ID529: Data Management and Analytic Workflows in R

Welcome!

Details:

  • Find the course on my.harvard.edu
  • Course Hours: 1:30-5:30 PM
  • Classroom: Our classroom will be Kresge 502.
  • Course Dates:
    • Monday January 12th - Friday January 16th,
    • Tuesday January 20th - Friday January 23rd, 2026
  • Office Hours:
    • 12:00 PM – 1:00 PM on Wednesday January 14th
    • 11:00 AM – 12:00 PM on Thursday January 15th, Wednesday January 21st, Thursday January 22nd
  • Limit 60 students, priority for Population Health Science (PHS) students

Course Description

Data Management and Analytic Workflows in R will introduce students to R programming and modern data management and analysis workflows applied to examples from population health science. Throughout, we will emphasize reproducibility, open science, data visualization, and dynamic document generation. Specific skills learned will include the use of the RStudio integrated development environment, tidy data management practices/workflows, how to get help in programming, and how to use GitHub to track changes in code, disseminate professional work, and integrate feedback. Coursework will consist of lectures, in-class group work, homework, peer assessment, and time for discussion. This course complements graduate-level courses in statistics and quantitative research methods by helping students develop practical skills for conducting independent research incorporating modern data science principles. Students completing this course will have a solid foundation enabling them to handle complex data management tasks and data communication skills for research and professional work.

Student Testimonials

Students were very happy with how the class went last winter! Here are some student testionials, shared with students’ permission:

“I really enjoyed the whole learning experience in this course.”

“Very informative and useful. As a someone who has his first exposure to R, I learned a lot.”

“The teaching team were very supportive and very promptly acted on feedback.”

“It was wonderful! Totally friendly to R beginners. And got a lot positive feedback and encouragement from the teaching team! Shout out to their efforts!”

“Slides that are managed so well! Unparellel instructional team! You are so friendly and patient! I really love that homeworks are managed through Github!”

“I loved this class!! So much was covered but it didn’t feel overwhelming at the same time because the expectation was that we all came in with different levels of experience with R and that these are resources we are introduced to and can always come back to.”

This course has been excellent! It was exactly what I was looking for - I wanted to kind of catch up to my peers who have had experience in R and learn best practices. R feels a lot less intimidating now, and I know where to look for help. Thank you!

I think this course was great. I am happy that all levels of R were welcome in the course. I felt like I could just do beginner level work and still get a good grade.

Extremely well. I think it will be the most recommended course for whoever wants to gain skills in data management and analysis

And lots more 🙂

Instructional Team

Christian Testa
2nd Year PhD Student
Department of Biostatistics

GitHub
Website
Mastodon
Google Scholar

Dean Marengi
4th Year PhD Student
Department of Environmental Health

Google Scholar
Jarvis Chen
Senior Lecturer
Department of Social and Behavioral Sciences

https://www.hsph.harvard.edu/profile/jarvischen/
Google Scholar

Teaching Alumnus

  • Amanda Hernandez was an amazing masters student in Environmental Health who helped us develop a lot of material and helped teach the course in Winter Session 2023.

Go on to syllabus

Click here to go to the syllabus.

Jump into the Curriculum