I R. 1 Installing R and RStudio.
2. Getting Started with R and RStudio.
3. R Basics.
4. Programming basics.
5. The tidyverse.
6. Importing data. II Data Visualization.
7. Introduction to data visualization.
8. ggplot2.
9. Visualizing data distributions.
10. Data visualization in practice.
11. Data visualization principles.
12. Robust summaries. III Statistics with R.
13. Introduction to Statistics with R.
14. Probability.
15. Random variables.
16. Statistical Inference.
17. Statistical models.
18. Regression.
19. Linear Models.
20. Association is not causation. IV Data Wrangling.
21. Introduction to Data Wrangling.
22. Reshaping data.
23. Joining tables.
24. Web Scraping.
25. String Processing.
26. Parsing Dates and Times.
27. Text mining. V Machine Learning.
28. Introduction to Machine Learning.
29. Smoothing.
30. Cross validation.
31. The caret package.
32. Examples of algorithms.
33. Machine learning in practice.
34. Large datasets.
35. Clustering. VI Productivity tools.
36. Introduction to productivity tools.
37. Accessing the terminal and installing Git.
38. Organizing with Unix.
39. Git and GitHub.
40. Reproducible projects with RStudio and R markdown.