Data Analyst | Storyteller | NLP Developer
Part of my capstone project was to look at different ways to visualize NBA player metrics, especially when comparing two players. This was one way to do it.
The link to the dashboard can be found here, where you can compare your favorite NBA players.
For my capstone project, I looked at the NBA salary cap, how it affects player recruitment, and finding a way to present player metric data in a easy-to-consume fashion. You can find the link to the presentation and dashboards on Tableau Public.
Most of my work cleaning was done in Python and using the Pandas package to pull the data from the NBA Stats API, as well as scraping player salary, salary cap, and luxury tax data from ESPN and Sportrac using Beautiful Soup. From there, I uploaded the data onto Tableau and created my data visualizations and storyboard. The slides themselves were SVG files created via Google Slides.
(NBA team logos and names are a property of the NBA)
My analysis on the Baywheels bikeshare program before and after the COVID pandemic and stay-at-home order in the Bay Area back in December 2023. You can see the presentation slides on Tableau Public.
A lot of work went into building my own PostgreSQL database from scratch on my own local machine. From there, I pushed the Baywheels data taken from their website into the database via Python and SQLalchemy. I had to modified the table schema because it was modified midway through the pandemic. Once all the data was fixed, I was then able to clean and manipulate the data. From there, I proceeded to connect my database into Tableau Desktop and then proceeded to create my dashboards and presentation.
This was a team sprint project. Split into groups of three, myself, Dan Cewe, and Luca Delgado were given a scenario: tasked by a clean energy firm on whether or not to invest into wind turbine market and to report our findings. Tasked as the scrum lead, I was responsible for making sure we stayed on task, met the deadlines for our deliverables, and resolve any issues that may have arose.
This is the link to our presentation. We used Python and SQL for much of our data cleaning and manipulation and Seaborn, Matplotlib, and Tableau for our data visualizations.