STA 141A course page: Fundamentals of Statistical Data Science
Fall 2025
Instructor: Akira Horiguchi (ahoriguchi@ucdavis.edu)
Lectures: Mondays, Wednesdays and Fridays, 9:00 AM - 9:50 AM, (TLC 1215)
Labs: Run by TAs, (Wellman 115)
- Section A01: Tuesday, 12:10 PM - 1:00 PM, Kwan Ho Lee (ksjlee@ucdavis.edu)
- Section A02: Tuesday, 1:10 PM - 2:00 PM, Pascal (Mingqian) Zhang (pazhang@ucdavis.edu)
Office hours:
| Day, time | Location | |
|---|---|---|
| Akira Horiguchi | F 10:00-11:00am | Physical and Data Sciences Building 0003 (in the basement; ignore the scary signs), Google Map |
| Kwan Ho Lee | M 2:00-3:00pm | MSB 1117 |
| Mingqian (Pascal) Zhang | T 8:50-9:50am | MSB 1117 |
Syllabus: here
Piazza: See Canvas
Textbooks: Four "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. https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
- Main:
- [R4DS2] R for Data Science, 2nd edition. Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund. 2023. https://r4ds.hadley.nz/
- [IP] Introduction to probability, statistics, and random processes. H. Pishro-Nik. 2014. https://www.probabilitycourse.com
- [MC] Introducing Monte Carlo Methods with R. C. Robert G. Casella. 2010. https://link.springer.com/book/10.1007/978-1-4419-1576-4
- You can access the PDF for free using your university credentials.
- [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.
Helpful (Optional) Links
- https://video.ucdavis.edu/media/Developing+Your+Data+Science+Portfolio+-+2024-02-13/1_mfvunlam
- https://events.library.ucdavis.edu/event/davis-r-users-group-2025fall (Thursdays, 10am-12pm)
- R Basics: https://video.ucdavis.edu/media/R%20Basics%3A%20%20Introduction%20to%20Programming%20for%20Researchers%20(4-part%20series)%20Part%201%20of%204%20-%202021-05-18/1_we50mwf1
- Private Tutoring Resources: https://statistics.ucdavis.edu/undergrad/resources/tutoring
Class Schedule
The exam, project, and homework dates are set, but the lecture topics are subject to change.
| Week | Day of Lecture | Topics | Slides | Additional references | Homework | Discussion |
|---|---|---|---|---|---|---|
| 1 | Sep 24 (W) | 1 (Class overview, basic R) | part 1 part 2 | IR | HW 1 files pdf qmd | |
| Sep 26 (F) | 2 (Vectors, matrices, arrays, lists, data frames) | html | IR | |||
| 2 | Sep 29 (M) | 2 (Functions, loops, apply, conditional execution) | html | IR | Sep 30: Disc 1 | |
| Oct 1 (W) | Discuss project, 3 (Explore data: import) | html | R4DS2, Ch3 | HW 1 due 11:59pm HW 2 files pdf qmd | ||
| Oct 3 (F) | 3 (Explore data: subset, inspect, reshape) | (see above) | R4DS2, Ch3,5 | |||
| 3 | Oct 6 (M) | 3 (Explore data: transforming) | html | R4DS2, Ch12-13 | Oct 7: Disc 2 | |
| Oct 8 (W) | 3 (Explore data: transforming) | (see above) | R4DS2, Ch12-13 | HW 2 due 11:59pm | ||
| Oct 10 (F) | Discuss project proposal, 3 (Explore data: EDA) | html | R4DS2, Ch9-10 | |||
| 4 | Oct 13 (M) | 3 (Explore data: joins), 4 (Probability) | html pdf | R4DS2, Ch16 | HW 3 files pdf qmd turkey | Oct 14: Disc 3 |
| Oct 15 (W) | 4 (Probability) | IP, Ch1,3-5 | ||||
| Oct 17 (F) | 4 (Probability) | IP, Ch1,3-5 | Project proposal due, 11:59pm | |||
| 5 | Oct 20 (M) | 4 (Probability) | IP, Ch1,3-5 | Oct 21: Disc 4 | ||
| Oct 22 (W) | 5 (Overview of statistical learning) | ISLR2, Ch1-2 | HW 3 due 11:59pm | |||
| Oct 24 (F) | 5 (Overview of statistical learning), review | ISLR2, Ch2 | ||||
| 6 | Oct 27 (M) | Midterm exam 1 (9:00 AM - 9:50 AM) | Oct 28: Disc 5 | |||
| Oct 29 (W) | 5 (Overview of statistical learning) | ISLR2, Ch2 | HW 4 files pdf qmd | |||
| Oct 31 (F) | 6 (Cross-validation) | ISLR2, Ch5 | ||||
| 7 | Nov 3 (M) | Discuss project 7 (Linear regression) | ISLR2, Ch3 | Nov 4: Disc 6 | ||
| Nov 5 (W) | 7 (Linear regression) | ISLR2, Ch3 | HW 4 due 11:59pm | |||
| Nov 7 (F) | 8 (Classification) | ISLR2, Ch4 | ||||
| 8 | Nov 10 (M) | 8 (Classification) | ISLR2, Ch4 | Nov 11: no disc (Veterans Day holiday) | ||
| Nov 12 (W) | 8 (Classification) | ISLR2, Ch4 | HW 5 due 11:59pm | |||
| Nov 14 (F) | 8 (Classification) | ISLR2, Ch4 | ||||
| 9 | Nov 17 (M) | 9 (Unsupervised learning) | ISLR2, Ch12 | Nov 18: Disc 7 | ||
| Nov 19 (W) | 9 (Unsupervised learning) | ISLR2, Ch12 | HW 6 due 11:59pm | |||
| Nov 21 (F) | 9 (Unsupervised learning) | ISLR2, Ch12 | ||||
| 10 | Nov 24 (M) | Midterm exam 2 (9:00 AM - 9:50 AM) | Nov 25: Disc 8 | |||
| Nov 26 (W) | TBD | |||||
| Nov 28 (F) | Thanksgiving Holiday, no class | |||||
| 11 | Dec 1 (M) | TBD | ISLR2, Ch8 | Dec 2: Disc 9 | ||
| Dec 3 (W) | TBD | |||||
| Dec 5 (F) | TBD | |||||
| 12 | …………… | No final exam ……………………………………… | Final project due Dec 9, 11:59pm |
