Projects

Due dates

Requirements

All Projects must:

• Be done in a Jupyter notebook

• Follow the style guide

• Use at least one dataset you aren’t familiar with

  • Using data from a primary source is preferred.

  • Using a dataset available in CSV or JSON is recommended, though pandas can read other formats.

  • Open data portals

  • If you’d be interested in working with SIPA alumni employment data, reach out to the instructor.

• Link to the source dataset(s)

• Read like a blog post

  • Pretend you’re explaining to a peer who hasn’t taken this class. You don’t need to teach them to code, but they should be able to follow what’s going on.

  • Walk the reader through what you’re doing in every step and what they should be taking away from it.

    • You are more than welcome to inject personality in there; doesn’t need to be dry.

  • Use text cells with Markdown for formatting.

    • You’ll need to change the cell type to Markdown.

  • If you hit any dead ends in your analysis, leave them in.

    • For example, include charts that you generate that may not show anything interesting and explain what you are choosing to look at instead.

    • You should still be cleaning up unused/broken code to make your notebook readable.

    • You may need to tweak your research question as you go. Show and explain why.

• Values must not be hard-coded

  • In other words, if the dataset got additional rows, had values changed, etc., the rest of the notebook should work as expected.

• Don’t leave any sensitive information in the notebook, such as:

  • API keys

  • Personally-identifiable information (PII)

Visualizations

All visualizations must:

• Have a title that’s clear

• Have axis labels that are clear

  • Include units, if applicable

  • Not required for Project 1.

• Look decent on a narrow (11”) laptop screen

Tips

• The Final Project resources from Python for Public Policy may be helpful.

• The dataset/analysis can be serious or silly.

• You can be creative, but start simple.

• Go back and find any information that’s available around the data, to get a better understanding of what it contains and means.

  • Might include a data dictionary

  • Might involve poking around a government agency’s web site to understand their processes

  • Understand what all the different columns and values represent

• For these Projects, the important thing is getting practice with the mechanics of working with data in code.

  • You won’t be graded on the scientific soundness of your work.

    • That said, please think through and note assumptions/caveats/unknowns of your approach.

• DON’T WAIT UNTIL THE LAST MINUTE TO SUBMIT. Leave yourself time to fix any issues that come up in doing so, computer crashing, etc.

Visualizations

• Consider which visualization type is most appropriate.

• If you’re trying to show more than three variables at once (e.g. X axis, Y axis, and color), try simplifying.

• Other best practices.