A more detailed description of the course, policies, and available materials can be found on our Canvas page (requires enrollment in the course).
This course provides a comprehensive introduction to computation and programming for Statistics and Data Science majors. Students will learn how to load and manipulate data, explore and visualize data, and design and implement algorithms for statistical analysis. Students will learn to use the R programming language, along with useful packages for data manipulation and visualization. We will also emphasize the skills and techniques required for team based collaboration in research and industry.
- Lead Instructor: Dr. Mark Fredrickson, mfredric@umich.edu
- GSIs:
- Victor Verma, vkverma@umich.edu
- Benjamin Osafo Agyare, bagyare@umich.edu
- Yilei Zhang, yileizh@umich.edu
- Jesse Wheeler, jeswheel@umich.edu
We will use the R programming language and the RStudio development environment. You have two options for using RStudio: online through the university's Great Lakes cluster computing environment or installed locally on your own computer. R and RStudio are also installed publicly available workstations throughout the university. For additional details on installing R and RStudio or using the Great Lakes cluster environment, please see our Canvas page.
We will use git to distribute lecture notes, lab assignments, and homework assignments. Clone this repository to get started.
- Our main text book is R for Data Science by Hadley Wickham and Garett Grolemund
- Similar material can be found in Introduction to Data Science by Rafael A. Irizarry.
- For learning git, we recommend Beginning Git and GitHub by Mariot Tsitoara.
- Homework Assignments (20%)
- Quizzes (15%)
- Three Exams (20%, each)
- Lab Attendance/Completion (5%), two allowed absences
Late homework will not be accepted. Please email the professor (mfredric@umich.edu) if you have circumstances requiring an extension. Please contact the professor as soon as possible to discuss any accommodations for exams.
Additional policies available on our course Canvas page.