Stanford MS&E 226 – Fundamentals of Data ScienceClass description – Autumn 2024This course is about understanding “small data”: these are datasets that allow interaction, visualization, exploration, and analysis on a local machine. The material provides an introduction to applied data analysis, with an emphasis on providing a conceptual framework for thinking about data from both statistical and machine learning perspectives. Topics will be drawn from the following list, depending on time constraints and class interest: approaches to data analysis: statistics (frequentist, Bayesian) and machine learning; binary classification; regression; bootstrapping; causal inference and experimental design; multiple hypothesis testing. Class lectures will be supplemented by data-driven problem sets and a project. Prerequisites: CME 100 or MATH 51; 120, 220 or STATS 116; experience with R at the level of CME/ STATS 195 or equivalent. Homeworks will have a significant practical and computational load to help students apply the concepts discussed in class. Outline
Course infoAll logistical information about the course is available in the syllabus linked from the menu at left. Enrolled students should use Ed Discussion via Canvas for course announcements. Professor |