Part 0: Prepare
- ds4psy: Introduction (i.e., this chapter)
- r4ds: Chapters 1 & 2.
Part 1: Explore
- Visualizing data
- Transforming data
Part 2: Wrangle
- Importing data
- Joining data
Part 3: Program
The following appendices provide supporting materials for the ds4psy course:
Appendix A provides the solutions to exercises.
Appendix B provides details on the datasets used in this course.
Appendix C describes characteristics of successful data science projects.
Other appendices are of a more general nature and provide introductions to specific topics:
The following diagram provides a schematic overview of the topics and corresponding chapters in our course curriculum. Whereas the colors of the horizontal lines signals which sessions belong to different parts, the height of each bar reflects the relevance of each chapter within this course:
As you can see, the first four chapters are particularly important. And as the contents of later chapters build and depend on earlier ones, please make sure that you really make a solid start in your endeavours.
Arranging the same information in a clock-wise fashion is a bit gimmicky, but allows signaling our current position (by highlighting it in yellow):
This course mostly uses the so-called flipped classroom paradigm, in which students are solving exercises — in pairs or small groups — with additional guidance by the instructor (see Wikipedia: flipped classroom for details). So what should you do when?
Before class: You need to prepare every session at home by
reading the current chapter, and
.Rmdscript that includes all chapter code (without the exercises).
During class: Every session consists of 2 parts:
Asking questions (plenary): To start each weekly session, the instructor introduces the current topic and answers questions on main concepts or commands.
Begin with weekly exercises (in random dyads/triples): Copy exercises into your script and solve them in a way that you can discuss/share with other members of the class.
After class: Finish exercises at home and submit your solutions on Ilias by Thursday of the same week (23:59).
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz