Below are some examples of front end, back end, and full stack data analysis and data visualization programs and web apps that I’ve put together
FIFA World Cup Rankings and Performance Analysis
Using a slider, a World Cup year can be selected and data from that year can be viewed. The map can be overlaid with every country’s FIFA ranking, along with World Cup performance for each team that made the World Cup. The slider will also modify the scatterplot, where the tournament performance of all World Cup teams are plotted vs. entering ranking to view which teams over or under-performed. The data goes up to (but not including) World Cup 2018.
The initial data was cleaned and the database was set up using Pandas running in a Python Jupyter Notebook environment.
World Map of Earthquakes
Full working app: www.aaronburke.net/earthquakes/index.html
Multi-Site Web Scraping Display
Technologies MongoDB, PyMongo, Flask, Python, BeautifulSoup, Splinter, Selenium, Pandas
This is a multi-site web scraping program. Splinter and Selenium are used to drive a browser programmed to scrape data on Mars from the USGS, Space Facts, NASA, and the Mars Weather Report Twitter account. Content is parsed for use using BeautifulSoup, and results are loaded into a Mongo non-relational database. When a user clicks the “scrape” button, the program executes and scrape for the most recent data on each site and updates the display.
This was a fun exercise in practical web scraping.