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Stat 131a: Statistical Methods for Data Science

UC Berkeley, Fall 2025

Schedule

Aug 27, 2025 - Dec 12, 2025 | Tu, Th 08:00 am - 09:29 am | Barker 101

Course Description

This course teaches a broad range of statistical methods that are used to solve data problems. Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.

Resources

Lecture Slides

Week 1
Aug 27Lecture 1The First Lecture!
Week 2
Sep 2Lecture 2Central Tendencies: Mean, Median, Mode
Week 2
Sep 4Lecture 3Visualization: The Boxplot
Week 3
Sep 9Lecture 4Probabilistic Distributions
Week 3
Sep 11Lecture 5Probabilistic Distributions
Week 4
Sep 16Lecture 6Probabilistic Distributions: The Normal Distribution; Kernel Density Estimation
Week 4
Sep 18Lecture 7Kernel Density Estimation; The Central Limit Theorem
Week 5
Sep 23Lecture 8Hypothesis Testing I
Week 5
Sep 25Lecture 9Hypothesis Testing II
R code R code for lecture example problems
Week 6
Sep 30Lecture 10Hypothesis Testing III
R code R code for lecture example problems
Week 6
Oct 2Lecture 11Hypothesis Testing IV
R code R code for lecture example problems
Week 7
Oct 7Lecture 12Hypothesis Testing V
R code R code for lecture example problems
Week 7
Oct 9Lecture 13Linear Regression I
Week 8
Oct 14Lecture 14Curve Fitting II
R code R code for lecture example problems
Week 8
Oct 16Lecture 15Curve Fitting III
Week 9
Oct 21Lecture 16Curve Fitting IV
R code R code for lecture example problems
Week 9
Oct 23Lecture 17Midterm Guidance
Week 11
Nov 4Lecture 18Multiple Linear Regression I
Week 12
Nov 6Lecture 19Multiple Linear Regression II: Measuring Fit
Week 13
Nov 13Lecture 20Final Project: Overview
Week 14
Nov 18Lecture 21Logistic Regression I
Week 14
Nov 20Lecture 22Logistic Regression II

Lab/Assignments

All lab assignments are graded for accuracy. See the syllabus for collaboration and late policies.

References
  1. Purdom, E. (2023). Statistical Methods for Data Science. https://epurdom.github.io/Stat131A/book/
  2. Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2e). https://r4ds.hadley.nz/
Stat 131a
Syllabus