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Team,<br>
<br>
Karen has been researching how to bridge an important gap in data
science training curricula. <br>
Everyone who tried to teach (or learn) data science will probably
agree that lectures and hands-on<br>
homework do not fully prepare students to be successful data
scientists in the real-world right<br>
after graduation. Some of that stems from the lack of
self-confidence due to little to none practical<br>
experience. Karen hypothesizes that this could be partially remedied
via self-assessment, if<br>
the students were exposed to real-world challenges in small doses,
chunk-by-chunk, and practice <br>
how to resolve them in an exercise.<br>
<br>
She has designed a little experiment (and won a rather humble amount
of funding for running it) <br>
and she will use the ongoing course on Applied Data Science as a
platform to give it a try. For the sake <br>
of comprehensiveness of problem coverage and to ensure that this
attempt is in fact meaningful, <br>
she is seeking our help with producing "caselets" - a little
interactive assignments - to help populate <br>
her engine.<br>
<br>
<div class="moz-forward-container">
<div dir="ltr">See below for more info and please reach out to
Karen if you could (and I truly hope you would) help.<br>
<br>
Cheers,<br>
Artur<br>
<br>
=======================================<br>
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. <br>
<br>
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. <br>
<br>
Here are the steps to get started:<br>
<br>
1. Pick a domain and dataset. You may refer to a list here, but
feel free to use your own data sources; <a
href="https://tinyurl.com/ybdowtn3" moz-do-not-send="true">https://tinyurl.com/ybdowtn3</a><br>
<br>
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; <a
href="https://tinyurl.com/y888mxru" moz-do-not-send="true">https://tinyurl.com/y888mxru</a><br>
<br>
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.<a href="https://tinyurl.com/y7y8bnnu"
moz-do-not-send="true"> https://tinyurl.com/y7y8bnnu</a><br>
<br>
<br>
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 (<a href="mailto:karenchen@cmu.edu"
moz-do-not-send="true">karenchen@cmu.edu</a>) if you’re
interested in being part of it. <br>
<br>
Thanks in advance!<br>
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<p><span
style="font-family:arial,helvetica,sans-serif;font-size:12.8px">Karen
(Lujie) Chen</span><br>
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<font face="arial, helvetica, sans-serif">Ph.D.
Candidate in Information Systems, Heinz
College</font></div>
<div dir="ltr"><span
style="font-family:arial,helvetica,sans-serif;font-size:12.8px">PIER
Fellow (Program of Interdisciplinary
Educational Research)</span><font
face="arial, helvetica, sans-serif"><br>
Member of Auton Lab, Robotics Institute</font></div>
<div dir="ltr"><font face="arial, helvetica,
sans-serif">Newell-Simon Hall 3124<br>
Carnegie Mellon University<br>
Pittsburgh, PA 15213</font>
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