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README.md
overplotting.ipynb

README.md

L&S 88: Reproducibility and Open Science

Spring 2019

This course covered questions of reproducibility in data science and the move toward open science. It included topics such as version control, p-hacking, differential privacy, and the infamous Reinhart & Rogoff study, among others.

I developed quite a few materials for this course while I was its connector assistant (a position which involved both curriculum/assignment development and lab assisting). The assignments and demonstrations that I developed for this course are contained in this folder.

Assignment Description
Code Annotation Lab This lab focused on the importance of annotating code with comments and making use of Markdown in Jupyter Notebooks so that others can understand the rationale behind code and so that they can reproduce the analysis.
DataHub Lab This lab, which was unfortunately not deployed, is a matplotlib tutorial framed as analyzing usage of UC Berkeley's Jupyter infrastructure, DataHub. It was not deployed in favor of the Wiki Pageviews Lab, which was more germane to the topic of that week's lecture.
Final Project Template [Not yet included] This is a template for the class's final project, which involved finding an analysis online (e.g. from Zenodo or Dataverse) and attempting to reproduce that.
Markdown Lab This lab was a tutorial in Markdown, specifically GFM, and how it can used in the Jupyter environment
R in Jupyter Lab This lab taught students some (very) rudimentary R and how to use it in Jupyter notebooks in order to demonstrate how different programming languages can be leveraged to complete tasks more efficiently.
Wiki Pageviews Lab This lab, another matplotlib tutorial, is framed from one of Stuart Geiger's analyses of Wikipedia. It demonstrates the basic functions of matplotlib by having students analyze page views for members of Congress over a couple of years.
Overplotting Demo This short demonstration was intended to teach students two basic ways to reduce overplotting problems in preparation for their final projects. It covers setting the opacity (alpha) of a scatter plot and how to jitter categorical variables.
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