caselets - invitation (and request) to contribute

[Lujie Chen]

Thank you for those signing up.

The workshop will take place on next Monday Feb 12th 9:00am-12:30pm at NSH
3001. Please bring your laptop to the meeting.

The rough schedule of the meeting is

9:00-11:00    braining storm on each one's caselet
11:00-12:00  individual work on authoring caselet (you may work in your
office or in the room, but do come back for lunch)
12:00-12:30  lunch and wrap up

To prepare for brainstorming, please bring one dataset/case that you're
interested , and be ready to brief on the data/problems and propose a few
challenges to be addressed in the caselet.
Please sign up for your caselet here to avoid duplication. To help with the
transition, please have the link to your presentation ready before the
meeting.
https://docs.google.com/spreadsheets/d/1u86AGII99nJNUn0vQql0658IlVfVN
g766ldDSm5KRws/edit?usp=sharing


Lunch will be delivered around noon. Pleas fill out the lunch order from
Coriander Indian Grill, by Sunday noon Feb 11th
https://goo.gl/forms/w59nMGhAr5UZs1cw2


Thank you for your interest in educating the next generation of data
scientist!

(If you missed the doodle poll and is available to attend, please send me a
note)


Thanks,
Karen













On Tue, Feb 6, 2018 at 11:15 AM, Artur Dubrawski <awd at cs.cmu.edu> wrote:

> We got very little in terms of response to this request.
>
> So, before taking any other measures ;)  let me suggest that we have a 1/2
> day working session
> with nice lunch or dinner food provided courtesy of the Auton Lab at the
> end, so that we could
> help Karen produce useful and robust caselets.
>
> Please indicate your availability thorough this doodle poll:
> https://doodle.com/poll/upcwwz367gnpridu
>
> Thanks
> Artur
>
>
> On 1/24/2018 4:02 PM, Artur Dubrawski wrote:
>
> 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
>
>
> 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)  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
>
>
>
>


-- 

==================

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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