STA 141A course page: Fundamentals of Statistical Data Science
Spring 2026
Instructor: Akira Horiguchi (ahoriguchi@ucdavis.edu)
- If emailing me or the TA, include [STA 141A Spring 2026] in the subject line.
- Lectures: Mondays, Wednesdays and Fridays, 1:10 PM - 2:00 PM, (TLC 1215)
- Office hour: Friday, 12:00 PM - 1:00 PM, Physical and Data Sciences Building 0003 (in the basement; ignore the scary signs), Google Map Campus Map
TA: Xinyi Wang (xyywan@ucdavis.edu)
- Discussion Section A01: Tuesday, 10:00 AM - 10:50 AM, Hart Hall 1130
- Discussion Section A02: Tuesday, 11:00 AM - 11:50 AM, Hart Hall 1130
- Office hour: Tuesday and Thursday, 12:00 - 1:00 PM, MSB 1117
Syllabus: See Canvas
Piazza: See Canvas
Textbooks: Three "main" textbooks and one "supplemental" textbook will be used for the course. They are all freely available online.
- Supplemental:
- [IR] An Introduction to R. W. N. Venables, D. M. Smith, and the R Core Team. 2020.
- Main:
- [R4DS2] R for Data Science, 2nd edition. Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund. 2023.
- [IP] Introduction to probability, statistics, and random processes. H. Pishro-Nik. 2014.
- [ISLR2] An Introduction to Statistical Learning with Applications in R, 2nd ed. G. James, D. Witten, T. Hastie, and R. Tibshirani. 2021.
- In 2023 the authors published a Python version of this book, but our course will use the R version of this book.
- The website also contains R markdown files for the lab portion of each chapter.
Helpful (Optional) Links all through UC Davis
Class Schedule
The exam, homework, and project dates are set, but the lecture topics are subject to change.
| # | Day | Topics | Slides | References | Comments | Wk |
|---|---|---|---|---|---|---|
| L1 | Mar 30 (M) | 1 (Class overview, basic R) | 1.1 1.2 | IR | HW 1 release | 1 |
| Mar 31 (T) | 2 (Arrays, lists) (TA) | 2.1 | IR | |||
| L2 | Apr 1 (W) | 2 (Functions, data frames, loops) | 2.2 | IR | ||
| L3 | Apr 3 (F) | 2 (Functions: apply, conditional exe) | IR | |||
| L4 | Apr 6 (M) | 3 (Data: import, subset, summarize) Discuss project | 3.1 | R4DS2, Ch3,5 | HW 1 due 11pm HW 2 release | 2 |
| Apr 7 (T) | Discussion (Sec 2,3) | |||||
| L5 | Apr 8 (W) | 3 (Data: mutate, reshape) | R4DS2, Ch3-5 | |||
| L6 | Apr 10 (F) | 3 (Data: exploratory data analysis) | 3.2 | R4DS2, Ch9-10 | ||
| L7 | Apr 13 (M) | 3 (Data: transform) | 3.3 | R4DS2, Ch12-13 | HW 2 due 11pm HW 3 release | 3 |
| Apr 14 (T) | Discussion (Sec 3) | |||||
| L8 | Apr 15 (W) | 3 (Data: transform, joins) | 3.4 | R4DS2, Ch16 | Teams created | |
| L9 | Apr 17 (F) | 4 (Overview of statistical learning) | 4 | ISLR2, Ch1-2 | Practice exam release | |
| L10 | Apr 20 (M) | 4 (Overview of statistical learning) | ISLR2, Ch1-2 | HW 3 due 11pm | 4 | |
| Apr 21 (T) | Discussion (Sec 3,4) | |||||
| L11 | Apr 22 (W) | 4 (Overview of statistical learning) | ISLR2, Ch1-2 | |||
| Apr 24 (F) | TA office hour in lecture room. | |||||
| L12 | Apr 27 (M) | Midterm exam 1 | HW 4 release | 5 | ||
| Apr 28 (T) | Discussion cancelled | |||||
| L13 | Apr 29 (W) | 5 (Probability) | 5 | IP, Ch1,3-5 | ||
| L14 | May 1 (F) | 5 (Probability) | IP, Ch1,3-5 | Project proposal due 11pm | ||
| L15 | May 4 (M) | 5 (Probability) | IP, Ch1,3-5 | HW 4 due 11pm HW 5 release | 6 | |
| May 5 (T) | Discussion (Sec 5) | HW 5 files: qmd pdf | ||||
| L16 | May 6 (W) | 6 (Cross-validation) | 6 | ISLR2, Ch5 | ||
| L17 | May 8 (F) | 7 (Linear regression) | 7 | ISLR2, Ch3 | ||
| L18 | May 11 (M) | 8 (Classification) | 8 | ISLR2, Ch4 | HW 5 due 11pm HW 6 release | 7 |
| May 12 (T) | Discussion (Sec 6,7) | |||||
| L19 | May 13 (W) | 8 (Classification) | ISLR2, Ch4 | |||
| L20 | May 15 (F) | 8 (Classification) | ISLR2, Ch4 | |||
| L21 | May 18 (M) | 9 (Clustering) | ISLR2, Ch12 | HW 6 due 11pm HW 7 release | 8 | |
| May 19 (T) | Discussion (Sec 8) | |||||
| L22 | May 20 (W) | 10 (Dimension reduction) | ISLR2, Ch12 | Practice exam release | ||
| L23 | May 22 (F) | 10 (Dimension reduction) | ISLR2, Ch12 | |||
| May 25 (M) | Memorial Day, no class | 9 | ||||
| May 26 (T) | Discussion (Sec 9,10) | HW 7 due 11pm (Tuesday) | ||||
| May 27 (W) | Midterm exam 2 | |||||
| L24 | May 29 (F) | TBD | ||||
| L25 | Jun 1 (M) | TBD | 10 | |||
| Jun 2 (T) | Discussion cancelled | |||||
| L26 | Jun 3 (W) | TBD | ||||
| ……………. | No final exam | Final project due June 9 (T) 11pm |