Data science is used to solve business problems. It also can help solve problems that impact humanity, not just the bottom line. That's why I'm interested in volunteering.

In Springboard's Data Science Career Track, a 1:1 mentor-led program, I became proficient in Python, SQL and machine learning.

One of my Springboard capstone projects was a Starbucks purchase prediction model where I segmented customers by engineering recency, frequency and monetary features. The model classified customers by the predicted day of their next purchase and achieved 60 percent accuracy. I built and presented reports that included visualizations created with the Matplotlib and Seaborn Python libraries.

I won the Central New Jersey Data Science Meetup Kaggle competition, where we classified cars based on six features. I've also created a Major League Baseball daily fantasy model both for pitching and hitting (two separate models). A lot of the data for that project was scraped using Python's Beautiful Soup library.

This baseball project in particular has helped sharpen my data wrangling and data cleaning skills in Python. I think that is my biggest strength when it comes to volunteer projects.