• Instructor: Randy Lai ([email protected])

• Meeting time: 9:00 - 10:20 AM, TR

• Location: Wellman Hall 216

• Office hour: MSB 4111 Tuesday and Wednesday 2:00 - 3:00pm (or by appointment)

• TA: Franco Liang ([email protected])

• Meeting time: 5:10 - 6:00 PM, W (Wellman 1) or 6:10 - 7:00 PM, W (Young 184)

• Office hour: 4:00 - 6:00 PM, R (MSB 1117)

• Canvas for grades
• Piazza for discussion
• GitHub for lecture notes

Important: submit your GitHub username to https://forms.gle/E8AiF7i1xTVEFshM7

• There will be around 5/6 assignments
• Assignments must be turned in by the due date. No late assignments are accepted.
• Participation will be based on your involvement in class, discussion, or office hours. The most subjective way to earn participation points is to have some interactions on Piazza. (A+ will be only given to those students with high participation)

The instructor and TA will not respond to any emails about general questions related to assignments and course materials. Please use piazza in regard to this matter. For private or sensitive questions you can do private posts on Piazza or email the instructor or TA.

Learn how to ask a question. Asking a question is an art, stackoverflow.com has some good tips.

See project.md.

• J. Bryan, Data wrangling, exploration, and analysis with R (https://stat545.com/)
• J. Bryan, the STAT 545 TAs, J. Hester, Happy Git and GitHub for the useR (https://happygitwithr.com/)
• G. Grolemund and H. Wickham, R for Data Science (https://r4ds.had.co.nz/)
• H. Wickham, Advanced R (https://adv-r.hadley.nz/)

• R. Peng, S. Kross, and B. Anderson, Mastering Software Development in R (https://bookdown.org/rdpeng/RProgDA/)

(Adapted from Nick Ulle and Clark Fitzgerald )

Point values and weights may differ among assignments. This is to indicate what the most important aspects are, so that you spend your time on those that matter most. Check the homework submission page on Canvas to see what the point values are for each assignment.

The grading criteria are correctness, code quality, and communication. The following describes what an excellent homework solution should look like:

The report does the following:

solves all the questions contained in the prompt makes conclusions that are supported by evidence in the data discusses efficiency and limitations of the computation cites any sources used The attached code runs without modification.

The code is idiomatic and efficient. Different steps of the data processing are logically organized into scripts and small, reusable functions. Variable names are descriptive. The style is consistent and easy to read.

Plots include titles, axis labels, and legends or special annotations where appropriate. Tables include only columns of interest, are clearly explained in the body of the report, and not too large. Numbers are reported in human readable terms, i.e. 31 billion rather than 31415926535. Writing is clear, correct English.

The report points out anomalies or notable aspects of the data discovered over the course of the analysis. It discusses assumptions in the overall approach and examines how credible they are. It mentions ideas for extending or improving the analysis or the computation.