MIDS Fall 2016 Capstone Presentations
On December 19th, the fall 2016 datascience@berkeley graduates presented their capstone projects in a public webinar. For the capstone project, the graduates were tasked to solve a real-world situation or problem utilizing their data science skills in communication, problem-solving, influence, and management to provide a fully realized solution. Some groups tackled modern day occupational hazards of locating information on slack (Awaybot), or the removal of advertising tracking code from websites (Content-Aware Ad Blocking). Other groups focused on tools for healthcare. The Dr. Art: Diabetes Re-Admission Risk Tool allows physicians to use predicting modelling to better care for diabetes patients.
Below we’ve compiled the full list of the capstones, short descriptions of each, and links to learn more about the projects:
- OptiLender uses Machine Learning to recommend portfolios that consistently outperforms the benchmark. With OptiLender, an investor can spend far less time on reading pages and pages of loan opportunities and understanding the various terms, and focus more on constructing a portfolio that one understands and is comfortable with.
- Daniel Dowler, Marek Sedlacek, Su Yan
- WAQ is a toxic substance data consolidation platform which allows virtually any user easy access to their overall exposure levels of pollutants while providing the the ability to drill down into specifics with context, resources, and analysis seamlessly integrated into visualizations.
- Ankit Tharwani, Ashley Levato, James King, Nina Kuklisova, Sean Underwood
Citation for this content: datascience@berkeley, the online Master of Information and Data Science from UC Berkeley