When I first started college, I needed to find a way to organize my time; I needed some sort of planner. I discovered an app called Structured. It has an inbox to list tasks that need to be done, and also a timeline where you can time-block your day. I made use of both of these features to organize my time. After my freshman year, I had learned quite a lot about doing data science in python. Since I had recorded literally all the time I spent on schoolwork into structured, I had a good source of data available to me. Then I used python to make some visualizations with pandas, matplotlib, and seaborn. After that, I used Tableau to present the data as a story.
notebooks: contains scratch work for data exploration and visualizationsrc: contains python scripts used to produce resultsresults: contains visualizations illustrating the data
This analysis is containerized using Docker and automated using Snakemake. To reproduce the results, run the docker image via the runall.sh script. However, I have not published the raw data due to privacy reasons.
The above stack plot shows how much time I spent studying on a weekly basis per class per semester. So far my fall semesters are typically lighter and Spring 2025 is proving to be especially tough.
The above barplot shows how much time I spent studying on average each weekday per semester. My study habits can vary day to day, and I see that I typically don't do much at all on Fridays.
The above heatmap shows at which times during the week I most frequently spend studying.
- Explore new visualizations to expand my knowledge of data visualization.
- Analyze additional variables available to look at, such as assignment due dates and assignment names.
- Draw conclusions to improve my study habits in the future.