CS109b: Advanced Topics in Data Science
Pavlos Protopapas, Mark Glickman, & Chris Tanner
Lab Leaders: Chris Tanner & Eleni Kaxiras
Head TF: Chris Gumb
Advanced Topics in Data Science (CS109b) is the second half of a one-year introduction to data science. Building upon the material in Introduction to Data Science, the course introduces advanced methods for data wrangling, data visualization, statistical modeling, and prediction. Topics include big data, multiple deep learning architectures such as CNNs, RNNs, autoencoders, and generative models as well as basic Bayesian methods, nonlinear statistical models, and unsupervised learning.
Lectures: Mon and Wed 1:30‐2:45pm in NW B103
Labs: Monday 4:30-5:45pm & 6:00-7:15pm in Pierce Hall 301 (identical material at both times)
Advanced Sections: Wed 4:30-5:45pm in Maxwell-Dworkin G115 (starting 3/4)
Office Hours: See weekly calendar for times and locations
Prerequisites: CS 109a, AC 209a, Stat 121a, or CSCI E-109a or the equivalent.
Course Email: email@example.com