cis-ds/course-site

Updated schedule of topics for info sci course

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bensoltoff commented

Current schedule

  1. Intro/grammar of graphics
  2. Data transformation/EDA
  3. Data wrangling (tidy data/relational data and factors)
  4. Core programming concepts (pipes and functions/vectors and iteration)
  5. Debugging/intro to R Markdown
  6. Reproducible workflows/reprex and Git
  7. Machine learning
  8. Getting data from the web (API/web scraping)
  9. Text analysis (fundamentals and sentiment analysis/supervised and unsupervised learning)
bensoltoff commented

Cornell runs on 16 week semester. Meet twice a week = 32 class meetings. In the fall there are 4 days when class is canceled (Labor Day, Fall Break, Thanksgiving Break, last week of classes ends on a Monday so no Wednesday meetings). Total of 28 instructional days, 10 more than what I have now.

Class periods missed

  • 5 (Labor Day)
  • 15 (Fall Break)
  • 28 (Thanksgiving)
  • 32 (pre-exam period)

Revised schedule

  1. Intro to the course
  2. Grammar of graphics and ggplot2
  3. Data transformation
  4. Exploratory data analysis
  5. No class (Labor Day)
  6. Data wrangling - tidy data
  7. Data wrangling - relational data and factors
  8. Core programming concepts - pipes and functions
  9. Core programming concepts - vectors and iteration
  10. Core programming concepts - tidyeval
  11. Debugging
  12. Reprex and asking for help
  13. Reproducible documents - Quarto and basic documents
  14. Reproducible documents - extensions to slide decks, websites, deploying with Netlify
  15. No class (Fall Break)
  16. Reproducible workflows
  17. Intro to Git
  18. Extended Git workflows
  19. Machine learning - intro to ML, build models with parsnip, resample data with rsample
  20. Machine learning - build better training data with recipes, create workflows with workflows
  21. Machine learning - hyperparameter tuning with tune
  22. Getting data from the web - API
  23. Getting data from the web - web scraping
  24. Geospatial viz - raster maps
  25. Geospatial viz - vector graphics and sf
  26. Text analysis - fundamentals and sentiment analysis
  27. Text analysis - supervised and unsupervised learning
  28. No class (Thanksgiving)
  29. Shiny apps - 1
  30. Shiny apps - 2
  31. Improved data communication
  32. No class (pre-exam period)