References

This reader provides an introduction to the basics of Python, but there’s lots more to learn. Fortunately, many Python and data science learning resources are available for free online or through the library.

Here are a few readers and notes created by DataLab staff:

• README, Write Me!, our workshop about how to organize and document computing projects.

• Principles of Data Visualization, our workshop about how to design clear and effective data visualizations.

• Adventures in Data Science, our course introducing humanities undergraduates to data science techniques.

• Nick’s teaching notes from several years of teaching statistical computing.

And here are a few books created by others that we’ve found useful:

• Python for Data Analysis (2nd edition) by McKinney. An introduction to using Python for data science, by the creator of the Pandas package.

• Python Data Science Handbook by VanderPlas.