STA 35C - Statistical Data Science III, Fall 2026

Course Information

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

Teaching Assistant: TBA

Lectures: Mondays, Wednesdays and Fridays, 9:00 AM–9:50 AM, Olson Hall 205

Discussions (Labs) are run by TA

Office hours:

Syllabus: TBA

Canvas: TBA

Piazza: TBA

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	Notes
Wed, Sep 22
Fri, Sep 24
Mon, Sep 28
Wed, Sep 30
Fri, Oct 2
Mon, Oct 5
Wed, Oct 7
Fri, Oct 9
Mon, Oct 12
Wed, Oct 14
Fri, Oct 16
Mon, Oct 19	Midterm 1 (in class)
Wed, Oct 21
Fri, Oct 23
Mon, Oct 26
Wed, Oct 28
Fri, Oct 30
Mon, Nov 2
Wed, Nov 4
Fri, Nov 6
Mon, Nov 9	Midterm 2 (in class)
Wed, Nov 11	No class (Veterans Day Holiday)
Fri, Nov 13
Mon, Nov 16
Wed, Nov 18
Fri, Nov 20
Mon, Nov 23
Wed, Nov 25	No class (Thanksgiving Holiday)
Fri, Nov 27	No class (Thanksgiving Holiday)
Mon, Nov 30
Wed, Dec 2
Fri, Dec 4

Wed, Dec 9	Final exam (8:00–10:00 AM)