This course teaches best practices for reproducible analytics, from data processing to publication. Participants will learn to implement transparent and reproducible workflows, to effectively code in a team environment, and to keep personal data secure throughout the lifecycle of a research project. The course will teach participants best practices of how to work with data once acquired - cleaning raw datasets, creating indicators, analyzing the data and exporting tables and graphs reproducibly.
The course is fully virtual and self-paced and will run from 29th November to 13th December, 2023. It will use a combination of pre-recorded lectures, live Q&A sessions, and quizzes. The course will not require use of statistical software; however, participants will have access to optional practice exercises in Stata and R. The expected time commitment for the course is 20 hours over the course of the two weeks.
Through the course, participants will learn:
- Why reproducibility matters
- Programming 101
- How to effectively manage, clean and tidy raw datasets
- Best practices for indicator construction and data analysis
- Principles for working with personal data
- How to create reproducible research outputs
Registration is now closed for the Fall 2023 course.