Before You Begin

If you plan to teach a course using these materials, it’s important to note that they are intended to be used in a flipped classroom style.  So what’s a flipped classroom?

In a flipped classroom, students experience new material for the first time at home, on their own time.  Class time is utilized for application, contextualization, and more in-depth explanation.

Using this approach, there are four pillars to a successful data science course for social scientists, resources for all of which are provided on this website:

  1. Initial exposure and practice coding at home, on students’ own time, on an interactive online coding platform.
  2. Reinforcement of key concepts in the social science context and introduction of skills not covered in the online practice.
  3. Applied projects that integrate what is learned in #1 and #2 above.
  4. An interactive debriefing stepping through the skills needed and used in #3.

Let’s look at these in detail.

First, one of the best teachers of basic programming skills is repeated practice with real-time feedback during initial exposure.  This is not something you can do during a lecture.  Instead, learners complete modules from an online, interactive coding platform that provides real-time feedback.  This helps build “programming chops” – the ability to write code and later be able to diagnose what’s wrong with it.  This website does this using the platform.  Sheer amount of practice is important here, and students should be able to work on this for as many hours as they need.  It is very easy to form bad habits when first learning to write code, so you really need someone “standing over your shoulder,” so to speak, to avoid needing to un-learn those habits later.  DataCamp accomplishes this well.

Second, the best teacher of applied programming is contextualization.  Most programming courses out there are not specific to social scientific applications, so this is where the classroom component is important. Here, I provide lecture slides if you want to base your own course off of my previous data science courses, or if you want something more plug-and-play or are doing this self-paced, I provide lecture videos which you can watch.

Third, applied projects allow students to continue practicing the skills learned during their initial exposure and apply them to social scientific problems.  In the current course, these are generally examples from psychology, but you could easily replace these examples with your own as long as the same key learning objectives are being tackled.  Although the programming skills become more useful the further students progress through the course, even the very first project contains an example of how first-week skills apply to real research tasks.

Fourth, the most critical component of the course is a live debriefing, stepping through how to accomplish each portion of the applied project.  The best in-person versions of this course will feature a live instructor writing the entire project from the project assignment document live.  This demonstrates the thought processes of the lecturer (i.e., an intermediate or expert R user), similar to an expert demonstration practice called a think-aloud, stopping to answer questions as needed.  For anyone without the resources or self-confidence to do this, I also provide videos recordings of my own stepping through of projects.  Some of these are in front of a live audience and some of these are recorded, mostly for technical reason (e.g., I forgot to turn on the recorder one day!).  Eventually, I will replace all videos with non-live versions to ensure higher consistency of recording quality, but that will come later.

If this sounds good to you, browse the schedule and materials to get started.