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OSE data science project template

This is a template for course projects. We use GitHub Classroom to administrate our student projects and so you need to sign up for a GitHub Account.

Project overview

Please ensure that a brief description of your project is included in the README.md, which provides a proper citation of your baseline article. Also, please set up the following badges that allow to easily access your project notebook.

Reproducibility

To ensure full reproducibility of your project, please try to set up a GitHub Actions CI as your continuous integration service. An introductory tutorial for conda and GitHub Actions is provided here. While not at all mandatory, setting up a proper continuous integration workflow is an extra credit that can improve the final grade.

Continuous Integration

In some cases you might not be able to run parts of your code on GitHub Actions CI as, for example, the computation of results takes multiple hours. In those cases you can add the result in a file to your repository and load it in the notebook. See below for an example code.

# If we are running on GitHub Actions CI we will simply load a file with existing results.
if os.environ.get("CI") == "true":
  rslt = pkl.load(open('stored_results.pkl', 'br'))
else:
  rslt = compute_results()

# Now we are ready for further processing.
...

However, if you decide to do so, please be sure to provide an explanation in your notebook explaining why exactly this is required in your case.

Structure of notebook

A typical project notebook has the following structure:

  • presentation of baseline article with proper citation and brief summary

  • using causal graphs to illustrate the authors' identification strategy

  • replication of selected key results

  • critical assessment of quality

  • independent contribution, e.g. additional external evidence, robustness checks, visualization

There might be good reason to deviate from this structure. If so, please simply document your reasoning and go ahead. Please use the opportunity to review other student projects for some inspirations as well.

Project Example

The notebook example_project.ipynb contains an example project by Annica Gehlen from the 2019 iteration of the OSE data science class at Bonn University. It replicates the results from the following paper:

Lindo et al. (2010) examine the effects of academic probation on student outcomes using a regression discontinuity design. The analysis is based on data from a large Canadian university and evaluates whether academic probation is successful in improving the performance of low scoring students. Consistent with a model of performance standards, the authors find that being placed on probation in the first year of university induces some students to drop out of school while it improves the grades of students who continue their studies. In a more general sense, academic probation can offer insights into how agents respond to negative incentives and the threat of punishment in a real-world context.