STA 35C - Statistical Data Science III, Spring 2026

Course Information

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

Teaching Assistant: TBA

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

• This is an in-person only class, and lectures will not be recorded.

Discussions (Labs) are run by TA

• Section A01: Tuesdays, 8:00 AM - 8:50 AM, TLC 2212
• Section A02: Tuesdays, 9:00 AM - 9:50 AM, TLC 2212

Office hours:

• Dogyoon Song: Wednesdays, 4:30 PM - 5:30 PM, or by appointment (MSB 4220)
• TA: TBA

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

Topics

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

Course Structure and Evaluation

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

• Homework: 35%
• Midterms: 25%
• Final exam: 40%
• Participation: up to 3% extra

Tentative Class Schedule

• 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.