University of Wisconsin-Madison
Statistics 327-1: Introductory Data Analysis with R

Check for updates to this tentative syllabus.


Students will use R to manipulate data and perform exploratory data analysis using introductory statistics. A student completing Statistics 327-1 can do these things:

  1. Use basic R vocabulary.
  2. Manipulate data in R.
  3. Produce graphics and reports.
  4. Apply statistical methods.
  5. Run basic simulations.
Here is a more detailed course map.

NameOfficePhoneEmail (please use piazza for most things)
Gillett, JohnMedical Sciences Center 1590   890-3216
Contador, GonzaloMedical Sciences Center 1130A

Class Times
This online course meets during session ACC, May 28 to June 13, 2019.
Asynchronous online help is available via a piazza Q&A forum.
Live online help is available via web conference with the teacher from 11:30-12:45 on the days listed in the schedule (and repeated in the following table) and with with the TA at the times in this table:
week 1 Tu 5/28
John 11:30-12:45
Gonzalo 11:30-12:30
We 5/29
John 11:30-12:45
Gonzalo 4:00-5:00
Th 5/30
John 11:30-12:45
Gonzalo 1:00-2:00
Fr 5/31

Gonazlo 4:00-5:00
week 2 Mo 6/3
John 11:30-12:45
Gonzalo 4:00-5:00
Tu 6/4
John 11:30-12:45
Gonazlo 1:00-2:00,
We 6/5
John 11:30-12:45
Gonzalo 4:00-5:00
Th 6/6
John 11:30-12:45
Gonzalo 1:00-2:00,
Fr 6/7

Gonazalo 4:00-5:00
week 3 Mo 6/10
John 11:30-12:45
Gonazlo 4:00-5:00
Tu 6/11

Gonzalo 4:00-5:00
We 6/12
John 11:30-12:45
Gonzalo 1:00-2:00,
Th 6/13: Exam

To attend a web conference, use your browser to visit, click on "STAT327", click on "Web conference," click on "STAT327: ... - Course Room," and click "Join Course Room."

An introductory statistics course. (No programming experience is necessary.)

No textbook is required. We'll provide course notes and online screencast lectures (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)

A laptop is required in class.

During summer, 2019, this one-credit course runs in three weeks, which is 1/5 of a regular semester. The weekly workload of this course should be like that of a five-credit, one-semester course: 1 credit = (5 credits/semester)*(1/5 semester).

These points are available:
8 online quizzes (Quiz 1, ..., Quiz 8)≈ 93
4 R or R Markdown scripts (hw1.R, hw2.R, hw3.Rmd, hw4.Rmd)≈ 70
Online exam on reading and writing R code≈ 75
Make an introductory post in piazza, and reply to somebody's post, before the end of the first week≈   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 = 50, B = 30, BC = 20, C = 10, D = 5, F = 0 (That is, performing better than 70% of the class => A. Here is a graph of this percentile curve.) Your grade will be the higher of these two grades.

Grades are recorded at

If you anticipate religious or other conflicts with course requirements, or if you require accommodation due to disability, you must notify me 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.

I 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.)

The registrar says the add deadline for our session (ACC) is 5/30/19 and the drop deadline is 6/7/19.

Here is a tentative schedule.