Data 102: Data, Inference, and Decisions

UC Berkeley, Fall 2026

Schedule

Week 1: Introduction

Thu, Aug 27

Lecture 1Course Intro, Math Pretest, Decisions I

Week 2: Hypothesis Testing

Tue, Sep 01	Lecture 2Decisions II: Hypothesis Testing Lab 1Lab 1 Homework 1Homework 1
Wed, Sep 02	Discussion 1Discussion 1 (Answers)
Thu, Sep 03	Lecture 3Case Study: Vietnam Draft Lottery and Permutation Tests

Week 3: Hypothesis Testing

Tue, Sep 08	Lecture 4Decisions III: Selecting a Test Statistic Lab 2Lab 2
Wed, Sep 09	Discussion 2Discussion 2 (Answers)
Thu, Sep 10	Lecture 5Decisions IV: Frequentist Multiple Testing

Week 4: Multiple Testing

Tue, Sep 15	Lecture 6Decisions V: Bayesian Multiple Testing Lab 3Lab 3 Homework 2Homework 2
Wed, Sep 16	Discussion 3Discussion 3
Thu, Sep 17	Lecture 7Decisions VI: Statistical Decision Theory

Week 5: Statistical Models

Tue, Sep 22	Lecture 8Statistical Modeling I: Parametric Models
Wed, Sep 23	Discussion 4Discussion 4
Thu, Sep 24	Lecture 9Statistical Modeling II: Parametric Estimation

Week 6: Statistical Models

Mon, Sep 28	Exam Midterm Exam
Tue, Sep 29	Lecture 10Statistical Modeling III: Analysis and Selection of Estimators Lab 4Lab 4 Homework 3Homework 3
Wed, Sep 30
Thu, Oct 01	Lecture 11Statistical Modeling IV: Validation

Week 7: Statistical Models

Tue, Oct 06	Lecture 12Case Study: Prior Modeling Lab 5Lab 5
Wed, Oct 07
Thu, Oct 08	Lecture 13Statistical Modeling V: Sampling and Predictive Checks

Week 8: Statistical Models

Tue, Oct 13	Lecture 14Statistical Modeling VI: Sampling and Predictive Checks Lab 6Lab 6 Homework 4Homework 4
Wed, Oct 14
Thu, Oct 15	Lecture 15Wide Models I: Estimation/regression in GLM's

Week 9: Generalized Linear Models

Tue, Oct 20	Lecture 16Wide Models II: GLM's as classifiers (Logistic Regression) Lab 7Lab 7
Wed, Oct 21
Thu, Oct 22	Lecture 17Case Study: Allegheny Algorithm

Week 10: Generalized Linear Models

Tue, Oct 27	Lecture 18Wide Models III: Modeling checking and selection in GLM's Lab 8Lab 8 Homework 5Homework 5
Wed, Oct 28
Thu, Oct 29	Lecture 19Case Study: Robustness

Week 11: Hierarchical Models

Mon, Nov 02	Exam Midterm Exam
Tue, Nov 03	Lecture 20Deep Models I: Hierarchical/Graphical Models
Wed, Nov 04
Thu, Nov 05	Lecture 21Deep Models II: Neural Network Classifiers and Backpropagation

Week 12: Deep Learning Models

Tue, Nov 10	Lecture 22Deep Models III: Random Forest and Decision Trees Lab 9Lab 9 Homework 6Homework 6
Wed, Nov 11
Thu, Nov 12	Lecture 23Deep Models IV: Depth vs Breadth and Bias Variance

Week 13: Deep Learning Models

Tue, Nov 17	Lecture 24Deep Models V: Model Architecture Lab 10Lab 10
Wed, Nov 18
Thu, Nov 19	Lecture 25Online Decision Making I: Bandits

Week 14: Online Decision Making

Tue, Nov 24	Lecture 26Online Decision Making II: Bandits Homework 7Homework 7 (Optional)
Wed, Nov 25
Thu, Nov 26	Holiday - No Lecture

Week 15: Online Decision Making

Tue, Dec 01	Lecture 27Online Decision Making II: MCTS Lab 11Lab 11
Wed, Dec 02
Thu, Dec 03	Lecture 28Online Decision Making III: Bayesian Optimization

Week 16

Tue, Dec 08	RRR Week
Wed, Dec 09
Thu, Dec 10	RRR Week

Week 17

Tue, Dec 15

Finals Week