Statistical Computing: Fall 2021

Statistics 36-350

Instructor: Ryan Tibshirani (ryantibs at cmu dot edu)

Important note: please direct emails on all course related matters to the Head TA, not the Instructor. The subject line of all emails should begin with "[36-350]".

Head TA: Shamindra Shrotriya (shamindra at cmu dot edu)

TAs: (all emails are at andrew dot cmu dot edu)
[MG] Maxine Graves (mgraves)
[SM] Stefano Martinez (gmolinam)
[KY] Kevin Yang (kevinyan)

Undergrad TAs: (all emails are at andrew dot cmu dot edu)
[LC] Linpeng Chen (linpengc)
[MC] Michael Chen (mlchen)
[JF] Jessica Fan (jlfan)
[SH] Sean Hough (smhough)
[PM] Parth Maheshwari (parthmah)
[AZ] Alan Zhu (yixuanz2)

Class times:
Tuesdays and Thursdays, 10:10-11:30am, ~~Baker Hall A51~~ REMOTE, Zoom link (see Canvas for passcode)

Office hours (OHs):
Tuesday: 2:00-3:00pm [MC]
Wednesday: 3:00-5:00pm [PM/SH]
Thursday: 9:00-10:00am [SS]
Thursday: 2:00-6:30pm [LC/MC/JF/AZ/MG/SM/KY]
Friday: 2:00-6:30pm [LC/MC/JF/SH/PM/AZ/MG/SM/KY]

Note: All OHs are REMOTE, at this Zoom link (see Piazza FAQs for passcode)

Syllabus: here. Announcements: Piazza group.

Class schedule

Week 1 (Tues Aug 31 & Thur Sep 2). Basic data structures.
- Notes: slides, html, Rmd
- Intruder code: intruder_demo.R, intruder.R
- Quiz 1: (see Gradescope)
- Lab 1: pdf, html, Rmd
Week 2 (Tues Sept 7 & Thur Sept 9). Indexing and iteration.
- Notes: slides, html, Rmd
- Quiz 2: (see Gradescope)
- Lab 2: pdf, html, Rmd
Week 3 (Tues Sept 14 & Thur Sept 16). Data frames and apply.
- Notes: slides, html, Rmd
- Quiz 3: (see Gradescope)
- Lab 3: pdf, html, Rmd
Week 4 (Tues Sept 21 & Thur Sept 23). Purrr and a bit of dplyr.
- Notes: slides, html, Rmd
- Quiz 4: (see Gradescope)
- Lab 4: pdf, html, Rmd
Week 5 (Tues Sept 28 & Thur Nov 30). Dplyr, pipes, and more.
- Notes: slides, html, Rmd
- Quiz 5: (see Gradescope)
- Lab 5: pdf, html, Rmd
Week 6 (Tues Oct 5 & Thur Oct 7). Text manipulation.
- Notes: slides, html, Rmd
- Quiz 6: (see Gradescope)
- Lab 6: pdf, html, Rmd
Week 7 (Tues Oct 12). Plotting.
No class Thursday: midsemester break.
- Notes: slides, html, Rmd
- Quiz 7: (see Gradescope)
- Lab 7: pdf, html, Rmd
Week 8 (Tues Oct 19 & Thur Oct 21). Functions.
- Notes: slides, html, Rmd
- Quiz 8: (see Gradescope)
- Lab 8: pdf, html, Rmd
Week 9 (Tues Oct 26 & Thur Oct 28). Simulation.
- Notes: slides, html, Rmd
- Quiz 9: (see Gradescope)
- Lab 9: pdf, html, Rmd
Week 10 (Tues Nov 2 & Thur Nov 4). Fitting models to data.
- Notes: slides, html, Rmd
- Quiz 10: (see Gradescope)
- Lab 10: pdf, html, Rmd
Week 11 (Tues Nov 9 & Thur Nov 11). Debugging and testing.
- Notes: slides, html, Rmd
- Quiz 11: (see Gradescope)
- Lab 11: pdf, html, Rmd
Week 12 (Tues Nov 16 and Thur Nov 18). Relational databases.
- Notes: slides, html, Rmd
- Quiz 12: (see Gradescope)
- Lab 12: pdf, html, Rmd
Week 13 (Tues Nov 23). Version control through Git, and how to make an R package.
No class Thursday: Thanksgiving break. There will also be no Quiz 13 and no Lab 13!
- Notes: This will be entirely impromptu: live building of a new R package with version control through GitHub.
Week 14 (Tues Nov 30 & Thur Dec 2). Statistical prediction.
Final exam will be held during our Thursday class time. There is no Quiz 14, and Lab 14 is due Mon Dec 6.
- Notes: slides, html, Rmd
- Lab 14: pdf, html, Rmd

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Supplementary materials

R for Data Science, by Hadley Wickham (based in the tidyverse)
RStudio Cheat Sheets, reference guides for common packages (mostly for tidyverse)
Data Visualization: A Practical Introduction, by Kieran Healy (gentle intro to ggplot2)
The R Graphics Cookbook, by Winston Chang
Software for Data Analysis: Programming with R, by John Chambers (advanced book)

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