STA 35C course webpage: Statistical Data Science III
Fall 2025
Lectures: Mondays, Wednesdays and Fridays, 12:10 PM - 1:00 PM, (Olson 158)
Labs: Run by TA, Thursdays, 11:00 AM - 11:50 AM, (TLC 2212)
Office hours (TBD):
| Office hour: day and time | Office hour: location | ||
|---|---|---|---|
| Instructor | Akira Horiguchi (ahoriguchi@ucdavis.edu) | W 10:10-11:10am | Physical and Data Sciences Building 0003 (in the basement; ignore the scary signs), Google Map |
| TA | Qiqi Liu (qqiliu@ucdavis.edu) | T 1:00-2:00pm | MSB 1143 |
Syllabus: here
Piazza: access through Canvas
Textbooks: Two textbooks will be used for the course. They are freely available online.
- [IP] Introduction to probability, statistics, and random processes by H. Pishro-Nik. Kappa Research LLC, 2014. https://www.probabilitycourse.com
- Each section has a link to its own lecture video!
- [ISLR2] An Introduction to Statistical Learning with Applications in R, 2nd ed. G. James, D. Witten, T. Hastie, and R. Tibshirani. 2021. https://www.statlearning.com/
- In 2023 the authors published a Python version of this book, but our course will use the R version of this book.
Links
Class Schedule
The exam, homework, and project dates are set, but the lecture topics are subject to change.
| Week | Day of Lecture | Topics | Slides | References | Homework | Discussion |
|---|---|---|---|---|---|---|
| 1 | Sep 24 (W) | 1 (Probability: basic concepts) | IP, 1.2 | HW 1 file pdf | No disc this week | |
| Sep 26 (F) | 2 (Probability) | sec1 sec2 | IP, 1.2-1.3 | |||
| 2 | Sep 29 (M) | 2 (Probability: conditional probability) | sec2 | IP, 1.3-1.4 | ||
| Oct 1 (W) | 2 (Probability: conditional probability) | sec2 | IP, 1.4 | HW 1 due 11:59pm ….HW 2 file pdf | 1 - Oct 2 (R) | |
| Oct 3 (F) | 3 (Random variables: discrete) | sec2 sec3 | IP, 3.1 | |||
| 3 | Oct 6 (M) | 3 (Random variables: discrete) | sec3 | IP, 3.2 | ||
| Oct 8 (W) | 3 (Random variables: discrete) | sec3 | IP, 3.2 | HW 2 due 11:59pm ….HW 3 file pdf | 2 - Oct 9 (R) | |
| Oct 10 (F) | 3 (Random variables: discrete) | sec3 | IP, 3.2 | |||
| 4 | Oct 13 (M) | 4 (Random variables: continuous) | sec4 | IP, 4.1-4.2 | ||
| Oct 15 (W) | 5 (Joint distributions) | sec4 sec5 | IP, 5.1-5.2 | HW 3 due 11:59pm ….HW 4 file pdf | 3 - Oct 16 (R) | |
| Oct 17 (F) | 5 (Joint distributions), 6 (Overview of statistical learning) | sec5 sec6 | ISLR2, Ch1-2 | |||
| 5 | Oct 20 (M) | 6 (Overview of statistical learning) | sec6 | ISLR2, Ch1-2 | ||
| Oct 22 (W) | 6 (Overview of statistical learning) | sec6 | ISLR2, Ch1-2 | HW 4 due 11:59pm | 4 - Oct 23 (R) | |
| Oct 24 (F) | 6 (Overview of statistical learning) / Review | mlr3 | ISLR2, Ch1-2 | |||
| 6 | Oct 27 (M) | Midterm exam 1 (12:10 PM - 1:00 PM) | ||||
| Oct 29 (W) | 7 (Resampling methods) | sec7 | ISLR2, Ch5 | HW 5 files qmd pdf | 5 - Oct 30 (R) | |
| Oct 31 (F) | 8 (Review of linear regression) | sec8 | ISLR2, Ch3 | |||
| 7 | Nov 3 (M) | 9 (Linear model regularization) | sec9 | ISLR2, Ch6 | ||
| Nov 5 (W) | 9 (Linear model regularization) | sec9 | ISLR2, Ch6 | HW 5 due 11:59pm ….HW 6 files qmd pdf | 6 - Nov 6 (R) | |
| Nov 7 (F) | 9 (Linear model regularization) | sec9 | ISLR2, Ch6 | |||
| 8 | Nov 10 (M) | 10 (Classification) | sec10 | ISLR2, Ch4 | ||
| Nov 12 (W) | 10 (Classification) | sec10 | ISLR2, Ch4 | HW 6 due 11:59pm ….HW 7 files qmd pdf | 7 - Nov 13 (R) | |
| Nov 14 (F) | 10 (Classification) | sec10 | ISLR2, Ch4 | |||
| 9 | Nov 17 (M) | class cancelled | ||||
| Nov 19 (W) | class cancelled | HW 7 due 11:59pm ….HW 8 files qmd pdf | 8 - Nov 20 (R) | |||
| Nov 21 (F) | Midterm exam 2 (12:10 PM - 1:00 PM) | |||||
| 10 | Nov 24 (M) | 11 (K-means clustering) | sec11 | ISLR2, Ch12 | ||
| Nov 26 (W) | class cancelled – watch PCA videos | HW 8 due 11:59pm ….HW 9 files qmd pdf | No disc, Thanksgiving | |||
| Nov 28 (F) | Thanksgiving Holiday, no class | |||||
| 11 | Dec 1 (M) | 11 (K-means clustering) / 12 (Hierarchical clustering) | sec11 sec12 | ISLR2, Ch12 | ||
| Dec 3 (W) | 12 (Hierarchical clustering) | sec12 | ISLR2, Ch12 | 9 - Dec 4 (R) | ||
| Dec 5 (F) | Review | HW 9 due 11:59pm | ||||
| 12 | Dec 9 (T) 3:30pm-5:30pm | Final exam | ……………… |