Data 102 Discussion Notes

Introduction

Welcome to Data 102! One of the most advanced upper-division data science classes at UC Berkeley!

This chapter offers supplementary resources to accompany Wesley’s discussions presented during the Fall 2026 iteration of the UC Berkeley course Data 102: Data, Inference, and Decisions.

Learning Topics

As mentioned in the course catalog:

This course develops the probabilistic foundations of decision-making in data science and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, robustness, Thompson sampling, optimal control, Q-learning, differential privacy, fairness in classification, recommendation systems and an introduction to machine learning tools including decision trees, neural networks, and ensemble methods.

More specifically, you will be learning topics including, but not limited to, the following:

  • Bayes’ Rule and Bayesian Decision-Making
  • Binary Decisions (TPR, FPR, FDP, FOP)
  • False Discovery Rate
  • Permutation Testing
  • Confidence Intervals
  • Causal Inference
  • Bayesian Hierarchical Models
  • Experimental Design

Important Websites

You might want to bookmark the following websites:

General Tips

TBD