A Data science projects
This appendix sketches the criteria and some ideas for interesting and engaging data science (DS) projects.
We distinguish between two types of projects:
Basic DS projects
A basic DS project could find and explore an interesting real-world dataset (as in Chapter 15: Exploring data) in order to address some hypothesis or answer some question.
Advanced DS projects
Advanced DS projects should integrate various chapters and topics and go beyond existing examples. Such projects could create new models or simulations, contribute to existing or create new R packages, or use existing R functions in interactive applications (e.g., using Shiny).
Desiderata
Key ingredients of a successful DS project include:
- Ask an interesting question that can be answered within a course project
- Sketch the method or model that is suited to answer the question
- Find or generate suitable data
- Implement the method or model
- Interpret your results (with summaries or visualizations)
- Document and your methodology and conclusions (in a .Rmd-/.html-file)
- Please note which R functions and packages you have been using for solving which tasks
All sources and steps of the data acquisition and analysis process should be documented (e.g., in an R Markdown notebook) and submitted as a zipped archive of an R project.
Ideas
The following ideas for advanced DS projects are based on Part 6: Applications:
- Comparing strategies in games (e.g., heuristic vs. learning agents)
- Performing a social network analysis
- Creating a mate search simulation
- Creating a foraging model (e.g., comparing heuristic or RL approaches in single vs. multi-agent simulations)
- Predicting the stock market (and evaluating portfolio performance)
- Plotting text (see Section 24.3)
- Conducting a sentiment analysis
- Creating artistic visualizations (see Section 24.4.2)
More general ideas for advanced DS projects include:
- Creating visualization functions (e.g., see the R packages unikn, Neth & Gradwohl, 2024; unicol, Neth et al., 2023; riskyr, Neth et al., 2022)
- Revising and extending functions from existing R packages
- Using devtools for creating new R packages (Wickham, 2015; Wickham & Bryan, in progress)
- Using R Shiny for creating interactive dashboards
Resources
In the Data Science for Psychologists textbook (Neth, 2023a):
Appendix C: Data science project provides general advice for successful data science projects
Appendix B.3.3 links resources for finding data online
Inspirations for models and simulations:
Page (2018) contains dozens of models that could be implemented in simulations
The Learning Machines blog provides many inspirations that can be developed into projects