Computational and Inferential Thinking: The Foundations of Data Science

I attended a terrific lecture by Michael Jordan at Rice in 2016[0] where he introduced the idea of inferential thinking. Jordan has several videos of that talk online, such as [1]. The big idea, the grand challenge, is that core statistical theory doesn't have a place for runtime and other computational resources, and core computational theory doesn't have a place for statistical risk. Merging those two tracks into one body of knowledge will keep people busy for decades.

After his lecture, I asked Prof. Jordan about the term "inferential thinking", as I was familiar with Jeanette Wing's work on computational thinking. He said that he coined the term "inferential thinking".

[0] http://dsp.rice.edu/2016/12/30/michael-jordan-2016-bryce-lec...

[1] https://www.youtube.com/watch?v=bQ02K0kWKzg

"The course uses a module for table manipulation, charts, and maps that provides an interface appropriate for an introductory course. The Table class is similar to a DataFrame in Pandas, but explicitly does not support row indexes, hierarchical indexes, time series data, missing values, slicing, and many other advanced features that can complicate table manipulation for novices. The charting features use Matplotlib, but customize the output to match the pedagogical goals of the course. The mapping features are implemented by Folium, but aim to simplify working with tables and geojson files. While the datascience module can certainly be used outside the context of the course, it was specifically designed to support the Data 8 curriculum, while setting up students to transition to more standard tools such as Pandas."

I can't imagine anyone not migrating into pandas after the course, therefore I question the usefulness of teaching a simplified tool in advance. That said, the course looks well put together.