Stat 214: Data Analysis and Machine Learning for Real-World Decision Making

UC Berkeley

Offerings

1. Spring 2025

Overview

This is an MA class in statistics. Students will be engaged in open-ended data projects for decision making to solve domain problems. It mirrors the entire data science life cycle in practice, including problem formulation, data cleaning, exploratory data analysis, statistical and machine learning modeling and computational techniques, and interpretation of results in context. It is guided by the Predictability-Computability-Stability (PCS) framework for veridical data science and emphasizes critical thinking and documenting human judgment calls and code. It coaches not only the technical but also communication and teamwork skills in order to obtain responsible and reliable data-driven conclusions for solving complex real world problems.

Logistics

Three hours of lecture and one hour of discussion per week.

Prerequisites

Prerequisites: Stat 134 and Stat 135 (or Data C100 and Data C140) or equivalents. Computing prerequisites: Stat 243 or equivalent.