caselets - invitation (and request) to contribute

[Artur Dubrawski]

Team,

Karen has been researching how to bridge an important gap in data 
science training curricula.
Everyone who tried to teach (or learn) data science will probably agree 
that lectures and hands-on
homework do not fully prepare students to be successful data scientists 
in the real-world right
after graduation. Some of that stems from the lack of self-confidence 
due to little to none practical
experience. Karen hypothesizes that this could be partially remedied via 
self-assessment, if
the students were exposed to real-world challenges in small doses, 
chunk-by-chunk, and practice
how to resolve them in an exercise.

She has designed a little experiment (and won a rather humble amount of 
funding for running it)
and she will use the ongoing course on Applied Data Science as a 
platform to give it a try. For the sake
of comprehensiveness of problem coverage and to ensure that this attempt 
is in fact meaningful,
she is seeking our help with producing "caselets" - a little interactive 
assignments - to help populate
her engine.

See below for more info and please reach out to Karen if you could (and 
I truly hope you would) help.

Cheers,
Artur

=======================================
We are building a repository of caselets (caselet = lightweighted case 
study) to help the beginner data scientists to build up data science 
problem solving skills using authentic problems and data sources. The 
caselets will be deployed in an online learning environment where timely 
feedback and explanation will be provided when users work through the 
problems. This is part of the CMU Simon Initiative funded project on 
“Accelerated Apprenticeship” with the goal to teach data science problem 
solving skills at scale.

You're invited to contribute to the repository given your experience in 
solving tough real world problems and/or the mentoring students or 
interns in our lab.

Here are the steps to get started:

1. Pick a domain and dataset. You may refer to a list here, but feel 
free to use your own data sources; https://tinyurl.com/ybdowtn3

2. Pick a subset of skills you want to target. This list gives you some 
idea to start with. It will be helpful to reflect on the tricky problems 
you’ve encountered yourself in your project or those observed when 
mentoring students; https://tinyurl.com/y888mxru

3. Author caselet. A caselet a) problem context; b) data description (in 
the form of tabular summary or plots) ; c) a list of questions (5-7)  
multiple choices questions with correct answers and explanations 
provided; a sample caselet write up can be found 
here.https://tinyurl.com/y7y8bnnu <https://tinyurl.com/y7y8bnnu>


We are aiming to have the first batch of caselets ready to be used by 
Artur’s students right after spring break. So we need to have drafts 
ready by Feb 16th and followed by internal review. Please send Karen an 
email (karenchen at cmu.edu <mailto:karenchen at cmu.edu>)  if you’re 
interested in being part of it.

Thanks in advance!

Karen (Lujie) Chen

Ph.D. Candidate in Information Systems, Heinz College
PIER Fellow (Program of Interdisciplinary Educational Research)
Member of Auton Lab, Robotics Institute
Newell-Simon Hall 3124
Carnegie Mellon University
Pittsburgh, PA 15213


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