This course provides the training for students to be independent in modern data analysis. The course emphasizes the rigorous treatment of data and the programming skills and conceptual understanding required for dealing with modern datasets. The course examines data analysis through the lens of statistics and machine learning methods. Students verify their understanding by working with real datasets. The course also covers supporting topics such as experiment design, ethical data use, best practices for statistical and machine learning methods, reproducible research, writing a quantitative research paper, and presenting research results.
Sample Reading List
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Elements of Stat Learn:Data Mining, Inference, & Prediction
• Jon Krohn with Grant Beyleveld and Aglaé Bassens, Deep Learn Illustrated: A Visual, Interactive Guide to AI
Prerequisites and Restrictions
SML 201 or other equivalent courses. One semester of calculus or discuss with the course instructor. Students are expected to have taken at least one introductory course that explores the fundamental statistical concepts in data science. Familiarity with R or Python programming is assumed.
More information can be found at the course registrar page.