Summary
Chapters
Video Info
Soubhik Barari, PhD student in Political Science at Harvard University, outlines ten data science principles to ensure successful social science research: focus on the big picture, know your data, build reusable and flexible tools, structure your project directory, prioritize reproducible code, use version control, name the files and folders, ownership of project components, sources of bias, and creation of painless code.
-
Chapter 1: Foundational Principles to Guide to Successful Data Science
icon angle down -
Chapter 2: One: Remember the Big Picture Goal, but be Ready to Change Specific Questions Based on the Data
icon angle down -
Chapter 3: Two: Know How Your Data were Created
icon angle down -
Chapter 4: Three: Focus on Building Flexible, Reusable Tools
icon angle down -
Chapter 5: Four: Structure Your Project Directory
icon angle down -
Chapter 6: Five: Prioritize Reproducible Code
icon angle down -
Chapter 7: Six: Use Version Control to Maintain Reproducible Code
icon angle down -
Chapter 8: Seven: Use the Shortest and Most Informative Names
icon angle down -
Chapter 9: Eight: Assign General and Specialized Ownership of Project Components to Team Members
icon angle down -
Chapter 10: Nine: Discuss Sources of Social Bias in Your Research Outcome
icon angle down -
Chapter 11: Ten: Try to Make Your Code Painless to Work With
icon angle down