Most modules begin when an instructor gets interested in adding a data science component to their course. At this early stage, it’s common to be unsure what the module will look like or to have questions about how much can reasonable accomplished during it. The next three sections will go over how a module gets made and provide tips for creating your own.

Important Considerations#

When thinking about adding a module to your course, there are four important questions to consider.

  1. What do you want students to take away from the module? This might include a particular coding or analysis skill, like ‘calculating the correlation coefficient’ or ‘interpreting histograms’

  2. How much instruction time and resources do you want to devote to the module? Modules that teach more programming skill tend to require more class or lab time.

  3. How much time for development is there before the course? Ideally, the majority of module development takes place in the semester before the course starts, with additional lead time for larger modules.

  4. What data set do you want to use? Having students collect data themselves and aggregate it into a class data set can be engaging but difficult for a short module or a small course. Conversely, data sets available on the internet may need hours of pre-processing to become usable for analysis. The data sets that are easiest to use right away are usually in a spreadsheet-like format, such as a csv or xlsx file. CDSS staff can assist with finding interesting data for you to use, but note that this will extend the necessary development time.

The clearer your answers to these questions, the faster your module can get up and running. If you have questions or would like support with the pre-planning stage, feel free to contact CDSS modules staff.

Ideas For Modules#

  • Replicate a study from a research paper currently used in the course

  • Have students collect quantitative data related to your subject that can be put in a table and analyzed as a class

  • Adapt an existing homework or lab with a computational aspect, perhaps something historically done on paper or in a program like Microsoft Excel, to a Jupyter notebook

  • Reconfigure an existing module that you saw and liked to use an interesting data set relevant to your course. You can browse previous modules at the modules website (easier to navigate but fewer examples) or at the modules github (harder to navigate but contains all past modules).