Chapter 14 Applications

This chapter marks a new part (and course): Applications.

In this part, we use what we have learned so far to solve interesting problems. In fact, as the previous chapters essentially provided an introduction to R and exploratory data analysis, the term data science only starts to make sense here.

Note that concepts like modeling and Machine Learning are popular, ubiquitous, and often used interchangeably. We will try to clarify and disentangle them by asking more specific questions like

  • What types of representations are being used?

  • What task(s) are being solved?

  • How is performance evaluated?

Topics in this part

The topics addressed in this part include:

  • Essentials of R:
    • Objects and data structures
    • Programming
  • Modeling:
    • Simulations (e.g., solving Bayesian puzzles, cognitive illusions)
    • Benchmarking (e.g., dynamic environments, bandits, RTA)
    • Prediction (e.g., binary, categorical, decision trees, vs. LR, mLR, logR)
    • Learning (e.g., foraging, MAB, RL)
    • Social networks and games (e.g., rock-paper-scissors, tic-tac-toe, …)
  • Visualization:
    • Defining and using colors (defining colors and color palettes)
    • Tailoring visualizations to tasks (e.g., Bayesian situations, risk perception)
    • R pour l’art (visualizing randomness, plotting text)

Mastering any of these topics and skills requires dedicated practice. The first parts also contain regular exercises that deepen and practice the skills conveyed in each chapter. However, students will also have ample time for working on a project of their choice (towards the end of the course).

14.0.1 Resources

Some pointers to resources for inspirations and ideas:

On models, modeling, and simulations

Modeling in R

Visualization

Machine Learning

The term data science is often used as a fancy name for statistical learning or machine learning:

Other collections and sources of inspiration

References

Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2021). Modern Data Science with R (2nd ed.). Chapman; Hall/CRC. https://mdsr-book.github.io/mdsr2e/
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer. https://www.statlearning.com/
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. http://appliedpredictivemodeling.com/
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. http://r4ds.had.co.nz