A project-based advanced computing course intended for masters and PhD students in Statistics & Data Science, though all students with both strong computing skills and statistical interests are welcome.
Students are welcome to bring their own projects, or to select project ideas from a list I provide; check the project page for more information. Contact me if you’re not sure if your project idea is suitable, and we can work together to develop it.
Along with the projects and student presentations, the course will include introductory lectures to various advanced computing topics, such as parallel programming, databases, and distributed computing.
For full course details, consult the syllabus.
- TR 3–4:30pm, mini 3, Spring 2019
- GHC 4215
- Alex Reinhart
- Office hours
- Wed 12:30-1:30pm and Fri 1-2pm, and by appointment; BH 232K
- Lecture notes
- Posted regularly online
- Computing resources
- Department servers and Amazon Web Services; see details
- Announcements and homework submission
Note: Masters and undergraduate students should enroll in 36-651, PhD students in 36-751. There’s no difference between the sections, just bureaucratic accounting.
- Tuesday, January 15: First day of class.
- Tuesday, January 22: Project proposals (see project page) due before class through Canvas.
- Each Tuesday thereafter: Brief status update due before class, through Canvas, specifying what you’ve worked on that week, what problems you’ve encountered or solved, and what you plan to do in the next week.
- Tuesday, February 26: Final presentations begin. (May move to the 21st, depending on the number of presentations.)
- Thursday, February 28: Last day of classes.
- Tuesday, March 5: Final progress update. Your project should be complete, and you should now focus on the written tutorial.
- Friday, March 8: Written tutorials due.
- Wednesday, March 13: Final grades submitted.