
Computational and Inferential Thinking: The Foundations of Data Science - jdnier
https://www.inferentialthinking.com/chapters/intro.html
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tacon
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...](http://dsp.rice.edu/2016/12/30/michael-jordan-2016-bryce-lecture/)

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

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hodder
"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.

~~~
noahA
FWIW, I took this class in person and the subsequent course that moved to
Pandas [1]. There was definitely a learning curve to pick up the intricacies
of the Pandas module but DS8 allowed me to pick up both the programming skills
(specifically Python skills) and data science necessary without having much
experience in either prior. I would recommend you review this course only if
you don’t know anything about both programming and DS, otherwise just jump to
DS100 if you have the requisite programming knowledge in Python.

[1] www.ds100.org

~~~
jackallis
do you know where the video repos are for ds100? not available in youtube.

