C.3 Resources

This page provides pointers to additional resources, as well as some notes on formal requirements.


Abundant amounts of data can easily be found online, but they were often collected mechanically or on the basis of rather rudimentary questions (e.g., How does some variable vary as a function of some other variable? How does it change over time?). To become interesting and useful, data must be analyzed with good questions in mind.

  • See Appendix B (especially Section B.3.3) for a list of data sources.


Your project should be presented in the form of a report (in html or pdf format) that includes tables and/or visualizations.


It is impossible to provide shortcuts for asking and answering good questions. Essentially, asking and answering questions is what science is all about. So be curious, ambitious, and dare to find out what you or others want to know.

There really is no shortage of theoretical and empirical questions in science. Explicating and answering them is a task that can be strenuous or enjoyable, but always requires a sound combination of data, tools, and skills.

The two types of projects, suggestions for, and requirements of successful DS projects for the current Introduction to data science courses are noted in Appendix A: Data science projects of the i2ds textbook (Neth, 2024).


[60_dsproject.Rmd updated on 2024-07-08 15:53:43.361953 by hn.]


Neth, H. (2024). Data science for psychologists. Retrieved from https://bookdown.org/hneth/i2ds/