Data 102: Data, Inference, and Decisions

UC Berkeley, Fall 2026

Schedule

Week 1: Introduction

Thu, Aug 27

Lecture 1 Course Intro, Math Pretest, Decisions 1: Binary Decision Basics

Week 2

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

Week 3: Pandas and API

Tue, Sep 08	Lecture 4 Decisions III: Selecting a Test Statistic Lab 2Lab 2
Thu, Sep 10	Lecture 5 Decisions IV: Estimating p-values

Week 4: Surveys and RCTs

Tue, Sep 15	Lecture 6 Decisions V: Frequentist Multiple Testing Lab 3Lab 3 Homework 2Homework 2
Thu, Sep 17	Lecture 7 Decisions VI: Bayesian Multiple Testing

Week 5: Visualization

Tue, Sep 22	Lecture 8 Decisions VII: Statistical Decision Theory (Loss Minimization and Bayes vs Frequentist)
Wed, Sep 23	Exam Midterm Exam
Thu, Sep 24	Lecture 9 Statistical Modeling I: Parametric Models

Week 6

Tue, Sep 29	Lecture 10 Statistical Modeling II: Parametric Estimation Lab 4Lab 4 Homework 3Homework 3
Thu, Oct 01	Lecture 11 Statistical Modeling III: Analysis and Selection of Estimators

Week 7

Tue, Oct 06	Lecture 12 Statistical Modeling III: Validation Lab 5Lab 5
Thu, Oct 08	Lecture 13 Case Study: Prior Modeling

Week 8

Tue, Oct 13	Lecture 14 Statistical Modeling IV: Sampling and Predictive Checks Lab 6Lab 6 Homework 4Homework 4
Thu, Oct 15	Lecture 15 Wide Models I: Estimation/regression in linear models, regularization

Week 9

Tue, Oct 20	Lecture 16 Wide Models II: Estimation/regression in GLM's Lab 7Lab 7
Thu, Oct 22	Lecture 17 Wide Models III: GLM's as classifiers (Logistic Regression)

Week 10

Tue, Oct 27	Lecture 18 Case Study: Allegheny Algorithm Lab 8Lab 8 Homework 5Homework 5
Thu, Oct 29	Lecture 19 Wide Models IV: Modeling checking and selection in GLM's

Week 11

Tue, Nov 03	Lecture 20 Case Study: Robustness
Wed, Nov 04	Exam Midterm Exam
Thu, Nov 05	Lecture 21 Deep Models I: Hierarchical/Graphical Models

Week 12

Tue, Nov 10	Lecture 22 Deep Models II: Neural Network Classifiers and Backpropagation Lab 9Lab 9 Homework 6Homework 6
Thu, Nov 12	Lecture 23 Deep Models III: Random Forest and Decision Trees

Week 13

Tue, Nov 17	Lecture 24 Deep Models IV: Depth vs Breadth and Bias Variance Lab 10Lab 10
Thu, Nov 19	Lecture 25 Deep Models V: Model Architecture

Week 14

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

Week 15

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

Week 16

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

Week 17

Tue, Dec 15

Finals Week