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Portfolio Projects

This portfolio is a compliation of projects I have created for data analysis

Project 1 - Tokyo Olympics and World Happiness
Project 2 - Udemy Course Data Exploration
Project 3 - New York City Property Data Cleaning


Project 1 - Tokyo Olympics and World Happiness

My goal for this project was to be able to discern any connection between a country's happiness score and the overall medals they won at the 2021 Tokyo Olympics. The Olympics in Tokyo dataset provided the summary of all medals won and final standings for each country that participated in the 2020 Tokyo Olympics. The World Happiness Report dataset provided every countries' ladder score, their region and additional happiness measures.

Datasets used:

Olympics in Tokyo
World Happiness Report

Process

  • Used VLOOKUP and Conditioning Formatting in Excel to match country names between World Happiness Report and listed 2020 Tokyo Olympic Teams/Countries
  • Manually corrected name differences between the two reports (ie. Great Britain and United Kingdom). Teams that were not represented in the World Happiness Report were excluded from the dataset.
  • Results were then filtered by colour to present final matches to better identify any potential missing points of interest.
  • Once data had been cleaned in Excel, imported it over to MySQL and 9conducted several queries which can be found here
  • Datasets were then imported to Tableau Public where several visualizations were made to better understand the relation between a country's happiness score and the medals they won at the 2021 Tokyo Olympics

Analysis

Of the 83 participating countries, excluding dependent territories and countries with missing data in the World Happiness dataset, 71% won at least one gold medal at the 2020 Tokyo Olympics. 46 of the participating countries that won at least one gold medal had a happiness score greater or equal to 5 whereas only 13 countries with a happiness score less than 5 won at least one gold medal at the 2020 Tokyo Olympics. Additionally, there is an average 9.4 medal difference between countries with a happiness score above 5 and below 5. And, with every ladder point increase, there appears to be an approximate 5.2 additional medals won. Observing the data by their regions, Western Europe, North America and ANZ, and East Asia performed the best at the 2020 Tokyo Olympics respectively.

Yet, while countries with a higher ladder score and score factors win more medals, increasing ladder points on the happiness scale does not cause more medals to be won at the olympics. While the correlation between higher medals and a happier country is prevalent, the trendline suggests that there is no relationship between the two variables.

Tableau Visual Dashboard:

Tokyo Olympics and World Happiness

Project 2 - Udemy Course Data Exploration

The goal for the Udemy course dataset was to throughougly explore the data, and determine which courses and subjects received the most interactions, engagements and subscriptions. The Udemy course dataset provided approximately 4000 unique courses from 2011 to 2017 with their corresponding data, such as the course length and number of subscription. The dataset was also categorized by 4 different subjects: Business Finance, Graphic Design, Musical Instruments and Web Development.

Datasets used:

Udemy Course

Process

  • Dataset was imported to MySQL where all duplicate courses were expunged
  • Once all duplicates were deleted, I explored the dataset in MySQL to determine the best courses and subjects provided by Udemy
  • Dataset was then imported to Tableau Public where I created several visualizations, such as the growth of Udemy from 2011-2017 and the performance of each of the four subject matters offered by Udemy

Analysis

The exploration of the Udemy Course dataset provided an insight on four different subjects offered by Udemy, in particular which subjects, and their respective courses, attracted the most users. Of the 3600+ courses provided by the dataset, there were 1198 Web Development courses (32.6%), 1187 Business Finance courses (32.3%), 680 Musical Instrument courses (18.5%), and 601 Graphic Design courses (16.3%). Despite the quantity of courses in Web Development and Business Finance being comparable, Udemy's Web Development courses have collectively generated over $624 million in profit compared to the $123 million generated by courses in Business Finance. Consequently, the most profitable course on Udemy was 'The Web Developer Bootcamp' with over $24 million in profit. Yet, this only provides an insight on Udemy's performance for charged courses.

Around 8.4% of the courses provided by Udemy were free of charge. The most popular free course taken by Udemy's students was 'Learn HTML5 Programming from Scratch' with 268,923 subscribers, almost triple the amount of subscribers in the second top free course. Consequently, the most reviewed courses on Udemy are all Web Development courses ranging from CSS to React in all varying difficulty level. Udemy's success in popularity and profit-generating margins appears to mainly derive from its abundance of easily-accessible courses on Web Development, whether paid or not. While other subject matters, such as Business Finance, offer just as many courses, Udemy's Web Development courses actively outperforms them. Thus, it's recommended that Udemy continues to expand and focus on its Web Development courses.

Additional Notes

  • There is a total of 29 SQL courses provided by Udemy, averaging a cost of $80 for 12 hours of content each.

Tableau Visual Dashboard:

Udemy Course Data Exploration

Project 3 - New York City Property Data Cleaning

The goal for the New York City Property Dataset was to througougly clean in preparation for future data exploration. The New York City Property Dataset was comprised of every building or building unit sold in a twelve month period from September 2016 to September 2017. The dataset was initially altered in an excel sheet by merging the address and neighbourhood for further SQL practice.

Datasets used:

New York City Property Sales

Process

  • Dataset was first imported to Excel where the ID numbers were changed to be distinct
  • Dataset was then imported to MySQL to be cleaned
  • The cleaning process included removing any duplicate data, converting time format, translating IDs into their respective locations, and seperating addresses into address and neighbourhood.

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