MS&E 226: Fundamentals of Data Science: Prediction, Inference, Causality
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.
Terms: Aut
| Units: 3
Instructors:
Johari, R. (PI)
;
Choi, J. (TA)
;
Fan, L. (TA)
;
Garg, N. (TA)
;
Li, H. (TA)
;
Wu, L. (TA)
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