General Overview

STAT 447: Data Science Programming Methods is a course in the Department of Statistics at the [University of Illinois][uofi].

Data Science Programming Methods started in the Spring 2019, Fall 2019 and Fall 2020 terms as STAT 430: Topics in Applied Statistics. Since the Fall 2021 and Fall 2022 terms, it has been offered under its own course number as STAT 447. The instructor is Dirk Eddelbuettel who also designed the course, and taught the previous instances (which can still be accessed, see the resources/websites link on the left).

Course lectures slides as well as guest lectures are publically accessible, see the lectures by topic links on the left.


A 2018 report by National Academies of Sciences, Engineering, and Medicine stated:

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data.

This courses introduces key concepts for computational literacy in a data science context:

  • We start with basic shell operations (which are a core building block for all computing systems) and key commands (find, grep, sed, awk, …) and build this up to simple shell scripts.
  • We work extensively with git (and the GitHub site): we instroduce version control to managed source code (and other files such as write ups and documentation) and much more such as social computing, plus a foray into GitHub Actions building on shell script.
  • We cover the Structured Query Language (SQL) as a key data processing tool via sqlite and duckdb
  • And of course we get into the R Programming language to get familiar with the best language and environment for programming with data.
  • Along the way we also cover reproducible computing and markdown as a versatile publishing too
  • Other topics may be covered as needed, one such example is using Docker for deployment.

This course is fast paced. We cover a considerable amount of material.


  • Lectures, generally as (on-line) slides along with short asynchronous videos
  • Self-study, which offers plenty of reading and coding to do
  • Five (on-line) homeworks administered via the PrairieLearn system
  • Five quizzes driven via PrairieTest and held at the CBTF facility in the Engineering college
  • A (optional but recommended) self-directed project demonstrating data science programming
  • On-line office hours with instructor and course assistants

Note that the CBTF tests generally require an on-campus presence. For Chicago-based students an alternate location downtown may be made available and upon request (and demonstrated reasons) remote students may be accomodated. Note, howeverm that the default is for on-line homework and in-person tests.

Brief One-Paragraph Description

Statistics and Data Science are focused on making sense of data – and face an ever-increasing demand for their work. Yet at the same time, data sets increase in size and scope. Proper tooling is essential to meet these challenges, and as applied work in data analysis is in effect applied computational work, we will learn the computational tools and programming methods to meet these data science challenges. Proficiency at the shell, familiarity with git version control, sufficient understanding of SQL, and of course acquiring actual expertise in R programming are the goals of this course to prepare students for the coming computational challenges. We offer an RStudio Server instance along with use of personal computers. Prior programming experience (in R or another language) will certainly be helpful, but is not a formal requirement for taking the course.

Detailed Content and Lectures

Please see the Lectures by Topics link to the left. Content is often refreshed or added as the course progresses but you always have prior years (from most recent year Fall 2022 to prior runs in Fall 2021, Fall 2020 and Fall 2019 as well as Spring 2019 as a complete reference.