Berkeley Statistics Computational Skills Workshop

UC Berkeley, August 2025

Skills related to computation, coding, and reproducibility are crucial for modern statistical work (applied and methodological), as well as for serving as graduate student instructor for Statistics and Data Science courses. Incoming Statistics graduate students arrive with a wide variety of backgrounds in these areas.

The department will be holding a computational skills workshop the week before classes start, focusing on best practices for computation, code development and statistics/data science workflows.

All incoming PhD and MA students are expected to attend the workshop, which will be held August 20-21. Those who already have extensive work or other experience with the topics can request to opt out of the workshop via this form (which will be considered by department faculty), as can PhD students who plan to focus exclusively on theory and feel the computing skills/tools covered in the workshop aren’t relevant for them.

In advance, there will also be an optional introduction to computing concepts and to Python for those students (particularly those taking Statistics 243) to be held August 18-19. If you are interested, please sign up for the optional introduction.

See the syllabus/overview for more details on workshop content.

Logistics (times and locations)

Optional Sessions (Monday/Tuesday)

Location: 334 Evans Hall

Lunch: On your own.

Snacks: Light refreshments during morning and afternoon breaks

Times:

• Monday August 18, 9:00 a.m - 5:00 p.m. with a one-hour lunch break.
• Tuesday August 21, 9:00 a.m - 3:30 p.m. with a one-hour lunch break.

Core Sessions (Wednesday/Thursday)

Location: 150 GSPP. Enter from the door at 1893 Le Roy Avenue and turn left immediately.

• Wednesday August 20, 9:00 a.m - 5:00 p.m. with a one-hour lunch break.
• Thursday August 21, 9:00 a.m - 3:30 p.m. with a one-hour lunch break.

Lunch: On your own.

Snacks: Light refreshments during morning and afternoon breaks

Preparation for the Workshop

• If you don’t already have a GitHub account, please sign up for an account.
• Bring your laptop as the workshop will focus on hands-on work.
• (Optional/special case) We’ll be using the campus DataHub as our primary computational environment, so you don’t need to install anything on your laptop, though you can install Git and Python on your laptop if you wish.
• (Completely optional) Last year, a few participants mentioned they would have liked to think about the coding problem in advance before Wednesday’s Computational Tools and Practices module. If you think you’d like to do that, simply take a look at the first section of the module and consider writing Python code to implement Newton’s method.

Schedule