Check www.stat.wisc.edu/~jgillett/3271 for updates to this tentative syllabus.
Goals
Students will use R to manipulate data and perform exploratory data analysis using introductory statistics. A student completing Statistics 3271 can do these things:
Teachers  
Name  Office  Office hour  Email (please use our Q&A forum for most things) 
Johnston, Liam  Medical Sciences Center 1582  Thur 9:3011:30 am  ljohnston2@wisc.edu 
Qing, Li  Medical Sciences Center 1217A  Mon,Thur 10:5011:50 am  qli295@wisc.edu 
TAs  
TA Song, Jie  jsong@stat.wisc.edu  
TA Wang, Jili  jili@stat.wisc.edu  
Class Times
Lecture 327001 (Teacher Li, Qing and TA Wang, Jili)  Tues, Thur, 9:3010:45 am  STERLING 2301 
Lecture 327007 (Teacher Li, Qing and TA Song, Jie)  Tues, Thur, 4:005:15 pm  STERLING 2301 
Lecture 327010 (Teacher Johnston, Liam and TA Song, Jie)  Tues, Thur, 8:009:15 pm  STERLING 2301 
Lecture 327011 (Teacher Johnston, Liam and TA Song, Jie)  Tues, Thur, 1:002:15 pm  Ingraham 122 
Prerequisite
An introductory statistics course. (No programming experience is necessary.)
Textbook
No textbook is required. We'll provide course notes, and we'll read R documentation and write R code. 
Optional Online Reading
R for Data Science by Garrett Grolemund and Hadley Wickham 
An Introduction to R (pdf) by W. N. Venables, D. M. Smith and the R Development Core Team 
Advanced R by Hadley Wickham (advanced) 
Intro to R video lectures by Google Developers 
R Programming wikibook 
Using
R for Data Analysis and Graphics by
J. H. Maindonald 
The R Inferno by Patrick Burns (advanced) 
Optional Reference Books
R for Data Science by Garrett Grolemund and Hadley Wickham 
Data Manipulation with R by Phil Spector 
Advanced R by Hadley Wickham (advanced) 
Introductory Statistics with R by Peter Dalgaard (2008) 
R in a Nutshell by Joseph Adler (2009) 
A Beginner's Guide to R by Alain F. Zuur, Elena N. Ieno, and Erik Meesters (2009) 
Software for Data Analysis: Programming with R by John Chambers (2008) (advanced) 
Computing
A laptop is required in class.
Help
Many questions outside of class should be posted at
our Q&A
forum. Please feel free to write answers when you know
them. We are eager to help in class and office hours too.
Grades
During the fall and spring, this course runs in five weeks. The
weekly workload of this onecredit, fiveweek course should be
like that of a threecredit, onesemester course: 1 credit = (3
credits/semester)*(1/3 semester).
These points are available (we might revise this as we write course materials):
≈ 8 online quizzes (Quiz 1, ..., Quiz 8)  ≈ 93 
≈ 4 R or R Markdown scripts (hw1.R, hw2.R, hw3.Rmd, hw4.Rmd)  ≈ 70 
Exam on reading and writing R code  ≈ 75 
Ask a question or make a comment in class  ≈ 1 
Answer a question on piazza  ≈ 1 
Total  240 
We'll assign grades according to the percentage scale, A = [92,100], AB = [88,92), B = [82,88), BC = [78,82), C = [70,78), D = [60,70), F = [0,60) (92% of points => A); and according to the percentile scale, A = 70, AB = 60, B = 45, BC = 30, C = 10, D = 5, F = 0 (performing better than 70% of the class => A). Your grade will be the higher of these two grades.
Grades are recorded in Canvas for 327010 and D2L for 327001 & 327007.
If you anticipate religious or other conflicts with course requirements, or if you require accomodation due to disability, you must notify your instructor during the first two weeks of class. You may not make up missed quizzes, homework, or exams, except in the rare case of a documented, serious problem beyond your control.
We encourage you to discuss the course, including the online quizzes, with others, but you must write the R scripts and the exam by yourself and prevent others from copying your work. (See the UW Academic Integrity policy.)
Note that the registrar's deadlines for threeweek courses are special: for our session, AEE (9/610/8/2017), the add deadline is 9/8/2017 and the drop deadline is 9/22/2017.
Tentative Schedule
Day #: Date  Subject  Before class  Homework due (11:59 p.m.) 
01: Th 9/7/17  Install R and RStudio 1. R as a Calculator Demo of Quiz 1, online lecture, piazza.com 
Read email Listen Q1 Bring questions 

02: Tu 9/12  2. Vector Discuss HW1 
Most of Q1 Listen Q2 
Quiz 1 (login help) 
03: Th 9/14 
3. Vector (continued) and List Discuss HW2 
Most of Q2 Most of HW1 Listen Q3 
Quiz 2 hw1.R (submit) 
04: Tu 9/19 
4. Data Frame, Factor, Formula (flowers.csv) R Markdown (screencast) 
Most of Q3 Listen Q4 
Quiz 3 
05: Th 9/21 
5. (Base) Graphics Group practice on graphics (to be continued) Discuss HW3 (first listen to RMarkdown, above) 
Most of Q4 Most of HW2 Listen Q5 
Quiz 4 hw2.R (submit) 
06: Tu 9/26 
6. Statistical Tests and Confidence Intervals Group practice on graphics (continued) (submit one graphics.Rmd per group) 
Most of Q5 Listen Q6 
Quiz 5 
07: Th 9/28 
7. Regression Discuss HW4 Group practice on Tests and Intervals (to be continued) 
Most of Q6 Most of HW3 Listen Q7 
Quiz 6 hw3.Rmd (submit) 
08: Tu 10/3  8. Simulation Group practice on Tests and Intervals (continued) (submit one tests.Rmd per group) Discuss exam  Most of Q7 Listen Q8 
Quiz 7 
09: Th 10/5  Exam (rules) 
Most of Q8 Most of HW4 
Quiz 8 hw4.Rmd (submit) 