97eTkJNcfUUNDA/369UNqamqmr+JKTEzEiBEjMHPmTKxevRqtWrVCmzZtsHz5cri7uyMoKAh79+796W3KzbXz77//LjfAZNGiRdGjRw/ExcWJLc4y06lTJ5w/fx4+Pj7o1q0bjI2NkZqairt372Lq1KkYOHBgmlfZpKamYvz48Th27BjGjx+P3r1753qbZWTXiX/99RcuX74sljl58mT4+/vLneezkqOms2XLlsXz58+RmJiY7aGUw8LCIAhChh12f/vtN1y/fh1hYWFyiUjp0qXllpN9oX+crqamlm67bBMTkzTTqlWrhn/++QehoaEoVaoU1NXV8ebNG6xevRpPnz7F69evERYWJv4w/nhxXKJECfGklxFra2sMHToU69evx7Bhw1CkSBFYW1ujQYMGcHNzEyvn1atX0NHREZud/LhPAKRpavPjtgPfDvisLuJl7zU0NjbOdlmKklnZ2traqFy5cqbvXQS+1fHnz5/h6+uLkJAQvH79GqGhoWI/sZ9JYhYtWoRSpUohNTUVjx8/hq+vLwRBwIIFC+Dk5CS37PPnzwF8azKzdOnSdNf3fT+dTp06Ydq0aTh//jyaNWuGy5cvIzw8XEwE05OTMmxtbVGyZElcuHAB7u7uuHjxIooUKYLff/8dGzduxJUrVzB48GDxIlOWaGb3GP1Z7dq1w7Fjx3D69Gls3rw5TbOd3CpVqpTc37Lv5I/TZeeMH4+P9I5FWdNQ2QBGsnro1atXhnH82Cfrx/LTExkZiejoaNjb26fb7OT776OVlVWW6/tefp6fsyu9c+bIkSPx6NEjbNu2Ddu2bYOBgQHq1KmDpk2bokWLFlBXV8enT58QFRUlNrfNyLt371C9enUsWLAAkydPFpsxmZiYwNnZGW3btoW5uXmO4wa+nVsDAwNx6dIlvHz5EmFhYfj48aNYbz8eV9k5P4eGhqJatWppmusZGBhka2Rr2SBA9erVkztn16hRA+rq6rh58yaePHkiHuNt2rTBggULcODAAbRs2RKpqanizSRZM+ycnteA9I/17J6nZeWl1xz7x+9mbmLLSP/+/eHs7AxBEBAREYGtW7ciJCQEI0eOxB9//CG37MuXL5Gamopz585levwlJCRAU1MTHTt2xIYNG3DgwAFYWlqK/QpdXFzkXp/1M2XUr18fly9fRnJyMv777z98/PgR9erVQ2RkJI4ePYrExEQEBwcjIiJCHB9AQ0NDId+NjK6nnj59io8fP+LZs2cAvr3bMaM+drk5XwIQf49kiVt2ZXbtUalSJejo6PzUdc+P8Wd3XyvymP5ebq4ps1sH2S0fyNn+zui4ApDp4Gbp3ZSS6dOnD7Zu3YqLFy/+9Ii8ubl2Tm/ZqlWrAkC2B0jU1tZG8+bN0bx5cwDfjv0jR45gzZo1uHjxInbu3CmXTAYFBYnd+G7cuJHhvsmJFi1aoGPHjvD398fly5ehpaUFe3t7NGzYEG5ubnI3VbOSo0SzVq1aePr0KW7fvp3pnZDp06fj69evGDduXJadc2V3qX68MMoooctum+D0LrRkZcnWvWTJEqxfvx6VKlWCg4MD6tatKw50MWzYsDSfz25b/jFjxqBnz544d+4cLl26hOvXr+PmzZtYu3Yt/v77b9jY2GS6X2Q/yD9uQ277EuSmLEXJTv1nVbafnx9mzZqFUqVKoXbt2rC1tYWpqSnKli2LTp06/VR8dnZ24t0nZ2dntGzZEl26dMGgQYOwfPlyuQF9ZNsyYsSIDPuLfN+n1dXVFfPmzcOBAwfQrFkzBAQEQFNTU3z6kJ6clKGmpobGjRvjyJEjSExMxKVLl2BtbQ0dHR04OTkhICAACQkJOHfuHExNTeXuHmfnGFWEWbNm4datW1i2bBkaNWqUo89mdAc7o34K2T03pLecbL//mJyuXLkyw37Ksjt+Mtl5/15uz4fZkZ/n5x/9ODiQTHrnrFKlSmH37t24f/8+zp8/j6tXr+LkyZMIDAyEjY0Ntm3bJu5/W1tbjBo1KsNyZe8Ubdq0KZycnHDhwgVcvHgR165dg6+vL/7++294enrm+A7v58+f0aNHDzx9+hQODg6wsrJC+/btYWlpKfZ7/FF2j7+MXqye1Q2zpKQksdwdO3Zgx44d6S63c+dO8UK3WLFiaN68OY4ePYpPnz4hODhYrtUDkPPzGpB+vWb3PC0bHCm9Y+r7Pny5jS0jxsbGqFevnvi3q6srBg0aBG9vb0RERMi1XpHVRdOmTTN9DZDsO29kZAR7e3scOXIEkyZNwtGjR5GQkJCm1cL3clpGkyZNcPz4cQQFBeHOnTvQ0NCAg4MDoqKi4O/vj6CgIFy5cgVSqVRu0Jyf/W5IJJJ0r8dk33l1dXWxnrp164YWLVqku54f15Hd6xkzMzPo6upmOXhRZGQkBg4ciEaNGmHEiBFZnttSU1OzdZ7N6Hcovfizs68VeUx/LzfXeYp8Z2xu9nd2rtNzSnZD9McWJz+KjIyErq5uutcTsrIVfZ2e2TY9efIE+/btQ/369eXOU8C3m5h9+/aFubk53N3dceXKFbnvrbq6OhYuXIjz58/jwIED2Lt3709fG0ulUsybNw/Dhg3D2bNncfnyZdy4cQOXLl3CunXrsHPnTlSpUiVb68pRTbZp0wY7d+7Etm3bMryQef/+Pfz9/aGtrQ0vLy9xUJHHjx+nu/yTJ08gkUgU9hRF5sWLF2LTOZlnz55BXV0dVapUwZs3b7BhwwbY2dlh8+bNcgdLehcR2RUREYHHjx/DwcEBnTt3RufOnSEIAg4cOICJEyfC19cXK1asQJUqVfDs2TO8fv06zR0o2b76vlnRz5AdDE+ePIGjo2OelpVZ2T+Ki4vD69ev09TT9xISEsSXXO/fv1+uudbPjCiZkfLly2P58uXo1asXJkyYAH9/f/FulCwh1dTUTHMiiImJwcWLF+Weaujq6qJFixYIDAxEREQETp06haZNm2Z6JyinZTRp0gR79+7F1atXcfPmTfTv3x/Atyceu3btwpkzZ3Dnzh25GyfZPUYVoXTp0pg6dSrGjx+fbhNa2Q9dehffWTUJz6307pTKRnuVPWWR1UPp0qXTJN0vX77Es2fPcjXYgIGBAXR1dfHkyRMIgpAmOZF9T35MYrMjP87P39fX9z+a2a0rQRDw+PFjxMfHw8rKCpaWlhgxYgRiYmLw559/4syZM7h48SIaNGgAHR0dREVFpfkeAN+an6mpqUFTUxMxMTEICQlBpUqV5O4CP3r0CL1798aqVatynGhu374dT548wfTp09MkAT9zXBoaGuLly5dpnjp//vw5y5H8zp07h48fP8LCwiLNqNIA8ObNG3h5eeHAgQMYN26c2HWkY8eOOHjwIE6dOoXbt2+nudmV03NOenJynpadT2VPwb734zRFxJYRDQ0NLFu2TBzxuGbNmuJ+kZUbHx+f7vEnawL9/XegY8eO8PT0xLVr13Dw4EGULVs200HqclpGo0aNoK6ujosXL+LevXuwtbWFlpYW6tatC4lEgitXruDs2bOwt7cXn44r4rshCAJevXqV7tNmfX196Ovri9siCEKabUlNTcXx48dz/b5oDQ0NNG/eHP7+/jh9+rTYZeRH+/fvx/3798Unh99fe8gGgJQJDQ1FfHy8eJ6V7eOf+R3K7r7Oq2M6P68pMyofyN7+lnnx4kWa9cjOAZkNQHf+/HnMnz8fXbp0SdMaQfYbmtl1pbe3N3x9fXHw4EG5AfZko+7q6eml2absXjun10JPdn0hO/elJzo6Gr6+vnj9+nW65wMAYperHwdtsrKyQuvWreHk5IRLly6JLfJycx0h8/r1a7x69Qp169aFu7s73N3dkZycjA0bNmDZsmXw8/OTuzmXmRw9IqtVqxaaNWuGkydPYsOGDWnmx8TEYNSoUUhKShL73xkYGIj9fGR9xmQuXryImzdvwtHRMVvNhnJi06ZNcneigoKCcPnyZdSvXx/FixfH58+fIQgCjIyM5H7w4+LixCH4czN0+t69e9GvXz+59uUSiUS8WJVdqMnu+q1YsULursnXr1+xYcMGSKVShb04WlbWqlWr5J48JCUlYfXq1XLLKJqTkxN0dXWxbdu2NP281qxZg4SEBLmyZftHtk/i4+MRGxuLChUqyF28JCcnw9fXF0Du6ikzdnZ2GDZsGL5+/YoJEyaI63d2dkbRokWxZcsWfPr0Se4za9euxejRo9MMI96pUyckJiZi1qxZiIuLy/QOd27KqFevHrS1tbF69WrExsaKJ6g6depATU0Nf/31F1JSUuSOpeweo4rStm1bNG3aFHfu3EnT/EmWwPzYJ/HGjRu5aj6UHf/884/cj4HsNRZSqVRsXtyyZUsA316X9ON3ZvLkyRgyZEi6fcOzoqamhmbNmiEsLExsBinz33//ITAwEJUrV85Vc8/8OD+XKVMGAPDgwQNxOVnf4+yQSCQYMWIEhg4diujoaHG6rq6u2IxKKpWK57/nz5+nGUY+ODgYgwcPhpeXF9TV1RESEoIePXqI5zKZ3377DcWKFcvVnXHZqyV+HOX3zp074utCMnqKmxlXV1fExsbif//7n9z0TZs2ZflZ2fHSr18/NG3aNM2/3r17o2bNmvjy5YvYxxgAateujcqVK+PIkSM4deoUmjVrJl5IAbk7r/0oJ+dpc3NzVK1aFUeOHMH79+/FZZOSkrBlyxa59SoitsyUKFEC3t7e4sj1stf5lCpVCvb29rh06VKa18OcP38ew4cPx/r16+Wmt2rVCkWLFsX27dtx48YNtG/fPtOnGzkto3jx4rC3t8fZs2dx+/Zt8VxfsmRJmJqa4siRI3j06JF4DgOgsO/Gxo0b5f4ODAzEixcv0Lp1a0gkElhYWKBixYo4cOCA2DRUZteuXRgzZkya811OjBkzBjo6OpgxY0a67xC8efMmli1bhqJFi4pP65s1awapVIoNGzaI4xAA364tZK99kJ3n9fX1UaRIEQQHB8tdT7x48SLb79HO7r7Oq2M6P68p05OT/S1z+vRpuZtLiYmJ2LRpk3hzISPGxsZ49eoVtm/fLjdGR3JyMpYvXw7g2/gbGZElkN+/NSImJgbPnj1D1apVxZvIubl2PnLkiNzrj758+YKtW7dCT08vwwQSAGxsbGBiYoKTJ0/Knb+/t23bNgBp96NMiRIlMH36dERHR2PKlCkZlpUda9asQd++fcVXVwHfbsjIuvTk5Doxx7/A8+bNw+fPn7F48WIcPXoUzZs3h4GBAV68eIGAgAB8/PgRXbt2RZ8+fcTPzJgxAz169MDAgQPRtWtXGBsb4/Hjx9i9ezdKlCiR5l2AihAUFAR3d3e0atUK79+/x7Zt21CiRAl4enoC+HagGhoaIiAgADo6OjA1NcX79++xf/9+8WL4y5cvOS63c+fO8PPzw5QpUxAUFITffvsNkZGR2LVrF4oUKSK+4072CoiAgAC8ffsWTZo0QVxcHPbt24dXr15h/Pjxub4D+KPatWuja9eu2LVrF7p06YLWrVsD+DbgycOHD9GjR48sBznKrWLFimHGjBmYOHEi2rVrh65du6JkyZK4dOkSTp8+jZo1a8q1J5c1e9i4cSPq16+Ppk2bolatWrh69SomTpwoNhU6dOgQnj17BjU1tVzVU1aGDBmCs2fP4u7du1i3bh2GDRsGPT09TJ8+HZMnTxa3pUyZMrh69SoCAwNhZWWVpk9ArVq1YGhoiJMnT6JChQqZ9sUBkOMytLS04OTkhFOnTkFXV1c8CZQoUQLm5uZ48OABKleuLDf4UHaPUeDbk6MPHz7Aycnpp/pzzJo1Czdv3kzz1KZy5cqwt7fHtWvXMGbMGDg5OeH58+fYtWsXjIyM0ly0KIKsc37v3r2hoaGBgIAAPHr0COPGjRPvNtetWxedOnXC3r17xe+MhoYGDh48iHv37qFHjx457kMpM27cOFy/fh1Tp07FzZs3YW1tjbCwMOzYsUNsrpLb1zXk9fm5ffv2OHDgADw8PNC3b19oa2vjyJEjOXqv1vDhwzFhwgR07doVHTp0QPHixfHo0SPs3r0b5ubm4o/x+PHjcePGDUyaNAlXr16FtbU13r59i507d0IqlYpx2dnZoV69eti5cye+fPkCR0dHpKSk4OjRo3j9+rXcXdfg4GCEhITA1NQ009c6uLi4YOvWrZgwYQJ69OgBPT09PHjwAPv374dUKkVSUpJcopxdffv2xbFjx7BkyRI8e/YMVlZWuHXrFs6cOZPpqwUiIiJw4cIFlCpVKtMLsL59++LPP//Ezp07xcFHJBIJOnToIF6A/dikKjfntR8VL1482+dpiUSCGTNmYODAgejQoQN69OgBHR0dHDp0SHwaITv+FRFbVurUqQN3d3ds2bIFnp6e2LRpkxhjr1690K9fP3Tt2hUmJiZ49uwZdu7ciRIlSqS5m6+jo4NWrVqJg5B06NAhy7JzWkaTJk0wb948AJC7aK1Xr56Y0H+fTOTku5GZwMBAREVFoX79+nj+/Dn8/PxQpUoVsVm7VCrF3LlzMXjwYHTq1AndunWDoaEh7t+/j3379qFKlSrpdkfKrrJly2LNmjUYMWIEOnXqhBYtWsDGxkYcJOXo0aPQ0tLCihUrxKd5VapUwZgxY7BkyRK4ubmhQ4cO4us2rl+/jsaNG4sDNWlqaqJVq1Y4ePAgBgwYgFatWiE8PBw7duxA5cqV5d5vnJHs7uu8Oqbz85oyPTnZ3zJqamro3r073N3doaurK/4We3p6ZtrKsWLFihg+fDhWrFiBTp06oUuXLpBIJDh06BD+/fdfDBkyBLa2thl+vm3btli1ahXmz5+P8PBwGBgYYO/evfj69avcdyI3184pKSno1q0bunfvjqSkJOzatQsfP37EokWL0gyg9j2JRILly5ejT58+GDduHHbt2oX69eujZMmSiIyMxPnz53H9+nV07do109+AFi1aoEWLFjh+/Dj8/PzQvXv3DJfNTN++fREYGIhBgwahW7du4sCrO3bsQLFixdClS5dsryvHiaaenh42bdqEwMBA7N+/H35+fvj48SOKFi0Ka2tr9OzZEw0bNpT7jLGxMfz9/bFq1SocP34cu3btQunSpdGpUycMHTpU4c1mAWD+/Pk4fvw4li5dCk1NTTRr1gyjR48WT0JFihTBxo0bsXjxYgQGBmLPnj0oU6YMHBwcMHz4cPTs2VN8T1VOLvpKlSqF7du3Y/Xq1Th16hT8/Pygo6MDe3t7/PXXX3J3A2Qvqw0ICMDixYuhra0NS0tL8eXNijR79mxYWVlh586dWLFiBaRSKczMzLB48WK0bdtWoWX9qF27dihfvjzWr1+PLVu2IDExUTwp/fHHH3L9cnr16oXbt29j3759uHr1Kpo2bYply5ZhyZIluHjxIgIDA1G6dGlYWFhg4cKFmDlzJm7fvo2vX78q5J2fMrI27+3bt8fq1avRqFEjmJubw83NDeXLl8fGjRuxZcsWJCQkoEKFChg6dCj69++fbnPKjh07YunSpVne4ZbJaRlNmjTBqVOnUKtWLbk71E5OTnjw4IHcHW4g+8co8O0O6/Xr17Fly5afSjRLlSqFadOmpRnFEwCWL1+OJUuW4Ny5czhz5gxMTU2xdOlSnD9/Pk8SzS5dukBHRwfbt2/Hp0+fUL16dSxZsiRN39m5c+fCxsYGu3btgo+PD6RSKapWrYq5c+f+VP+H0qVLY+/evVi9ejXOnDmDw4cPo0SJEmjatCmGDBmS5j2XOZHX5+e6deti8eLF2LRpE5YvXw49PT20aNECffv2TXOcZeT3339H0aJFsXnzZmzatAnR0dEoX7483N3dMXToUPEYLlu2LPbt24c1a9bgzJkzOHToEPT19eHo6IihQ4eKT30lEgl8fHzg6+uLo0eP4ty5cwCQ7vnt5MmTWLlyJUaMGJFpolm3bl0sXboUGzZswKpVq6ChoYEKFSpgzJgxMDY2xqBBg3DhwoUc92XW0NDA1q1bsXLlSgQGBuLIkSMwMzPDhg0b4OHhkeHn9u/fj+TkZHTq1CnTfmWtWrXC4sWL8eDBAzx48EAcQbpDhw7w8fFB+fLl021WnZvz2o9ycp6WJUY+Pj5Yv3491NXV0ahRI/Tq1QuTJk2S20ZFxJaV8ePH4+LFi7h06RK2b9+OXr16wdTUFP7+/li9erXc96Jly5YYNmxYuk3zZDenZDcYs5LTMmSJpp6entzo4E5OTmLz3++b8uXku5GZNWvWYOPGjfD29kbx4sXRtWtXjBw5Uq4bSL169bB7926sWbMG/v7+iI6ORrly5dCjRw8MHjw4102cZerUqYNDhw6J79G9cOECYmNjUa5cOXTv3h0DBgxI04xx0KBBqFatGv73v/9h3bp1AL41yZw+fTq6d+8u93s8Y8YMFC9eHMeOHcONGzdgZGSEiRMn4uPHj1i4cGGW8eVkX+fFMZ3f15Tpycn+Br6NIG9hYQFfX198+fIFZmZmWLVqVbaevA4fPhxGRkbYvHkzli9fDjU1NZiYmGDp0qViQpgRXV1d+Pn5YdGiRdi5cyeio6NRvXp1LFq0KE0ynNNr5xEjRiAsLAzr1q3D169fYW5ujlmzZqUZXDI91apVw9GjR7F161acO3cOmzZtQkxMDIoXL46aNWtme99Mnz4d165dw8KFC+Hs7Jzl8ukxNjbG9u3bsWbNGvEmdYkSJVC3bl0MHz482/0zAUAiZNWDt4Dx8fHBypUr8ffff2f6mJqIsqddu3aYPXu2wgYIIlKmuXPnokyZMj/1MmvKPUEQ8PHjx3RvXB06dAjjx4/H/Pnzs/VEkPLWpEmTsH//fpw4cSJbiTNRdoSFhaFJkyZo27YtFi9erOxwKI/lbhhTIioUzp07h/Dw8Axff0FUkLx69QrHjx/PcLRHyh9NmjRJMwiNIAhiX1/e1CIi+jXkbvxgIioUoqKisHnzZoU2SyZSloiICEyfPp2JphLJ+ozu2LEDQ4cORYMGDZCSkoIzZ87g0qVL6Nmzp/guPSIiKtiYaBJRhtzc3JQdApHCMMFUDVOmTEG1atXg7++PRYsWAQCqV6+OuXPnioMYERFRwffL9dEkIiIiIiIi5WIfTSIiIiIiIlIoJppERERERESkUOyjSXkiNTUVycnJUFNTy/XL54mIiIgo7wiCgNTUVKirq2frXd9EOcFEk/JEcnIy7t+/r+wwiIiIiCgLlpaW0NDQUHYY9Ithokl5QnZXzNLSElKpVMnRqJ6UlBTcv3+f++cXxLr9dbFuf12s218b6zdjsn3Dp5mUF5hoUp6QNZeVSqU8qWeC++fXxbr9dbFuf12s218b6zdj7OZEeYG3L4iIiIiIiEihmGgSERERERGRQjHRJCIiIiIiIoViH00iIiJSqNTUVCQmJio7jGxLSUkBAMTHx7MP3y+osNevhoYGB/shpWCiSURERAqTmJiI58+fIzU1VdmhZJsgCFBXV8fLly85KMovqLDXr5qaGoyMjPj6Esp3TDSJiIhIIQRBwNu3byGVSlG5cuUC8xRFEATExcVBW1u7UCYiv7rCXL+pqal48+YN3r59iypVqhS67SflYqJZCCUmJqJDhw6YNm0aateune4yDx8+xIwZM/Dff//B2NgYs2bNgoWFRT5HSkREBUlycjJiY2NRoUIF6OjoKDucbBMEAampqdDS0uKF+C+osNdv6dKl8ebNGyQnJ6NIkSLKDocKkYJxq5EUJiEhAR4eHnj8+HGGy8TGxmLQoEFwcHCAv78/bG1tMXjwYMTGxuZjpEREVNDI+sKxiR6R6pB9H2XfT6L8wkSzEHny5Am6dOmCV69eZbpcYGAgNDU1MWHCBFSvXh1TpkxB0aJFcezYsXyKlIiICrLC+NSISFXx+0jKwkSzELl+/Tpq166NXbt2Zbrc3bt3YW9vL56YJBIJ7OzsEBQUlA9REhERERFRQcc+moVIjx49srVcREQEjI2N5aaVLFky0+a2REREBdGkSZOwf//+DOdv2bIlw/EMAODo0aNwdHREyZIlsyzL3d0djo6OGDlyZJp5Pj4+WLlypdw0LS0tVKlSBSNHjkTz5s2zXH9WXFxcMGLECHTo0OGn10VElBUmmpRGXFxcmv41GhoauXonGvsDpC81NRXa2toFavh/yh7ZMc9j/9fDus1aSkoKBEEQ/30vP5vv/Vh2Zjw9PTF27FjEx8fj7Nmz+Pvvv7Fnzx5xfvHixTNc3+vXrzFmzBicOnUKBgYG2YorvX0jm2djYwMfHx9xWmRkJDZu3AgPDw8cPnwYhoaG2d6u9OzZswc6Ojo52j+/Atn2FrbtlpEdcykpKWnOXzyfUV5ioklpaGpqpkkqExMToaWlleN13b9/X1FhZUuRIkVQ06ImpGr590JmQUiFRJKzVuhSqRTm5ua5LE/Itwu2lJRU/PvvAyQlJeVLeb+S/D72Kf+wbjOnrq6OuLg48UaampoatLW1cefjc0Qnxed5+cWKaMG2pJFcDJmRSqXQ1dWFrq4uNDU1IZFIULRoUXF+cnIykpOT0/1sXFwcACA+Pj5bA+alpqYiKSkp3WWTkpIglUrlyi5atCimTJmCEydO4OTJk9lumZQRLS0tpKamFtrB/WT1VdgkJCQgKSkJwcHByg6FChkmmpRG2bJl8eHDB7lpHz58QJkyZXK8LktLS0il+Zf0AYBUTZpvFzRltPRgVqIiUp9fB+Kj87w86JWFWkULPAv7jPjE9C98FEVLQx3VKhVHzZo187ScX01KSgru37+vlGOf8hbrNmvx8fF4+fIltLW109ycjE6Kx5ek/LvQz8nNUdl7FjU0NCCRSORezfLu3TssWLAAV65cgUQiQZs2bTBhwgRoaGigTZs2AIA2bdpg3rx5aN++PdatW4c9e/bg/fv3KFGiBLp27YoRI0YA+JZ0FylSJN1XvxQpUgRqampp5qWkpKBIkSLQ1tYW5+3cuRMbNmxAZGQkatasialTp8LU1BQ7d+7E+vXrcebMGfHzu3btgq+vL44fPy7XdFYQBKxZswZ+fn6Ij4+Hvb09pk+fjgoVKmDYsGEwNDTExIkTAQDTpk3DpUuXxPVevHgRU6dOxblz5xAYGIgVK1bgzZs3qFy5MsaOHYumTZtme9/nh8L8Hk3g/447Y2PjNN8L2XmNKC8w0aQ0rK2tsWHDBvHJmSAIuH37NoYMGZLjdUmlUqVckOXXBY2u+v8/YcdHA3FReV4etIp9Ky4xGbHxeZtoyvCCOneUdexT3mPdZkwqlUIikYj/lCm35X8fe2JiIvr27QtDQ0Ns3boVnz59wrRp0yCRSDB16lTs2bMHnTt3xp49e2BiYoIDBw5gy5YtWLp0KSpXrowLFy5g5syZcHFxQc2aNTPdN+nNi42Nxbp165CYmIhGjRpBIpHgzJkzWLVqFebMmQMjIyMEBASgT58+OHHiBFq0aIG5c+fi33//Fd99ffLkSbRq1Upu/RKJBNu2bcOhQ4ewZMkSlCpVCr6+vujfvz8OHToEZ2dn7Nu3T4zlxo0bePv2LcLDw1GuXDlcvnwZzs7O+PTpEyZOnIjZs2ejdu3aOHbsGMaNG4d//vkHJUqUyNX+z0uqcFwqg2y7ee6i/MZRZwnAtwGA4uO/PQFs2bIlvnz5Ai8vLzx58gReXl6Ii4tDq1atlBwlERFR/rlw4QLCw8OxaNEimJqaom7dupg+fTr8/Pzw9etXsV+mgYEBtLS0UL58ecyfPx9169ZFpUqV0L17d5QuXTrbg+ndvHkTtra2sLW1hY2NDezt7XHx4kVs2LABlSpVAgBs3LgRgwcPRuPGjVG1alWMGTMGFStWxMGDB2FgYIA6dergxIkTAIDPnz/j2rVrcHV1TVPWxo0bMWHCBNSuXRvVq1fH7Nmz8fnzZ1y4cAHOzs4IDg5GdHQ0IiIiEBUVBWtra9y+fRsAcOXKFdSvXx/h4eFISkpCuXLlULFiRfzxxx9YvXo1NDU1FbH7iaiAY6JJAABnZ2cEBgYCAHR1dbFu3TrcunULHTp0wN27d7F+/fp0m/oQERH9qp4+fYqqVauiePHi4jQ7OzskJyen+07qOnXqQF9fH0uWLMGwYcPQuHFjREREZHvgNwsLCwQEBMDf3x8eHh4oWrQo+vXrJzfq7dOnT7Fo0SIxIbW1tUVwcDBevHgBAGjdurWYaJ4+fRqGhoYwNTWVK+fr16949+4dxo4dK67DwcEBUVFRePHiBQwNDVGhQgXcvHkTN27cEJe5desWPn36hCdPnqBevXqoUaMGGjVqhH79+qFly5ZYvHgxKlWqBG1t7ZzuaiL6BbHpbCEVEhKS6d9WVlaZDvdORET0q0vvyVxmow/v2bMH8+bNQ+fOndG8eXNMnDgRvXv3znZ5Wlpa4siyRkZGiI+Px8SJE1G5cmVYW1uL5Xp6eqJu3bpyn9XV1QUANGvWDDNmzMDjx49x4sSJdFsjyWJfvnw5jIyM5ObJkmonJydcv34dCQkJsLOzg5GREVavXo2rV6/C0tISenp6AIB169bh3r17OH36NE6ePIkdO3Zgx44dqFGjRra3m4h+TXyiSURERJQOIyMjvHjxAlFRUeK0oKAgqKuro0qVKmn6+/n5+WH48OHw9PSEm5sb9PX18fHjx1y/VqN///747bffMHXqVDE5NDIywrt372BoaCj+W7t2LYKCggAAxYoVQ/369XH06FFcvnwZrVu3TrNePT09lCxZEhEREeI6ypcvj0WLFuH58+cAgPr16+P69eu4ffs2HBwcYG9vj//++w/Hjx9H/fr1AXx7uurt7Q0rKyuMHTsWR44cQfny5XHhwoVcbS8R/VqYaBIRERGlw8nJCZUrV8aECRMQEhKCq1evYs6cOWjTpg309PTEJqLBwcH4+vUr9PX1ceXKFTx//hwPHjzA2LFjkZSUlKv3UAPfBleaNm0a/vvvP+zYsQMA0K9fP2zevBkBAQF49eoVFi1ahKNHj6J69eri51q3bo2///4b1apVS/PEUqZv375YtmwZzpw5gxcvXmDq1Km4ffs2qlWrBuBbM+D//vsPL1++hIWFBQwMDFClShW5RFNPTw9+fn5YvXo1QkNDce7cObx+/TrXr+8iol8Lm84SERFRnitWJOfvYlZ2OVKpFKtXr8acOXPQpUsXFC1aFG3btoWHhweAb4MAtWvXDmPGjMH48ePh6ekJT09P/P777yhZsiRatWoFbW1tPHr0KNcx2Nvbo127dlixYgVat24NV1dXfPjwAStWrMCHDx9gbGyMNWvWoGrVquJnGjduDEEQ0h0ESKZ///74+vUrpk+fjpiYGFhYWGDTpk1i01ldXV1YWlpCIpFAQ0MDAMR+nLIRbUuXLg0fHx8sXrwYa9euRcmSJeHh4QFnZ+dcby8R/TokQm7bcxBlIiUlBUFBQbCxsVHKUNr/vHuUL683qaCtD7tSRkh9dDp/Xm+iXxlqRo54+Oxjnr/eREdLHebVSuZpGb8iZR/7lHdYt1mLj4/H8+fPYWRkJPe+PtnrsvJLTssTBAGxsbHQ0dEplK+/+NUV9vrN6HsJ8LxGeYtNZ4mIiChP5ffFfWFMJoiIVA0TTSIiIiIiIlIoJppERERERESkUEw0iYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTTSIiIirUzMzM4OnpmWa6v78/XFxc8qRMFxcX+Pv758m6s+P06dNo0KABrK2tceHChXSXefv2LaZOnYoGDRrAxsYGbm5uCAgIyN9AiajAYqJJREREeUoQBJUv79ixY7h69WoeRKOaVqxYAWdnZwQGBqJWrVpp5r948QIdO3ZEVFQUli9fjoMHD6J79+6YMWMGfH19lRAxERU06soOgIiIiH5tEokEqc+vA/HReV+YVjGoGTnm+GMVKlTA7NmzceDAAWhoaORBYKolOjoa9vb2qFixYrrzZ82aBTMzM/j4+EAikQAAqlSpgsTERCxduhSdOnWCnp5efoZMRAUMn2gSERFR3ouPBuKi8v5fLpPZYcOGITw8HJs2bUp3flhYGExNTREWFiZO8/Hxgbu7O4BvzWzd3d2xZs0a1KpVC05OTggICMCxY8fQuHFjODg4YNGiRXLrfPz4Mdzc3GBpaYn+/fvjzZs34ry3b99iyJAhsLa2houLC1auXImUlBSxrG7dumH48OGwt7fHwYMH08SbkJCARYsWoWHDhrCxscGQIUPw9u1bAN+a7b5+/Rqenp7pNg1+9+4drly5gr59+4pJpkynTp2wYcMG6OjoiMuOHj0ajo6OqF27NubOnYvExEQxzh/X7+7uDh8fHwDApEmTMGnSJLRr1w5169bFixcvEBgYiBYtWsDS0hKurq44depUtvZJUlISpk6ditq1a8PW1hZDhgxBeHh4unVJRPmDiSYREREVemXKlMHIkSOxdu1ahIaG5modd+7cQWhoKPbu3YvWrVtj5syZ2LJlC9asWYNJkyZh48aNePjwobi8n58fBgwYgH379iE5ORkTJ04E8K3p74gRI1CyZEns378f8+fPx6FDh7B27Vq5soyNjbF79244OzuniWXGjBk4efIkvL29sXPnTiQnJ2PYsGFITU3F3r17Ua5cOXh6emLv3r1pPhsSEgJBEGBpaZlmnra2NhwcHKCuro7ExET06dMHcXFx2Lp1K5YtW4Zz585h4cKF2d5nBw4cwJgxY7Bu3ToUK1YMEyZMwODBg3Hs2DF07NgRHh4eiIqKynKfbN++HTdu3ICvry/27t2Lr1+/Yt68edmOg4gUj4kmEREREb49bTM0NISXl1euPi8IAqZOnQpDQ0N07doVcXFxGDlyJMzMzNCpUyeULFkSz549E5fv3r072rRpAxMTE3h5eeH69et4+vQprl69ijdv3mDOnDmoVq0aateujYkTJ2LLli3iZyUSCYYOHYrq1avDwMBALo7Pnz/jwIEDmD59OurUqQMzMzMsXrwYz58/x6VLl2BgYACpVIpixYql+SwAfPnyBQBQrFixTLf3woULCA8Px6JFi2Bqaoq6deti+vTp8PPzw9evX7O1zywtLeHi4gIrKyuEh4cjKSkJ5cqVQ8WKFfHHH39g9erV0NTUzHKfhIWFQVNTExUrVkT16tWxYMECDBo0KFsxEFHeYB9NIiIiIgBSqRQzZ85Ejx495JpsZlfJkiXFJqWampoAgEqVKonztbS0xGalAGBlZSX+v1KlSihRogSePXuG8PBwREVFwd7eXpyfmpqK+Ph4REZGimVpaWmlG8eLFy+QmpoKa2trcVqJEiVgZGSEp0+fon79+pluR4kSJQB8SzjTS0Rlnj59iqpVq6J48eLiNDs7OyQnJ+PVq1eZliHzfR/RGjVqoFGjRujXrx+MjIzQpEkTdO7cGdra2nj69Gmm+6Rr1644cuQInJ2d4ejoiKZNm6JDhw7ZioGI8gYTTSIiIqL/z87ODh07doSXlxcGDBggTv+xryIAJCcny/2trp72siq9z8lIpVK5v1NTU1GkSBEkJyejWrVqWL16dZrPyJ4yyhLZ9GQ0LyUlBampqRl+TqZmzZqQSCR48OABGjRoIDcvNjYWw4cPx8SJE9MtR9ZnMiUlJVv77Pt1SCQSrFu3Dvfu3cPp06dx8uRJ7NixAzt27Mhyn+jr6+PMmTM4d+4czp07h6VLl+Lw4cPYvn17lttLRHmDTWeJiIiIvjN+/HjExsbKDQxUpEgRAJBrEvr9wEC58d9//4n/f/HiBb58+QIjIyMYGRnhzZs3MDAwgKGhIQwNDREWFoYVK1ZkmrjKVK5cGerq6ggKChKnRUZG4uXLlzAyMsry8wYGBnBycsLmzZvTvCpm3759uHnzJsqXLw8jIyO8ePECUVFR4vygoCCoq6ujSpUqKFKkiNz+EgQh03329OlTeHt7w8rKCmPHjsWRI0dQvnx5XLhwIct9EhAQgLNnz6JVq1bw9vbGxo0bcevWLXz8+DHL7SWivMFEk4iIiOg7+vr6GD9+PF6/fi1OK1WqFMqXL49NmzYhNDQU/v7+OHfu3E+V8/fff+PEiRMIDg7G5MmT0bhxYxgaGsLZ2RkVK1bEn3/+iZCQENy8eRPTpk2DtrZ2mqeg6SlatCg6d+6MOXPm4Nq1awgODsaff/6JcuXKwcnJKVuxTZ48Gffu3cPo0aNx7949PH/+HL6+vli0aBHGjRuH4sWLw8nJCZUrV8aECRMQEhKCq1evYs6cOWjTpg309PRgYWGBqKgobN26FaGhoZg/fz4+f/6cYZl6enrw8/PD6tWrERoainPnzuH169cwNzfPcp9ER0fDy8sLV65cQWhoKA4dOoRy5cpBX18/2/VBRIrFRJOIiIjynlYxQLtE3v/TynwAm+zq1KkTbG1txb/V1NTg5eWFe/fuwdXVFceOHcOQIUN+qox+/fph2bJl6NKlC0qWLCmOkiqVSrFmzRqkpqaiS5cuGDlyJBo2bIipU6dme90TJ05EvXr1MGrUKHTv3h2ampr43//+l+13hBobG2PHjh0AgKFDh6J9+/Y4fPgwvLy80LdvXzFOWVPWLl26wMPDA02aNMHs2bMBAFWrVsXEiROxZs0auLm5QRAEtGjRIsMyS5cuDR8fHxw/fhytW7fG7Nmz4eHhAWdn5yz3Sc+ePeHm5oY///wTrq6uePjwIdasWZOtxJyI8oZE+LFNBJECpKSkICgoCDY2Nko5yf/z7hG+JMXleTkVtPVhV8oIqY9Of3t/W17Trww1I0c8fPYRsfHJWS//E3S01GFerWSelvErUvaxT3mHdZu1+Ph4PH/+HEZGRnID1QiCkK0mn4qS0/IEQUBsbCx0dHTyNU7KH4W9fjP6XgI8r1He4hNNIiIiylP5fXFfGJMJIiJVw0STiIiIiIiIFIqJJhERERERESkUE00iIiIiIiJSKCaaREREREREpFBMNImIiLJJW1tb2SEQEREVCOrKDoCIiCi/5eZ1G1KpFObm5vlWHhERUUHGRJOIiAodiUSC1OfXgfjovC9MqxjUjBzzvhwiIiIVwkSTiIgKp/hoIC5K2VEQERH9kthHk4iIiOj/8/f3h6mpKfbs2aPsUBRCtj3f/7O0tESLFi2wY8cOhZTh7u4OHx8fhaxLVb19+xZTp05FgwYNYGNjAzc3NwQEBCg7LCKVxieaRERElKfyu4/qz5R35MgRVKlSBQcOHEDnzp0VHJlylCtXDnv37hX/jo6Oxt69ezFr1iwYGxvD0fHnmnb7+PigSJEiPxumynrx4gV69OgBOzs7LF++HCVLlsSVK1cwY8YMfPr0CX/88YeyQyRSSUw0iYiIKE9JJBI8C/uM+MTkPC9LS0Md1SoVz9VnP378iCtXrmDevHmYNGkSQkNDUblyZQVHmP+kUilKly4t/l26dGlMmDABp06dwqlTp3460SxRosRPRqjaZs2aBTMzM/j4+Ig3MKpUqYLExEQsXboUnTp1gp6enpKjJFI9bDpLREREeS4+MRmx8Xn/72eS2WPHjqFYsWJo164dypQpgwMHDgAAxo4di4kTJ8otO27cOEyZMgXAt2aVQ4YMgbW1NVxcXLBy5UqkpKQA+NZ0tVu3bhg+fDjs7e1x8OBBxMTEYPLkyahbty4sLCzQsmVLnDp1Slx3ZGQkRowYAVtbWzRp0gR+fn4wNTUV5//3339wd3eHlZUVWrRoge3bt+dqezU0NCCVSsW/T548CVdXV1hbW6NTp064fv06AOCff/6BtbU14uLixGUvXrwIOzs7xMfHp2k6u3PnTri4uMDW1hbu7u4ICQkBAMydOxejRo0Sl1uzZg0sLCyQkJAAAHj+/DksLS0RGxuLK1eu4Pfff4elpSWaNGmCnTt3prsNYWFhMDU1xaFDh1C/fn04ODhg7ty5SE7+v+Pg5MmT6NixI2xsbOS2C/jW7HfOnDlo0qQJGjVqhJiYGLn1v3v3DleuXEHfvn3TPCXv1KkTNmzYAB0dHXHZ0aNHw9HREbVr18bcuXORmJgI4Ntx4OLiIvf57/fbpEmTMGnSJLRr1w5169bFixcvEBgYiBYtWsDS0hKurq5yx0hmxxyRqmCiSURERAQgMDAQjRo1gpqaGlxcXBAQEABBENC6dWucPXsWSUlJAIDExEScPXsWrVu3hiAIGDFiBEqWLIn9+/dj/vz5OHToENauXSuu986dOzA2Nsbu3bvh7OwMLy8vPH/+HL6+vjh8+DAcHBwwZcoUMSnx8PDAp0+f4Ofnh+nTp2PVqlXiuuLj4zFw4EAxaZ04cSJWr16do/6CiYmJ2L59O548eYLmzZsDAIKDgzFx4kQMHToUBw8eRLt27TBw4EC8fPkS9erVg7a2Nv755x9xHSdOnICLiwu0tLTk1n3mzBmsXLkS06ZNw/79+2Fvb4/evXvj8+fPqF+/Pm7cuAFBEAAAN27cQHJyMu7fvw8AuHz5Muzt7aGpqYkxY8agZcuWOHr0KEaPHo1Zs2bhyZMnGW7TypUr8ddff2HlypU4ceKEmMAFBwdj0qRJGDBgAA4cOCC3XTL+/v5YtGgRVq5cCV1dXbn1hoSEQBAEWFpapilTW1sbDg4OUFdXR2JiIvr06YO4uDhs3boVy5Ytw7lz57Bw4cJs18uBAwcwZswYrFu3DsWKFcOECRMwePBgHDt2DB07doSHhweioqKydcwRqQImmkRERFTovXv3Drdv30bTpk0BAM2bN0doaChu3bqFBg0aIDU1FdeuXQPw7WmelpYWateujatXr+LNmzeYM2cOqlWrhtq1a2PixInYsmWLuG6JRIKhQ4eievXqMDAwQK1atTB79mzUqFEDVatWxR9//IGoqCh8/PgRz58/x+XLl+Ht7Q0zMzM0bNgQI0aMENd16NAhlCxZEmPGjEHVqlXh4uKCIUOGyJX3ozdv3sDW1lb8Z21tjR07duCvv/6Cra0tAGDTpk3o0qUL2rZtC0NDQ/Tu3RsNGjSAn58f1NXV0bx5c5w4cQIAkJKSglOnTsHV1TVNWRs3bsTgwYPRuHFjVK1aFWPGjEHFihVx8OBBODo6Ijo6Go8fP0ZycjKCgoLg7OyM27dvA/iWaNavXx/R0dGIiopCqVKlUKlSJbRr1w5///23XPPfH/35559wcHBAnTp1MHr0aOzevRuCIGDTpk3o3LkzWrVqlWa7ZBo1agQ7OztYWFikWe+XL18AAMWKFcuwbAC4cOECwsPDsWjRIpiamqJu3bqYPn06/Pz88PXr10w/K2NpaQkXFxdYWVkhPDwcSUlJKFeuHCpWrIg//vgDq1evhqamZraOOSJVwD6aREREVOgdP34cmpqacHZ2BgA4OjqiePHi2L9/PxwcHNC0aVOcOHECzs7OOHHiBFq0aAGpVIqnT58iKioK9vb24rpSU1MRHx+PyMhIAEDJkiXlnvy5ubnh1KlT2L17N549e4Z///0XwLcELiQkBCVKlJDrG2pjYyP+/9mzZwgODhYTRNnnvm8C+6MyZcpg69atEAQBd+/exbx589CxY0e0atVKXObp06c4evQodu3aJU5LSkoS90fr1q0xbNgwJCYm4s6dO3Lzvvf06VMsWrQIS5cuFaclJCTgxYsX0NbWhr29Pa5fv474+HhUrFgRDRs2xKVLl5CSkoLr169j9OjRKFGiBLp3746pU6di9erVaNy4MTp27IjixTPue2tnZyf+38LCAp8+fUJkZKTcdsmavv4Ye8WKFTNcr6z/6ZcvX2BgYJDhck+fPkXVqlXlYrSzs0NycjJevXqV4ee+930cNWrUQKNGjdCvXz8YGRmhSZMm6Ny5M7S1tbM85vT19bNVHlFeY6JJREREhd7x48cRHx8vd/GekpKCY8eOYdq0aXB1dcXkyZMxdepUnDlzRmzOmpycjGrVqmH16tVp1il7CqapqSk3fcKECbhz5w5+//13dO/eHaVLl0bXrl0BAOrq6mLT0vQkJyeLT8uyS11dHYaGhgCAqlWrQl1dHR4eHqhUqZLYdDYlJQUDBw6Em5ub3GdlCXKtWrWgo6ODy5cv48KFC2jatCk0NDTSlJWSkgJPT0/UrVtXbrqsSaqTkxOuX7+OhIQE2NnZwd7eHitXrsT9+/eho6MDExMTAMDMmTPRs2dPccCiXbt2YfXq1WjYsGG62/j9qLepqakAvj1Jlm1X8+bNoa2tLSab3yf+P9bP92rWrAmJRIIHDx6gQYMGcvNiY2MxfPhwTJw4Md11yPpMpqSkpDsK8vf9SH+MQyKRYN26dbh37x5Onz6NkydPYseOHdixY0e2jjkiVcCms0RERFSoPX/+HMHBwZgyZQoCAgLEf3/99RdiYmJw8uRJ1KtXDykpKfj777+hpaUFBwcHAICRkRHevHkDAwMDGBoawtDQEGFhYVixYkW6yUVMTAwOHz6Mv/76C6NGjUKzZs3w+fNnAN9ey1K9enV8/vwZoaGh4mcePHgg/t/IyAjPnz9HpUqVxPKCgoKwdevWbG9v69at0bhxY8yaNUsc/MbIyAhhYWHiOg0NDbFr1y6xX6aamhpatmyJc+fO4fTp02jdunW66zYyMsK7d+/k1rN27VoEBQUBgNhP89atW3BwcICZmRmSk5OxZcsW8SljREQEZs2aBUNDQwwdOhT79u1DnTp1cObMmQy36dGjR3L7q0yZMtDX1xe3q0qVKuluV1YMDAzg5OSEzZs3p7kBsG/fPty8eRPly5eHkZERXrx4gaioKHF+UFAQ1NXVUaVKFRQpUkSuCa0gCAgLC8uw3KdPn8Lb2xtWVlYYO3Ysjhw5gvLly+PChQs5PuaIlIWJJhERERVqR44cQfHixdG1a1eYmJiI/1xdXWFsbIyAgACxn+LatWvRsmVL8YLe2dkZFStWxJ9//omQkBDcvHkT06ZNg7a2drrNWTU0NKCtrY0TJ04gLCwMFy5cwOzZswF8G6THyMgIzs7O8PT0RHBwMC5duoQVK1aIn2/Xrh3i4+Mxffp0PH36FOfPn4eXlxdKliyZo22eMmUKvnz5gpUrVwIA+vbti8DAQGzZsgWvXr3C//73P/zvf/9D1apVxc+0bt0aBw4cQEJCAurUqZPuevv164fNmzcjICAAr169wqJFi3D06FFUr14dAGBmZgY1NTX8888/sLe3h5qaGmxtbREYGIj69esDAIoXL46TJ09i3rx5ePXqFW7cuIHg4GCYm5tnuD1eXl64f/8+Ll++jOXLl6Nnz55y2+Xn55fhdmVl8uTJuHfvHkaPHo179+6JAzktWrQI48aNQ/HixeHk5ITKlStjwoQJCAkJwdWrVzFnzhy0adMGenp6sLCwQFRUFLZu3YrQ0FDMnz9fvMGQHj09Pfj5+WH16tUIDQ3FuXPn8Pr1a5ibm+f4mCNSFiaaRERElOe0NNSho5X3/7Q0ct4rKDAwEK6uruk2Be3evTsuX76M8PBwtG7dGrGxsXJP86RSKdasWYPU1FR06dIFI0eORMOGDTF16tR0y9LQ0MCiRYtw/PhxtG7dGgsWLMDQoUNRunRp8anc/PnzoaOjgy5dumDmzJno0KGD2DRUV1cXGzZswIsXL+Dm5oapU6eiZ8+eGDx4cI62uXLlyujfvz+2bduGp0+fwsbGBgsXLsSOHTvg6uqK3bt3Y8mSJahVq5b4GRsbG+jr66N58+ZQV09/P7u6umLs2LFYsWIF2rRpgytXrmDNmjViYieRSFCvXj2UKlUKFSpUAAA4ODhATU0N9erVE/fR6tWrERwcjHbt2mHMmDHo1KkTOnfunOH2uLq6YvDgwfDw8EDnzp0xaNAgMWZvb2/s2bMHrVu3Tne7smJsbIwdO3YAAIYOHYr27dvj8OHD8PLyQt++fQF8Ow5kTVm7dOkCDw8PNGnSRLyJULVqVUycOBFr1qyBm5sbBEFAixYtMiyzdOnS8PHxEY+T2bNnw8PDA87Ozjk+5oiURSJk1hGAKJdSUlIQFBQEGxsbpdxd++fdI3xJist6wZ9UQVsfdqWMkProNBAXleflQb8y1Iwc8fDZR8TG5+2Lz3W01GFeLWd3yEn5xz5lX759b7VLQK1Gk7wvRwXEx8fj+fPnMDIykusDJwhCvjbpy2l5giAgNjYWOjo6Sm96GBcXh8uXL6NBgwZicnn06FEsWrQo06ajhVVYWBiaNGmC06dPo1KlSukuo0r1qwwZfS8B/mZR3uITzUImISEBnp6ecHBwgLOzM3x9fTNc9uTJk2jVqhVsbW3RvXt3cVQ8IiKinMjvi/uCnExoamrC09MTq1atQmhoKO7cuYNVq1Zl+vSLiEgVMdEsZBYuXIgHDx5g8+bNmDFjBlauXIljx46lWe7x48cYN24cBg8ejAMHDqBGjRoYPHgw4uLy/ikhERFRYaWmpoZVq1bh8uXLaNOmDUaMGIH69etj7Nixyg6NiChH+HqTQiQ2NhZ79uzBhg0bULNmTdSsWROPHz/G9u3b0bJlS7llL126BGNjY3GYcw8PD2zfvh1PnjyBpaWlEqInIiIqHBwcHLB7925lh1EgVKpUCSEhIcoOg4jSwSeahUhwcDCSk5PlXvJsb2+Pu3fviu+ckilRogSePHmCW7duITU1Ff7+/tDV1UWVKlXyO2yiQiO/u8yziz4RERHlFT7RLEQiIiKgr68vN6peqVKlkJCQgKioKBgYGIjTXV1dcebMGfTo0QNSqRRqampYt24dihcvrozQiQoFiUSCZ2GfEZ+YtwM9Ad9GAK1Wid9nIiIiyhtMNAuRuLi4NEO3y/5OTEyUmx4ZGYmIiAhMnz4d1tbW8PPzw+TJk7F///4cvasrJSXl5wPPIY6apljKqMOCTLa/crPfpFIp4hOT83xE4e8V1vpVxnmiMOzrlJQUCIKA1NTUAvXEXBZrQYqZsq+w16/s+5iSkpLmPFQYzkukPEw0CxFNTc00CaXs7x+Hu168eDFMTEzEFx7PmTMHrVq1wr59+8R3U2XH/fv3fzLqnNHW1s70hc6UcyEhIRwEKhdyeuwr69gtjPXLfZ23JBIJ3r59ixIlShS40V8TEhKUHQLlocJYv4IgICoqCvHx8QgODlZ2OFTIMNEsRMqWLYvIyEgkJyeLL1qOiIiAlpYW9PT05Jb9999/4e7uLv6tpqYGMzMzvHnzJkdlWlpa8gljAWdqaqrsEAqUlJQU3L9/v8Ac+6zf/FNY9vXXr1/x+vVrhIeHKzuUHElKShLfW0m/nsJcvxKJBMbGxihatGiaebLfLKK8wESzEKlRowbU1dURFBQEBwcHAMCtW7dgaWkJNTX5caHKlCmDp0+fyk17/vx5jkeclUqlBeJimzLG+sudgnLsF4QYfxWFZV/r6emhaNGiSEpKUnYo2ZaSkoLg4GAYGxsXmnoqTAp7/RYpUqRQbjcpHxPNQkRbWxtubm6YOXMm5s2bh/fv38PX1xfz588H8O3pZrFixaClpYUuXbpg0qRJsLCwgK2tLfbs2YM3b96gffv2St4KIiJSdQXlRouMrJ+alpZWgYqbsof1S6QcTDQLmcmTJ2PmzJno06cPdHV1MXLkSDRv3hwA4OzsjPnz56NDhw5wdXXF169fsW7dOrx79w41atTA5s2bczQQEBERERERFU5MNAsZbW1teHt7w9vbO828H1943LlzZ3Tu3Dm/QiMiIiIiol+EWtaLEBEREREREWUfE00iIiIiIiJSKCaaREREREREpFBMNImIiIiIiEihmGgSERERERGRQjHRJCIiIiIiIoVioklEREREREQKxUSTiIiIiIiIFEpd2QFQ5j59+oRNmzbh8uXLiIiIwMaNG3Hq1CmYmZmhadOmyg6PiIiIiIgoDT7RVGGhoaFo164ddu/ejbJly+Ljx49ISUnB8+fPMWrUKJw7d07ZIRIREREREaXBJ5oqzNvbGyVLlsTWrVuho6MDCwsLAMCSJUuQkJCAtWvXolGjRsoNkoiIiIiI6Ad8oqnCrly5gmHDhkFPTw8SiURuXteuXfH48WMlRUZERERERJQxJpoqTl09/YfOiYmJaZJPIiIiIiIiVcBEU4U5ODhg3bp1iI2NFadJJBKkpqbCz88PdnZ2SoyOiIiIiIgofeyjqcLGjRuH7t27o3nz5qhduzYkEgk2bdqEp0+f4uXLl9ixY4eyQyQiIiIiIkqDTzRVmImJCfbt24fatWvj2rVrkEqluHz5MqpUqYKdO3eiRo0ayg6RiIiIiIgoDT7RVGH79+9HvXr1sGTJEmWHQkRERERElG18oqnCZs+ejXv37ik7DCIiIiIiohxhoqnCypUrh5iYGGWHQURERERElCNsOqvCunbtCi8vL9y5cwempqYoWrRommXc3NzyPzAiIiIiIqJMMNFUYQsWLAAA7N69O935EomEiSYREREREakcJpoq7PTp08oOgYiIiIiIKMeYaKqwihUriv+Pi4tDTEwMSpQogSJFiigxKiIiIiIioswx0VRxN2/exMKFC/HgwQMIggAAsLKywtixY1GnTh0lR0dERERERJQWE00Vdvv2bfTt2xeVK1fGsGHDUKpUKbx//x5HjhzBgAEDsHXrVtja2io7TCIiIiIiIjlMNFXYsmXL4ODggE2bNkEqlYrTR4wYgf79+8PHxwe+vr5KjJCIiIiIiCgtvkdThd2/fx+9e/eWSzIBQE1NDb169cK9e/eUFBkREREREVHGmGiqsKJFiyI5OTndecnJyWKfTSIiIiIiIlXCRFOF2dnZYf369YiLi5ObHhsbi/Xr18PBwUFJkREREf1atLW1lR0CEdEvhX00Vdi4cePQoUMHNGnSBI0aNULp0qURERGBc+fOIT4+Hl5eXsoOkYiISOUIggCJRJLt5aVSKczNzfOlLCKiwoKJpgozNDTErl27sHLlSpw/fx6fP39G8eLF4ejoiBEjRsDY2FjZIRIREakciUSC1OfXgfjovC1IqxjUjBzztgwiogKKiaaKMzY2xvTp02FgYAAA+Pz5MyIiIphkEhERZSY+GoiLUnYURESFFvtoqrDo6GgMGDAAPXv2FKfdvXsXbdq0wahRoxAfH6/E6IiIiIiIiNLHRFOFLV68GI8ePcLIkSPFaXXq1IGPjw9u374NHx8fJUZHRERERESUPiaaKuzMmTOYOHEiXF1dxWkaGhpo1qwZPDw8EBgYqMToiIiIiIiI0sdEU4XFxMSgePHi6c4rXbo0Pn36lM8RERERERERZY2JpgozMzPDvn370p0XEBAAU1PTfI6IiIiIiIgoaxx1VoUNGTIEQ4YMQYcOHdCsWTOULFkSnz59wtmzZ3H//n2sWbNG2SESERERERGlwURThTVs2BCrV6+Gj48PVqxYIb4UukaNGli9ejUaNmyo7BCJiIiIiIjSYKKp4ho3bozGjRsjISEBUVFRKFasGHR0dJQdFhERERERUYbYR7OA0NTURHR0NP755x88e/ZM2eEQERERERFliImmCjp16hTatm2Lbdu2idO8vb3Rtm1bjBkzBq1bt8bs2bOVGCEREREREVHGmGiqmBs3bmDUqFHQ0NBA9erVAQCXL1/G33//DXt7ewQEBGDJkiUICAjIcERaIiIiIiIiZWIfTRWzadMmODk5Yd26dVBT+3YfwM/PDxKJBPPnz0flypVhZmaGx48fY/fu3ejYsaOSIyYiIiIiIpLHJ5oq5u7du+jcubOYZKampuLKlSswNjZG5cqVxeUcHR3x+PFjZYVJRERERESUISaaKiY6OhoGBgbi3yEhIYiJiUHt2rXlllNTU0Nqamp+h0dERERERJQlJpoqplSpUnj79q3495UrVyCRSFCnTh255R49eoTSpUvneP0JCQnw9PSEg4MDnJ2d4evrm+GyISEh6N69O6ysrNC2bVtcvXo1x+UREREREVHhw0RTxTg5OWHLli2IjY1FdHQ0du3aBV1dXdSvX19cJioqClu2bEnzlDM7Fi5ciAcPHmDz5s2YMWMGVq5ciWPHjqVZLjo6Gn/88QeMjY1x6NAhNGvWDCNGjMDHjx9/avuIiIiIiOjXx0RTxQwfPhxv375FvXr10KBBA7x8+RIeHh7Q1NQEAKxcuRLt27fH58+fMXjw4BytOzY2Fnv27MGUKVNQs2ZNNGvWDAMGDMD27dvTLLt//37o6Ohg5syZMDQ0xKhRo2BoaIgHDx4oZDuJiIiIiOjXxVFnVUyFChUQEBCAXbt24ePHj2jUqBEaNGggzvf390e5cuWwcuVKucGBsiM4OBjJycmwtbUVp9nb22Pt2rVITU0VByACgOvXr6NJkyaQSqXiNL5OhYiIiIiIsoOJpgoqVaoUhg8fnu68U6dOySWEOREREQF9fX1oaGjIlZWQkICoqCi5QYhCQ0NhZWWFadOm4cyZM6hYsSImTpwIe3v7HJWZkpKSq1h/xvfJMf08ZdRhQSbbX7nZb8o4dgtr/XJf/9ryu35Zt6rtZ87LvzruE8pLTDQLmNwmmQAQFxcnl2QCEP9OTEyUmx4bG4v169ejd+/e2LBhA44cOYL+/fvj6NGjKF++fLbLvH//fq7jzQ1tbW2Ym5vna5m/upCQEMTFxSk7jAInp8e+so7dwli/3Ne/NmXUL+u2YMjvaxKiwo6JZiGiqamZJqGU/a2lpSU3XSqVokaNGhg1ahQAwNzcHJcuXcKBAwcwZMiQbJdpaWnJJ4wFnKmpqbJDKFBSUlJw//79AnPss37zD/f1r4t1q9oK2nk5P8n2DVFeYKJZiJQtWxaRkZFITk6Guvq3qo+IiICWlhb09PTkli1dujSqVasmN61q1apyr17JDqlUypN6Acf6y52CcuwXhBh/FdzXvy7WbcFQUM7LRL8KjjpbiNSoUQPq6uoICgoSp926dQuWlpZpmuTa2NggJCREbtqzZ89QsWLF/AiViIiIiIgKMCaahYi2tjbc3Nwwc+ZM3Lt3D6dOnYKvry969+4N4NvTzfj4eABAt27dEBISAh8fH7x8+RLLly9HaGgofv/9d2VuAhERERERFQBsOqtibty4kaPla9WqlaPlJ0+ejJkzZ6JPnz7Q1dXFyJEj0bx5cwCAs7Mz5s+fjw4dOqBixYrYuHEjvLy8sH79elSvXh3r169H2bJlc1QeEREREREVPkw0VYy7uzskEkm68wRBAAC5+Y8ePcrR+rW1teHt7Q1vb+80835sKmtvbw9/f/8crZ+IiIiIiIiJporZsmWL+P83b95g2rRp6NixI1q1aoXSpUsjKioKZ86cwc6dOzF79mwlRkpERERERJQ+JpoqxtHRUfy/u7s7+vbti3HjxsktY2dnBy0tLfz9999wdXXN7xCJiIiIiIgyxcGAVNi9e/dQt27ddOfZ2triv//+y+eIiIiIiIiIssZEU4WVK1cOFy5cSHfesWPHUKVKlXyOiIiIiIiIKGtsOqvC+vXrh5kzZ+L9+/do3Lgx9PX18eHDBxw7dgznzp3D0qVLlR0iERERERFRGkw0VVi3bt2QnJyMNWvW4MiRI+L08uXLY/HixWjVqpUSoyMiIiIiIkofE00V16tXL/Tq1QvPnj3D58+foa+vj6pVqyo7LCIiIiIiogyxj2YB8PnzZzx//hzBwcHQ09PDs2fPxHdqEhERERERqRo+0VRxa9aswbp16xAfHw+JRAIrKyssW7YMkZGR8PX1hZ6enrJDJCIiIiIiksMnmips27Zt8PHxQb9+/bB7927xKWavXr0QGhqK5cuXKzlCIiIiIiKitJhoqrCtW7di0KBBGD16NGrWrClOb9iwIcaMGYMzZ84oMToiIiIiIqL0MdFUYW/evIGjo2O686pVq4YPHz7kc0RERERERERZY6KpwsqXL487d+6kO+/BgwcoX758PkdERERERESUNQ4GpMI6deoEHx8faGlpoVGjRgCA2NhYHD9+HOvWrUO/fv2UGyAREREREVE6mGiqsIEDByIsLAyLFy/G4sWLAQC9e/cGALRt2xaDBw9WZnhERERERETpYqKpwiQSCWbPno0//vgDV69eRVRUFIoVK4ZatWrBxMRE2eERERERERGli4mmCrtx4wYsLS1RtWpVVK1aVW5eeHg49uzZgxEjRignOCIiIiIiogxwMCAV5u7uju7du+PNmzdp5r179w6rVq1SQlRERERERESZY6Kp4sLDw9GhQwdcvXpV2aEQERERERFlCxNNFbdkyRI4ODhgwIAB+N///qfscIiIiIiIiLLERFPFFS1aFCtXrkT//v2xYMECTJgwAYmJiZBIJMoOjYiIiIiIKF1MNAuIsWPHYsmSJThx4gS6d++Oz58/KzskIiIiIiKidDHRLEBat26NLVu2ICIiAqNHj1Z2OEREREREROlioqnCatWqhaJFi8pNs7Kywp49e1CtWjUlRUVERERERJQ5vkdThW3dujXd6WXLloWfnx/ev3+fzxERERERERFljYmmigkICEDDhg2hr6+PgICALJevWLFi3gdFRERERESUA0w0VcykSZOwe/du6OvrY9KkSZkuK5FI4Obmlj+BERERERERZRMTTRVz+vRplC5dWvw/ERERERFRQcNEU8V83xSWzWKJiIiIiKggYqKpYiZPnpztZSUSCebNm5eH0RAREREREeUcE00Vc+3atWwvK5FI8jASIiIiIiKi3GGiqWLOnDmj7BCIiIiIiIh+ipqyA6DciY2NxT///KPsMIiIiIiIiNLgE00V9vr1a8ycORPXr19HYmJiuss8evQon6MiIiIiIiLKHBNNFTZ//nzcvn0bnTt3xu3bt6GtrQ0bGxtcunQJ//33H3x8fJQdIhERERERURpsOqvCbty4gbFjx2Lq1Kno0KEDNDU18eeff2Lfvn2oVasW37NJREREREQqiYmmCvv69StMTU0BANWqVcPDhw8BAFKpFD169MDVq1eVGR4REREREVG6mGiqsDJlyuDDhw8AAENDQ3z+/BkREREAgBIlSuDjx4/KDI+IiIiIiChdTDRVWMOGDbFs2TLcuXMHFStWRLly5eDr64uYmBjs27cPZcuWVXaIREREREREaTDRVGGjRo2Cnp4eli9fDgAYO3YsNm/ejFq1auHQoUPo16+fkiMkIiIiIiJKi6POqjB9fX3s2bMH79+/BwC0a9cOFSpUQFBQEKysrODo6KjkCImIiIiIiNJiolkAlClTRvy/g4MDHBwclBgNERERERFR5phoqrDExERs27YNt2/fxpcvX9LMl0gk2Lx5sxIiIyIiIiIiyhgTTRU2e/Zs7N27F7/99htKlCiRZr4gCPkfFBERERERURaYaKqwkydPYuTIkRg+fLiyQyEiIiIiIso2jjqrwtTU1GBra6vsMIiIiIiIiHKEiaYKc3Nzw969e5GamqqwdSYkJMDT0xMODg5wdnaGr69vlp8JCwuDra0trl27prA4iIiIiIjo18WmsypszJgxcHNzQ4sWLVCzZk1oa2vLzZdIJJg3b16O1rlw4UI8ePAAmzdvxps3bzBx4kRUqFABLVu2zPAzM2fORGxsbK62gYiIiIiICh8mmips8eLFeP78ObS1tXHv3r008yUSSY7WFxsbiz179mDDhg2oWbMmatasicePH2P79u0ZJpoHDx7E169fcxU/EREREREVTkw0VdjBgwfRt29fTJgwAWpqP9/KOTg4GMnJyXL9Pu3t7bF27VqkpqamKSMyMhKLFi2Cr68v2rRp89PlExERERFR4cBEU4WlpKSgcePGCkkyASAiIgL6+vrQ0NAQp5UqVQoJCQmIioqCgYGB3PILFixA+/bt8dtvv+W6zJSUlFx/NrekUmm+l/krU0YdFmSy/ZWb/aaMY7ew1i/39a8tv+uXdavafua8/KvjPqG8xERThTVr1gxHjx5F7dq1FbK+uLg4uSQTgPh3YmKi3PTLly/j1q1bOHz48E+Vef/+/Z/6fE5pa2vD3Nw8X8v81YWEhCAuLk7ZYRQ4OT32lXXsFsb65b7+tSmjflm3BUN+X5MQFXZMNFWYtbU1Fi9ejODgYNja2qJo0aJy8yUSSY7esampqZkmoZT9raWlJU6Lj4/H9OnTMWPGDLnpuWFpacknjAWcqampskMoUFJSUnD//v0Cc+yzfvMP9/Wvi3Wr2graeTk/yfYNUV5goqnCZs6cCQAICgpCUFBQmvk5TTTLli2LyMhIJCcnQ139W9VHRERAS0sLenp64nL37t1DaGgoRo0aJff5gQMHws3NDbNnz852mVKplCf1Ao71lzsF5dgvCDEWeOqaEAQhX/e1IAg5HjCOco/fo4KhoJyXiX4VTDRV2MOHDxXWPxMAatSoAXV1dQQFBcHBwQEAcOvWLVhaWsqVY2VlhRMnTsh9tnnz5pg7dy6cnJwUFg8RkcwvnRipa0AikeBZ2GfEJybneXFaGuqoVql4npdDRESUGSaaKqxdu3YYN24cGjdurJD1aWtrw83NDTNnzsS8efPw/v17+Pr6Yv78+QC+Pd0sVqwYtLS0YGhomObzZcuWRcmSJRUSCxHR9yQSCe58fI7opPg8L6uMlh7MSlTM83J+FJ+YjNj4vE80iYiIVAETTRX29u1baGtrK3SdkydPxsyZM9GnTx/o6upi5MiRaN68OQDA2dkZ8+fPR4cOHRRaJhFRdkQnxeNLUt4PqKKr/nN9z4mIiChrTDRVWNu2bfG///0P1apVQ5kyZRSyTm1tbXh7e8Pb2zvNvJCQkAw/l9k8IiIiIiKi7zHRVGEvXrzAzZs30bBhQ5QoUQI6Ojpy8yUSCU6dOqWk6IiIiIiIiNLHRFOFlS9fHm3btlV2GERERERERDnCRFOFyQbpISIiIiIiKkiYaBYA//zzD65fv44vX75AX18fDg4OqF+/vrLDIiIiIiIiShcTTRWWmJiIYcOG4eLFi5BKpdDX10dkZCTWr1+POnXqYN26ddDQ0FB2mERERERERHLUlB0AZczHxwe3bt3CwoULce/ePVy8eBF3797F/PnzERQUhDVr1ig7RCIiIiIiojSYaKqww4cPY8SIEWjXrh2kUikAQF1dHW5ubhgxYgQOHTqk5AiJiIiIiIjSYqKpwj59+gRzc/N055mbmyM8PDyfIyIiIiIiIsoaE00VVqVKFdy6dSvdeTdu3ED58uXzOSIiIiIiIqKscTAgFdatWzcsWLAAWlpaaN26NUqVKoUPHz7g8OHD2LBhA0aMGKHsEImIiIiIiNJgoqnCunfvjocPH2Lx4sVYsmSJOF0QBLRv3x6DBg1SYnRERERERETpY6KpwtTU1ODl5YV+/fqJ79EsXrw4HB0dUb16dWWHR0RERERElC4mmgWAsbExjI2NlR0GERERERFRtjDRVDGTJ0/O9rISiQTz5s3Lw2iIiIiIiIhyjommirl27VqWy0RGRiIuLo6JJhERERERqSQmmirmzJkzGc5LTk7G6tWrsX79epQqVQozZ87Mv8CIiIiIiIiyiYlmAfHo0SNMnjwZISEhaN26NaZNm4bixYsrOywiIiIiIqI0mGiquOTkZKxatQobNmxAiRIlsHLlSjRp0kTZYREREREREWWIiaYKe/jwofgUs127dpg6dSr09PSUHRYREREREVGmmGiqoOTkZKxcuRIbN26Evr4+1qxZg8aNGys7LCIiIiIiomxhoqli/v33X0yaNAlPnjyBm5sbPD09UaxYMWWHRURERERElG1MNFVMly5dkJqaimLFiuH169cYPnx4hstKJBJs3rw5H6MjIiIiIiLKGhNNFWNnZyf+XxCETJfNaj7Rr0YQBEgkkl+2PCIiIqJfBRNNFbN161Zlh0CksiQSCe58fI7opPg8L6tYES3YljTK83KIqABT1+QNMCKiDDDRJKICJTopHl+S4pQdBhERoK4BiUSCZ2GfEZ+YnOfFaWmoo1olvkObiAoGJppEREREPyE+MRmx8XmfaBIRFSRqyg6AiIiIiIiIfi1MNImIiIiIiEihmGgSERERERGRQjHRJCIiIiIiIoVioklEREREREQKxUSTiIiIiIiIFIqJJhERERERESkUE00iIiIiIiJSKCaaREREREREpFBMNImIiIiIiEihmGgSESmYtra2skMgIiIiUip1ZQdARKSKNNXUIQgCJBJJjj4nlUphbm6eR1ERFUy5+S4REVHBxkSTiCgdRdTUIZFIkPr8OhAfnfcF6pWFWkWLvC+HSAkkEgnufHyO6KT4PC+rjJYezEpUzPNyiIgoc0w0iYgyEx8NxEXlfTlaxfK+DCIlik6Kx5ekuDwvR1ddK8/LICKirLGPJhERERERESkUE00iIiIiIiJSKCaaREREREREpFBMNImIiIiIiEihmGgSERERERGRQjHRLGQSEhLg6ekJBwcHODs7w9fXN8Nlz507h99//x22trZo27YtTp8+nY+REhERERFRQcVEs5BZuHAhHjx4gM2bN2PGjBlYuXIljh07lma54OBgjBgxAh07dkRAQAC6deuG0aNHIzg4WAlRExERERFRQcL3aBYisbGx2LNnDzZs2ICaNWuiZs2aePz4MbZv346WLVvKLXv48GHUqVMHvXv3BgAYGhrizJkzOHr0KMzMzJQRPhERERERFRBMNAuR4OBgJCcnw9bWVpxmb2+PtWvXIjU1FWpq//eAu3379khKSkqzjujo6HyJlYiIiIiICi42nS1EIiIioK+vDw0NDXFaqVKlkJCQgKioKLllq1evLvfk8vHjx7hy5Qrq1q2bX+ESEREREVEBxSeahUhcXJxckglA/DsxMTHDz3369AkjR46EnZ0dmjRpkqMyU1JSch7oT5JKpfle5q9MGXWYEdat4qlK/bJuFU9V6hZg/SqaKtVtQSDbX9xvaXGfUF5iolmIaGpqpkkoZX9raWml+5kPHz6gX79+EAQBK1askGtemx3379/PXbC5pK2tDXNz83wt81cXEhKCuLg4ZYfBus0jqlC/rNu8oQp1C7B+84Kq1G1Bk9/XJESFHRPNQqRs2bKIjIxEcnIy1NW/VX1ERAS0tLSgp6eXZvnw8HBxMKAtW7bAwMAgx2VaWlryTnYBZ2pqquwQKA+xfn9drNtfF+s2Z1JSUnD//n1ek6RDtm+I8gITzUKkRo0aUFdXR1BQEBwcHAAAt27dgqWlZZonlbGxsRgwYADU1NSwZcsWlC5dOldlSqVSntQLONbfr431++ti3f66WLe5w2sSovzFwYAKEW1tbbi5uWHmzJm4d+8eTp06BV9fX/GpZUREBOLj4wEA69atw6tXr+Dt7S3Oi4iI4KizRERERESUJT7RLGQmT56MmTNnok+fPtDV1cXIkSPRvHlzAICzszPmz5+PDh064Pjx44iPj0fnzp3lPt++fXssWLBAGaETEREREVEBwUSzkNHW1oa3t7f4pPJ7ISEh4v+PHTuWn2EREREREdEvhE1niYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTTSIiIiIiIlIoJppERERERESkUEw0iYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTTSIiIiIiIlIoJppERERERESkUEw0iYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTTSIiIiIiIlIoJppERERERESkUEw0iYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTTSIiIiIiIlIoJppERERERESkUEw0iYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTTSIiIiIiIlIoJppERERERESkUEw0iYiIiIiISKGYaBIREREREZFCMdEkIiIiIiIihWKiSURERERERArFRJOIiIiIiIgUiokmERERERERKRQTzUImISEBnp6ecHBwgLOzM3x9fTNc9uHDh+jcuTOsra3RsWNHPHjwIB8jJSIiIiKigoqJZiGzcOFCPHjwAJs3b8aMGTOwcuVKHDt2LM1ysbGxGDRoEBwcHODv7w9bW1sMHjwYsbGxSoiaiIiIiIgKEiaahUhsbCz27NmDKVOmoGbNmmjWrBkGDBiA7du3p1k2MDAQmpqamDBhAqpXr44pU6agaNGi6SalRERERERE32OiWYgEBwcjOTkZtra24jR7e3vcvXsXqampcsvevXsX9vb2kEgkAACJRAI7OzsEBQXlZ8hERERERFQAMdEsRCIiIqCvrw8NDQ1xWqlSpZCQkICoqKg0y5YpU0ZuWsmSJfHu3bv8CJWIiIiIiAowdWUHQPknLi5OLskEIP6dmJiYrWV/XC4jgiCI65VKpbkNOVekUil0pZpAqpDnZWmrqSMlJQWpmsWAvC8OUNeGkJICTXU1CJp5e59IU10NKSkpSElJydNycoJ1qziqVr+sW8VRtboFfuH6Zd0WCKmpqdDS0kJSUhL33Q9k+0N23UakSEw0CxFNTc00iaLsby0trWwt++NyGZE1xX348GFuw/1pRfOhjFjEIQjvAUgBlMj7AuMSgfdBeV8OgHgAQRH5UlSOsW5/nqrWL+v256lq3QK/YP2ybguUf//9V9khqKwfu1ARKQITzUKkbNmyiIyMRHJyMtTVv1V9REQEtLS0oKenl2bZDx8+yE378OFDmua0GVFXV4elpSXU1NTEfp5EREREpDoEQUBqaqp4XUikSDyqCpEaNWpAXV0dQUFBcHBwAADcunVLTAi/Z21tjQ0bNkAQBEgkEgiCgNu3b2PIkCHZKktNTS1N01siIiIiIiocOBhQIaKtrQ03NzfMnDkT9+7dw6lTp+Dr64vevXsD+PZ0Mz4+HgDQsmVLfPnyBV5eXnjy5Am8vLwQFxeHVq1aKXMTiIiIiIioAJAI7P1bqMTFxWHmzJk4ceIEdHV10b9/f/Tt2xcAYGpqivnz56NDhw4AgHv37mHGjBl4+vQpTE1NMWvWLJibmysxeiIiIiIiKgiYaBIREREREZFCseksERERERERKRQTTSIiIiIiIlIoJppERERERESkUEw0ifKYIAjYvn27+PekSZMwadIkJUZEyuDj4wN3d3dlh1Eg5WTfJSYmYvfu3XkcUe64uLjA399f6esoaExNTXHt2rV05127dg2mpqZ5Uq67uzt8fHzyZN2qXHZhlNkxlhNXrlzB06dPAQD+/v5wcXH56XUSFWRMNIny2I0bNzB79mxlh0FUKBw5cgRr165Vdhh5Zu/evXB1dVV2GCrD1tYWFy9eVHYYCufj44M//vhD2WEUGhcvXoStre1Pr6dv37748OEDAMDV1RV79+796XUSFWTqyg6A6FfHgZ2J8s+v/n0zMDBQdggqRUNDA6VLl1Z2GApXokQJZYdQqOTFMaSlpQUtLS2Fr5eoIOETTaJsCgsLg6mpKQ4dOoT69evDwcEBc+fORUxMDOzs7HDixAlx2aSkJNSuXRt79uxB7969Acg3zYmJicHYsWNhbW2NRo0a4dChQ+JnExISsGjRIjRs2BA2NjYYMmQI3r59KxfDiRMn0LRpU1haWmLw4MGIiorKvx1B2LJlCxo3bgxLS0t06NABN2/eBADcvHkTbm5usLa2xrBhwzB//nw2k86FJ0+eoHv37rC2tkbv3r0RGRkpN3/Pnj1o2bIlLCwsULt2bcyaNQspKSm4du0aJk+ejNevX8PU1BRhYWGIiYnB5MmTUbduXVhYWKBly5Y4depUhmXfunVLLNvGxgYDBw7E+/fvAXxrCufu7o4VK1agdu3acHBwwPz588XkNjExEfPnz0f9+vVRs2ZNuLi4YNeuXemWYW5ujk+fPonTHjx4AGtra8TExCA4OBjdunWDtbU16tevj5UrV4rLfd90NrPl8prsXHTu3Dm4uLjA1tYWc+fOxX///YcOHTrAxsYGgwcPRkxMDICs901sbCymT5+O2rVro3bt2pg2bRoSEhLE+Tdv3kTbtm1haWmJXr164fXr1wDkm85m5/x48+ZNdOjQAVZWVmjbti2OHz+e7W3euXOnuK3u7u4ICQkR54WHh2PUqFGoVasWLCws0L59e9y6dUsurlWrVqFWrVqYPXs2fHx8MG7cOMyYMQN2dnaoW7cuNmzYIK7v+6azkyZNwvz58zFmzBhYW1ujYcOGCAgIEJeNj4/HlClTYG9vj/r162PPnj0wNzdHWFhYtrctp16+fIn+/fvD1tYWjRo1wpYtWwAAT58+Rf/+/WFnZycek6mpqQC+PaWdMGEC5syZA1tbW7i4uODixYvYtm0b6tWrhzp16ojrAb79Zh49ehStWrWCtbU1PDw8EBoait69e8Pa2ho9evRAeHi4uO4fm9d//11xd3fHmjVr0L9/f1hZWaFFixa4cOGCXFmy3+fMjsUnT56I221paYkePXqITWVlzWR79+4NHx+fNE1ns9o3mR0PRAUVE02iHFq5ciX++usvrFy5EidOnMCGDRvQtGlTuQuWy5cvQ11dHR06dBAvFr5vmnPy5EnUrFkThw8fRqtWreDp6Yno6GgAwIz/1969R2VV5X0A/6JcRHFEU6MxRZb1iIo2GKtRYEYEClRQLoI+5g0rcJJIuoAhFxNwVBwhMgMUREwhUdQkRXkQFEdNE0hQMeXOMCWNeIkc5bLfP1yc4ZGLj70PIsvvZy3W4pyzzzn77LPP5Xf2PucJCUFmZibWrVuHlJQUNDY24t1335UuSAAQExODjRs34quvvkJhYSG2bdv2BEvg2Xbp0iWsX78eISEhOHz4MMzMzLB8+XL88ssv8PT0hLm5OdLS0vDSSy8pvZtLqrl//z48PT0xfPhwpKWlwc7OTikgOXv2LMLCwvDBBx8gIyMDn376Kfbs2YOsrCyYmpoiICAABgYGOHnyJF544QWEh4ejrKwMCQkJSE9Ph5mZGVauXIn79++3WfedO3fg5eUFCwsLpKenIz4+HpWVlYiLi5PS5Ofno6ysDMnJyQgKCkJSUhJOnToFAIiLi0NOTg4+//xzZGRkwMnJCaGhoVJXuhYTJ07E888/j8zMTGnc4cOHMWXKFOjp6cHPzw9jxoxBeno6wsPDsXXrVhw/frxNflVN15Xi4uKwefNmhIaGYseOHfD29saHH36I+Ph4FBQUSF0HH1U2gYGBOH/+PDZv3oyEhAScP38eUVFR0npSU1MRGBiIPXv24NatW9iwYUOHeero/FhbWwsvLy+4uLjg4MGDePvtt7FixQrpQVFnjh07hk2bNiEoKAj79u3Dq6++ioULF+LWrVsAgI8++ghNTU1ISUnB/v378fzzz2PVqlVKy8jLy8PevXulh49HjhyBjo4O9u3bh7feegsbNmxAWVlZu+vfuXOndM144403EBISIl0zwsLCkJ+fj/j4eERGRmLr1q1oamp65Db9Xvfu3cOSJUvQr18/7N69G8HBwYiMjMSBAwcwb948DB06FKmpqQgJCcFXX32lFDweOnQI/fv3x4EDBzBhwgQsX74cJ0+exI4dO7BgwQKsW7dO6QFMdHQ01q5di9jYWBw9ehRyuRxyuRwpKSmora19rGAsJiYGM2bMQHp6OoyNjREUFKR0XW3RUV1sbm7G0qVLMWzYMBw4cAApKSloampCREQEAEh1vb1uzzdu3Hhk2TxOfSDqMQQRqaSqqkrIZDKRmZkpjduzZ4+YNGmSyMnJEaampuK///2vEEKIFStWiNWrVwshhDhz5oyQyWTSPP7+/mLOnDnS8O3bt4VMJhMFBQXi5s2bwtjYWOTm5krT6+rqxCuvvCJOnDgh5SE7O1uavmbNGuHh4dFVm00POXr0qDAxMRFXrlwRQghRX18vTp06JbZv3y5sbW1Fc3OzlNbFxUX4+/sLIYSIjo4W8+fP75Y89yTZ2dnC1NRU1NfXS+N8fHyksissLBQHDx5Umsfd3V1s2rRJCCHE3r17xdSpU6Vpe/fulfaVEEKUlJQImUwmampq2qz7+vXrIj4+XmkfbtiwQSxcuFBalrGxsbhz54403cnJSXz55ZdCCCEyMzPFuXPnpGn37t0TMplMGjd16lSxd+9eIYQQ69evF4sXL5bSWltbi4yMDCGEEBMnThRRUVGiqalJCCFEXl6euH79eptldJauq7Wci1qfqyZPniyioqKk4ffff18EBQUJITovm5s3b4oxY8aIM2fOSNPPnTsnkpKShBBCyGQykZKSIk1LTEwU9vb2Qgjl8+ujzo+RkZHC29tbaTv+/ve/txnXYv78+SI6OloIIYRcLpfy08LZ2VkkJSWJ5uZmkZiYKP79739L006cOCGMjY2V8nX8+HFpenR0tLCwsBCNjY3SuNdee0188803bdbt7+8vXFxcpHR37twRMplMnD9/Xvz6669i3Lhx4tSpU0rrlslkoqqqqt3t+v9SKBTiT3/6k9JxsGfPHpGYmCimTJkiGhoapPG7du0SFhYW0jZbWlpKx1dOTo6QyWSisrJSCCHE3bt3hUwmE3l5eUKItvt99uzZ4uOPP5aG169fL5YsWSIt++Hza+tjZf78+eK9996Tpl2+fFnIZDLx008/Ses6c+ZMp3Wxvr5ebNmyRenclJycLGxsbKThluUIoXwu2r59+yPLprP6QNRT8R1Nosc0ceJE6X8TExPcuHED48ePh7a2NnJzczFlyhQoFIpOP0gyfPhw6f/+/fsDePCUuLy8HM3NzXjllVek6fr6+jAyMkJJSQmMjIwAAIaGhtJ0PT09NDQ0qG37qHOWlpaQyWRwdHTE2LFjYWNjAzc3Nxw5cgTGxsbQ0NCQ0pqamkpdB0k1165dw8iRI9G3b19p3Pjx46WWOhMTE/Tp0wfR0dG4du0arly5goqKClhaWra7PCcnJygUCuzevRulpaW4ePEiALTb4jNkyBA4OTkhMTERly9flpbf+ph/7rnnoKenJw3r6emhsbERAGBra4t//vOfWLt2LUpLS3Hp0qUO1+Xg4IDExETU1dWhqqoKdXV1sLKyAgB4eXlh48aN+Prrr2FlZYVZs2a1+w6Zqum6UutzWZ8+fTBs2DCl4ZaW487KpqKiAk1NTRg3bpw0r5mZGczMzKThESNGSP/3799fqVvtwzo6P5aWliI7O1vpoy8NDQ3SebUzJSUliIiIwMaNG6VxLedsDQ0NyOVyHDp0CHl5eSgrK0NRUVGb1rLWZQMAL774Inr37i0N9+vXT6pLDxs5cqTSNgFAY2MjSktL0dDQgPHjx0vT1fFRm86UlZXByMhI6ThwdXVFSEgIxo0bB03N/91ampqaora2Frdv3wbwYJtbzpEt7y+2lEvLcOveBqrWL1V0VIatPaouyuVy7N+/H0VFRVI9Hjx48CPXXVJSolLZqFofiHoKBppEj0lLS0v6v+VGQkNDA3Z2djhy5Ai0tLSgp6endHP6sNYXkxZCCOjo6LSbvqmpSemmpXUe6MnS1dVFamoqzp49i+zsbKSlpSE5ORl//etf23yIhvvp9+msHHNzc7Fs2TI4OTnhL3/5C5YtW4ZPP/20w2X5+fkhPz8fs2bNglwux5AhQzBnzpx20/78889wdXXFuHHjYG5uDnd3d+Tk5OCHH36Q0mhra3eY38jISKSmpsLFxQVOTk4ICQnp8OcNxowZgxEjRkChUKC8vBw2NjbS8e/p6Ylp06ZBoVDg2LFjWLRoEUJDQ+Hm5qa0DFXTdaWHz2W9erX/Rk5nZaPKcdLRctvT0fIaGxvh6OiIpUuXKo1vffPfkaamJgQEBGDy5MlK4/X09NDc3IwlS5bg9u3bmD59OqytrdHQ0ABvb2+ltA+f39vL58N1/1Fp28t7R8tQl47Kq73rV8t1q+VhS3vzdrZvVa1frR/wtXg4SFOlvDuri/X19Zg9ezYGDhwIa2trODg4oLS0FAkJCR3O00KVsnmc+kDUU/AdTaLHdPnyZen/oqIiDB06FAMHDoSjoyNOnDiBY8eOwd7eXrrwtXcB7Mjw4cOhqamJgoICaVxdXR0qKipUeupOXS8/Px+xsbGYNGkSPvnkE2RkZODevXswMjLCpUuXlFqvWtcVUs3LL7+M8vJy6f0zQLkcU1NT4erqitWrV8PNzQ2jRo1CZWWldEPW+nj79ddfkZ6ejsjISPj4+OD111+X3qlr7wYuMzMTAwYMQGxsLBYtWgQzMzNUVVWpfLOXkpKCoKAgfPTRR5g+fTru3r3b4bqAB62a2dnZOH78OGbMmAHgQStZWFgYtLW14eHhgR07dsDd3b3NR2tUTfe06Kxshg8fjt69e6O4uFhKr1Ao4OzsrNY8GBkZoaKiAoaGhtJfVlaW0sfYOpv3p59+Upo3JiYGBQUFuHbtGs6dO4fExEQsXboUVlZW0gekujpQGDFiBLS0tFBUVCSNa/1/Vxg5ciQqKiqkfQgA69atw65du3Dx4kWlHjb5+fkYNGhQl39FV0tLC/X19dJwfX290ruequqsLp49exbXr19HUlIS3n77bZibm6OmpkalfWxkZNRtZUPUnRhoEj2m8PBwFBYW4tSpU/jss8/w5ptvAgBeffVV6OrqYt++fdJNI/CgBQx4cPHvrLsX8KCrjJubG0JDQ/Hdd9+huLgYH3/8MQwMDGBhYdF1G0Uq69OnD7744gukpqaiuroa3377LX777TfY2NigsbERYWFhKC0txbZt23D69Onuzm6PY25ujhdeeAErV65ESUkJ0tLScOjQIWm6vr4+8vPzceXKFVy9ehUrVqxAbW2t1IVOV1cXt27dQnl5ObS1taGrq4ujR4+iuroaubm50m/attflTl9fHzU1NTh9+jSqqqoQFxeHo0ePqtw9T19fH9nZ2aiqqsL3338PPz+/DtcFPAg0T548idraWun41tHRQV5eHkJDQ1FaWorCwkJ8//33GDt2rNK8qqZ7WnRWNnp6enByckJ4eDguXLiAwsJCREZGYtKkSWrNw7x581BUVITIyEiUl5fj4MGD2LhxI/74xz8+cl4PDw9s374d+/fvR2VlJSIiInD48GGMGjUKf/jDH9CrVy98++23+Ne//oWMjAzpI3CP07Xz9+jXrx9cXFwQHh6OH374AQUFBQgPDwfweA85H4elpSUGDx6M4OBglJSUICsrCykpKYiKisL9+/el8QqFAp9//jnkcnmX5aXF+PHjUVxcjMOHD6OsrAzBwcGP1QreorO6qK+vj99++w0KhQLV1dVITU3Fzp07lfZx3759cfXqVaUHZQDg6OjYbWVD1J0YaBI9punTp8PLywsffPAB3Nzc4OnpCeDBRd3e3h4GBgYwMTGR0o8ePRoWFhaYO3euSl+E9Pf3h7m5OXx8fCCXy6Gjo4PExMR2u+zRkzdmzBjpC5/Tpk1DTEwMIiIiMGrUKMTFxeHixYuYNWsWzpw5A1tb2+7Obo+jpaWF2NhY3Lp1C87OzkhOTpYe5gCAt7c3nnvuOcyZMwceHh7Q0dGBXC6XWj0nTZoEQ0NDODo64sqVK4iIiMCRI0cwY8YMrF27Fn/7298wZMiQdlubp02bhpkzZ8LHxweurq747rvv4O/vj5KSEpUChjVr1uDy5cuYMWMGPvnkE9jb22PChAkdtmwbGhripZdewuuvv67UbS4yMhJ3797F7Nmz8dZbb8HMzAzvvvtum/lVTfc0eFTZBAQEwNjYGB4eHnjnnXfw5z//Gb6+vmrNw7BhwxATE4Pc3Fw4ODggKioKK1aswMyZMx857/Tp0+Hr64vo6Gg4ODjg9OnT+PLLLzFy5EgYGBhg1apV2LJlCxwcHBAXF4fAwEBoampK76J2JX9/f4wePRqLFy/Ge++9BwcHBwBd13VfU1MTmzdvxvXr1+Hs7Izw8HD4+fnB1tYWW7duRWVlpfRV4UWLFrXpQtwVJk+ejMWLFyM4OBhz587Fyy+/rPStg8fRUV00NTWVuurPnDkTaWlpCA4Oxn/+8x/pZ1YWLFiA9evXSw8aWujp6XVb2RB1Jw3BDuBEKqmuroaNjQ2ysrLw4osvtpvmww8/hKGhIXx8fJ5w7uhp1PIbmmvXru3mnNDTqLm5GVOnTsW6devU3npHzw6FQoHJkyejX79+AIALFy5g3rx5yM/P53viRNSt+DEgIjUoKCjAxYsXkZWVhfT09O7ODhE95XJycnDy5En06dMHr732Wndnh3qwTZs2ITs7G56enqivr0dERASsra0ZZBJRt2OgSaQGubm5SEhIgK+vb4etnURELeLj41FWVoaoqKjf9S4ZUYsNGzYgNDQUTk5O0NbWhrW1NQICAro7W0RE7DpLRERERERE6sXHqERERERERKRWDDSJiIiIiIhIrRhoEhERERERkVox0CQiIiIiIiK1YqBJREREREREasVAk4iInlk//vgjfH19YWFhARMTE1haWmL58uUoLi6W0ixYsAALFizocBlpaWkYPXo0qqur26Svrq7G6NGjkZaW1rUbQkRE9JRhoElERM+kq1evYs6cObh58yYCAwORkJAAPz8/1NTUwN3dHQUFBSotx8rKCl9//TWGDh3atRkmIiLqQTS7OwNERETdYdu2bRg4cCC2bNkCTc3/XQ5tbW1hb2+PzZs3Iy4u7pHLGTRoEAYNGtSVWSUiIupx2KJJRETPpF9++QVCCDQ3NyuN79u3LwICAjBt2rR258vNzYWJiQlWrlwJIUSbrrNERETEQJOIiJ5RVlZWqKmpwdy5c7Fz506UlJRACAEAsLe3h7Ozc5t5zp07B29vbzg6OiIsLAwaGhpPOttEREQ9ArvOEhHRM2nevHmora1FfHw8Vq9eDQAYOHAgLC0tsXDhQkyYMEEp/YULF+Dl5QU7OzuEh4czyCQiIuoEWzSJiOiZ9f777yM3Nxf/+Mc/MHv2bOjp6eHgwYNwd3dHUlKSlK6mpgbvvPMOhBAIDg5Gr168fBIREXWGV0oiInqmDRgwAA4ODggPD4dCocC+ffswatQoREREoK6uDsCDnykxMTFBQ0MDNm3a1M05JiIievox0CQiomfOzz//DEtLS6SmpraZNnbsWPj6+uL+/fuoqqoCAMhkMsTGxmLx4sVISkpCYWHhk84yERFRj8JAk4iInjmDBw+GpqYmdu3ahXv37rWZXlpaCh0dHRgaGgIA9PX1oampiWXLlsHAwACBgYFobGx80tkmIiLqMRhoEhHRM6d3795YtWoVfvzxR7i6uiI5ORlnz57F8ePHsWbNGnz22Wfw9vbGgAEDlObT1dVFSEgIiouLER8f3025JyIievrxq7NERPRMsrKywu7duxEfH4+YmBjcuHED2traGDt2LCIjI/HGG2+0O9+UKVNgZ2eHL774AnZ2dk8410RERD2Dhmj50TAiIiIiIiIiNWDXWSIiIiIiIlIrBppERERERESkVgw0iYiIiIiISK0YaBIREREREZFaMdAkIiIiIiIitWKgSURERERERGrFQJOIiIiIiIjUioEmERERERERqRUDTSIiIiIiIlIrBppERERERESkVgw0iYiIiIiISK0YaBIREREREZFa/R9NvjlgZRA6xgAAAABJRU5ErkJggg==",
      "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 a high 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 the top at least 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": 76,
   "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": [
    "22% is considerably 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": 77,
   "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 signficance test aimed to see whether or not over 50% of tech-related cousera courses taught one of the top 5 skills that tech 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 the course."
   ]
  },
  {
   "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\" compare to their representations across a broader spectrum of _technology-related_ courses on Coursera. \n",
    "\n",
    "To achieve this, we will be conducting 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": 78,
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   "outputs": [
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       "      <th></th>\n",
       "      <th>CS</th>\n",
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       "                     CS     DS    IT  MATH\n",
       "python            113.0  106.0  26.0  52.0\n",
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   "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": 79,
   "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. They 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": [
    "- Thoughtful discussion of ethical concerns included\n",
    "- Ethical concerns consider the whole data science process (question asked, data collected, data being used, the bias in data, analysis, post-analysis, etc.)\n",
    "- How your group handled bias/ethical concerns clearly described\n",
    "\n",
    "Acknowledge and address any ethics & privacy related issues of your question(s), proposed dataset(s), and/or analyses. Use the information provided in lecture to guide your group discussion and thinking. If you need further guidance, check out [Deon's Ethics Checklist](http://deon.drivendata.org/#data-science-ethics-checklist). In particular:\n",
    "\n",
    "- Are there any biases/privacy/terms of use issues with the data you propsed?\n",
    "- Are there potential biases in your dataset(s), in terms of who it composes, and how it was collected, that may be problematic in terms of it allowing for equitable analysis? (For example, does your data exclude particular populations, or is it likely to reflect particular human biases in a way that could be a problem?)\n",
    "- How will you set out to detect these specific biases before, during, and after/when communicating your analysis?\n",
    "- Are there any other issues related to your topic area, data, and/or analyses that are potentially problematic in terms of data privacy and equitable impact?\n",
    "- How will you handle issues you identified?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we are not dealing with individuals’ data, only with open source information like LinkedIn posts and Coursera courses, which are readily available online, which eliminates our consern 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 only to 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. When writing and finalizing our analysis we will discuss how biases of the dataset might have skewed our results and conclusions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Expectations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Read over the [COGS108 Team Policies](https://github.com/COGS108/Projects/blob/master/COGS108_TeamPolicies.md) individually. Then, include your group’s expectations of one another for successful completion of your COGS108 project below. Discuss and agree on what all of your expectations are. Discuss how your team will communicate throughout the quarter and consider how you will communicate respectfully should conflicts arise. By including each member’s name above and by adding their name to the submission, you are indicating that you have read the COGS108 Team Policies, accept your team’s expectations below, and have every intention to fulfill them. These expectations are for your team’s use and benefit — they won’t be graded for their details.\n",
    "\n",
    "Our team came up with the following guidelines:\n",
    "\n",
    "- Our group is planning to meet bi-weekly to check on each other’s progress as well as to assign our next steps.\n",
    "- We plan on using Discord as our primary communication.\n",
    "- Messages on discord should be responded to within a day.\n",
    "- If any of us need to miss one of the meetings, we must make sure that we effectively communicate that in the discord.\n",
    "- Communicate if you’re not able to complete your assignment on time and ask for help in the discord.\n",
    "- Make sure to pull from the working branch on GH so that our data/progress is kept clean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Timeline Proposal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify your team's specific project timeline. An example timeline has been provided. Changes the dates, times, names, and details to fit your group's plan.\n",
    "\n",
    "If you think you will need any special resources or training outside what we have covered in COGS 108 to solve your problem, then your proposal should state these clearly. For example, if you have selected a problem that involves implementing multiple neural networks, please state this so we can make sure you know what you’re doing and so we can point you to resources you will need to implement your project. Note that you are not required to use outside methods.\n",
    "\n",
    "\n",
    "\n",
    "| Meeting Date  | Meeting Time| Completed Before Meeting  | Discuss at Meeting |\n",
    "|---|---|---|---|\n",
    "| 2/15  |  N/A | Pick dataset by this date  | Get approvals from everyone on the dataset. | \n",
    "| 2/20  |  1:30 - 2:30 PM | Have data cleaned by this date | Any debugging issues, final resuls | \n",
    "| 2/25  | 1:30 - 2:30 PM  | Dataset defined and described; Data cleaned  | General meeting to discuss the steps of our analysis |\n",
    "| 3/7  | 1:30 - 2:30 PM  | Analysis and results draft by this date | Discuss the results of the analysis   |\n",
    "| 3/12  | 1:30 - 2:30 PM  | Final report draft done | Proof read the final report draft. Discuss the video logistics |\n",
    "| 3/16  | 1:30 - 2:30 PM  | Video logistics done| General project review, video recording |\n",
    "| 3/20  | Before 11:59 PM  | Everything else! | Turn in Final Project & Group Project Surveys |"
   ]
  }
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