Main content start

Fundamentals of Data Science: Prediction, Inference, Causality

MS&E
226
Instructors
Thapa, I. (TA)
Liu, Y. (TA)
Zou, C. (TA)
Sojitra, R. (TA)
Wu, H. (TA)
Xu, W. (TA)
Johari, R. (PI)
Section Number
1
This 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.
Grading
Letter or Credit/No Credit
Units
3
Academic Career
Graduate
Course Tags
Computational Policy - Electives
Academic Year
Quarter
Winter
Section Days
Tuesday Thursday
Start Time
10:30 AM
End Time
11:50 AM
Location
Gates B1