This course builds core skills for reproducible research.

• Build reproducible research projects and workflows
• Create compelling visual arguments
• Draw useful conclusions from data and model results

1 - Syllabus

2 - R Markdown

• Example Notes

• Example Problem Set / Data Exercise (pdf)

• Example Portfolio

3 - Reproducable & collaborative research with git

4 - Tidy data & readable code

5 - Efficient code that does not break

6 - Data visualization & summary analysis

7 - Tidy text analysis

• Counting <legislator letters example>
• Matching <web scraping text example>, <web scraping documents and tables example>, <text reuse example>
• Classification <example>
  • Unsupervised <example>
  • Supervised Learning <content tagging example, <learning example>
• Clustering <example>

8 - Model results

• Hypothesis testing <Gender pay gap example>

• Interpreting Logit <Sentencing decisions example>

9 - Duration models <Department of Transportation rulemaking example>

10 - Computation