STA 35C - Statistical Data Science III, Spring 2026

Course Information

Instructor: Dogyoon Song (dgsong [at] ucdavis [dot] edu)

Teaching Assistant: Yanhao Jin (yahjin [at] ucdavis [dot] edu)

Lectures: Mondays, Wednesdays and Fridays, 1:10 PM – 2:00 PM, Wellman Hall 6

Discussions (Labs) are run by TA

Office hours:

Dogyoon Song (Instructor): Wednesdays, 3:30 PM – 4:30 PM, or by appointment (MSB 4220)
Yanhao Jin (TA): Tuesdays, 12:30 PM – 2:30 PM (MSB 1143)

Syllabus: link

Canvas: link

Piazza: link

Textbooks: There is one required textbook, which is available online.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R, 2nd edition (2021) [Springer]. link

A tentative list of topics to be covered include:

Supervised learning
- Intro to supervised learning
- Regression
- Classification
- Basic nonparameterics
Model assessment, selection, and inference
- Cross-validation
- Subset selection and regularization
- Multiple hypothesis testing
Unsupervised learning
- Dimension reduction
- Clustering

The students’ performance in this course will be evaluated based on the following:

Before the course begins, students should complete the “Homework 0” for self-assessment; this will not be collected or graded, and no solutions will be provided.

Lecture Day	Topics	Slides	Additional references	HW
Mon, Mar 30	Introduction	Lecture 1	JWHT, Ch 1	“HW 0”
Wed, Apr 1	Probability review: basics and conditional probability	Lecture 2		Homework 1 posted; due Tue, April 7
Fri, Apr 3	Probability review: Bayes’ theorem and random variables	Lecture 3
Mon, Apr 6	Statistical learning	Lecture 4	JWHT, Ch 2.1
Wed, Apr 8	Simple linear regression	Lecture 5	JWHT, Ch 3.1	Homework 2 posted; due Tue, April 14
Fri, Apr 10	Multiple linear regression	Lecture 6	JWHT, Ch 3.2 & 7.1
Mon, Apr 13	Qualitative predictors & pitfalls in linear regression	Lecture 7	JWHT, Ch 3.3 & 3.5
Wed, Apr 15	Classification & Logistic regression	Lecture 8	JWHT, Ch 4.1-4.3	Homework 3 posted; due Tue, April 21
Fri, Apr 17	Logistic regression (cont’d) & Classification errors	Lecture 9	JWHT, Ch 4.3-4.4
Mon, Apr 20	Generative models & Linear discriminant analysis	Lecture 10	JWHT, Ch 4.4
Wed, Apr 22	More on generative models + Review for Midterm 1	Lecture 11	JWHT, Ch 4.4-4.5
Fri, Apr 24	Midterm1 (in-class)	Solution
Mon, Apr 27	The bias-variance tradeoff	Lecture 12	JWHT, Ch 2.2
Wed, Apr 29	Cross-validation	Lecture 13	JWHT, Ch 5.1	Homework 4 posted; due Tue, May 5
Fri, May 1
Mon, May 4
Wed, May 6
Fri, May 8
Mon, May 11
Wed, May 13
Fri, May 15	Midterm 2 (in-class)
Mon, May 18
Wed, May 20
Fri, May 22
Mon, May 25	Memorial day, no class
Wed, May 27
Fri, May 29
Mon, Jun 1
Wed, Jun 3
Fri, Jun 5	Final exam (1:00 - 3:00 PM)

Sample exams from the previous year (Spring 2025):