I am a hockey player turned concussion researcher turned startup co-founder turned data scientist. I am interested in analyzing data to derive key actional findings for clinical care and business purposes. Transforming data into action and results.
My experiences have spanned data cleaning, exploratory data analysis, machine learning, dashboarding, and deploying production web apps.
- 👍 Disney Recommendation System
- Using data from Disney movies and shows I created a content Recommendation system that will suggest 10 movies/shows based on one you select.
- 🏒 NHL Goals
- Using Python and data from TidyTuesday I preprocessed and analyzed data from top goal scorers in the NHL to understand whether it was easier to score goals in previous years, whether goal production decreases with age, and who is the most efficient top goal scorer.
- 🏀 2023 March Madness Competition
- A Kaggle competion to predict 2023 March Madness results for both Men's and Women's tournaments.
- 🆎 A/B Test Analysis of Campaign
- Analysis of an A/B test to determine whether campaign increased number of purchases. Results are summarized in Tableau dashboard here.
- 🏎️ F1 Dashboard
- This project visually displays F1 data for different teams and racers over the years. This dashboard was build via Shiny and deployed via Shinyapps.io.
- 🧠 Classifying Concussion Rates
- This project compiled concussion rates from 83 research papers to directly compare and rank sports with the greatest concussion rate. Results were published in Sports Med. in 2021.
- 🏇 Horse Racing
- Kaggle competition to predict winning race strategies.
- 🤖 Factors influencing liklihood of ML implemented into production
- Few ML models are successfully implemented into production. This exploratory analysis cleaned, analyzed, and modeled data to find that the following factors increase liklihood: If the person has ever published, More years of experience, but only after 3 years, Years of experience with ML models, More people working on Data science in the company, Higher salary
