I attended the Wolfram Data Science Boot Camp with the hopes to learn enough to create a set of lecture for the Student Initiated Courses (STIC's UMD) and came away with much more. The material taught was extremely comprehensive and illustrated the breadth and depth of the Wolfram language.
The course required a computational essay be presented on the final day on work that had been done over the two week period. I created an analysis of my own personal fitness data. The data was collected from my Apple Watch and exported via "Workout - CSV Exporter". The focus of the project was to see if trends could be clustered, analyzed and predicted.
The analysis focused on using clustering, supervised machine learning, unsupervised machine learning, fitting and statistical analysis.
Some major conclusions include the ability to predict the type of workout based on features such as: duration, average heart rate, maximum heart rate, distance, speed, temperature, and humidity. Visualizations of workouts were able to be performed and clusters of workouts were visible based on the type of workout performed.
The project won Favorite as voted by the sixty-plus attendees as well as 2020 Wolfram Data Science Boot Camp Rookie Award. It has been submitted for Wolfram Data Science Level 2 Certification and is awaiting a response for successful completion