01. Start with R + Git

The importance of reproducibility. Ideas of computational statistics, data science, and machine learning. Some resources for starting with R + RStudio + Git + GitHub.

Artwork by @allison_horst.

Agenda

August 25, 2025

1. Syllabus & Course Outline
2. Reproducibility & GitHub

Readings

• The syllabus
• Modern Data Science with R Prologue
• Class notes: Introduction
• Why Git? + monsters

Reflection questions

• What can statistics & data science do? How do they do that?

• What can’t statistics & data science do? Why not?

• What choices were made to collect the Twitter data?

• What choices were made to model the Twitter data?

• What are the advantages and disadvantages of high touch versus low touch data?

Ethics considerations

• Why is it problematic if the analysis isn’t reproducible?

• Is every analysis worth doing? (e.g., time to get to work, predicting presidential results, etc.). Can the act of doing the analysis be ethically questionable?

Slides

• In class slides for 8/25/25.

• WS1 - Twitter

Additional Resources

• Great algorithm for the whole process

• Design Challenge (Not So Standard Deviations), listen to the full conversation.

• Video (less than 2 min) on the strengths of reproducible research

• R vs. Python? (My personal opinion is that neither of the languages is “best”.)

• 2017 Kaggle user survey and 2019 Stack Overflow Developer Survey

• PNAS paper retracted due to problems with figure and reproducibility (April 2016)

• Analysis of Trump’s tweets with evidence that someone else tweets from his account using an iPhone part 1 and part 2