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Community Data Science Workshops (Fall 2014)/Reflections: Difference between revisions
Community Data Science Workshops (Fall 2014)/Reflections (view source)
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{{CDSW Moved}}
:''If you're interested in putting on your own CDSW, you should also see our [[Community Data Science Workshops (Spring 2014)/Reflections|reflections from Spring 2014]].''
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We had 30 mentors who attended at least one of the sessions and at least 20 mentors at each sessions. Many of our mentors were UW students in more technical departments like [https://www.cs.washington.edu/ Computer Science and Engineering] and [https://www.hcde.washington.edu Human Centered Design & Engineering]. Perhaps half of them worked outside of the university as software developers.
We had about 150 participants apply to attend the sessions. We selected on programming skill (to ensure that all attendees were complete beginners), enthusiasm, and randomly to maintain a learner to mentor ratio of between 4 and 5. We admitted 80 participants. 58 listed a UW
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We had two people each who listed their affiliations as Bio- and Health Informatics, the Foster School of Management, Microsoft, and Wikipedia.
We also had people from
Retention between session and 0 and 1 was nearly 100%. Retention between sessions 1 and 2 and sessions 2 and 3 was roughly 75% leaving us with perhaps 55-60% retention between session 0 and session 3.
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Once again, quite a large number of people applied were already skilled programmers. We're still not exactly sure why these people are applying because we think that the fact that the workshops are for absolute beginners is very clear. Perhaps people just want more exposure to data science?
Once again, the constraint on scaling the workshop was the number of mentors. Every mentor we added means that the workshop can accommodate four more
One suggestion was allowing
== Morning Lectures ==
[[User:Mako|Benjamin Mako Hill]] gave lectures in Session 1 and 3. Frances Hocutt gave the lecture in Session 2 and
Our biggest challenge with growing the workshops was with physical space for the lectures. Basically, rooms
We reserved a lecture hall that fit 200 people and filled it with 100 students in alternating rows to make it at least possible to reach each person.
People continue to want a record of lectures. At the very minimum, we should make sure that we turn on console logging so that we can post this after the lectures. We intended to record lectures but, once again, this got lost in all the crazy preparation for the events.
== Afternoon Sessions ==
Projects are done in breakout sessions in a series of three rooms. The general problem was that insisted on teacher per topic and topics were very unequal in their popularity. Next time, we will likely prepare to have multiple teacher for multiple rooms on topics we know will be more popular.
Several changes we hope to make include:
* As we refine this process, we were also interested in thinking of trying to select or refine breakout sessions so that they are more closely tailored to individuals and their interests. Next time, we will consider mining the registration for a list of research questions we might use.
* We want to emphasize bringing people back together more often. In particular, we found that bringing people together back together share work several time during each session and then once in the end to show of achievements or interesting results was effective. We also need to designate a person to a person to go between for each session to remind people to reconvene and to create a program of important or inspiring achievements for presenting to the group at the very end.
* There seemed to be broad interest in examples or projects that are focused on public health and/or epidemiological data.
* We would love to create an afternoon project for Session 3 on basic statistical analysis in Python using scipy, statsmodels, and pandas. At least ten participants would have been enthusiastic to take it.
== Session 0: Python Setup ==
The goal of this session was to get users setup with Python and starting to learn some
* Anaconda is not free software
* Anaconda does not support Python 3 which we'd like to move to.
*
Additionally, we moved the Windows curriculum from away from <code>cmd</code> to using Powershell. This was
Changes for next time include:
* Because it was less
* Because Powershell was successful, we're going to try to create a single consolidated set of installation instructions for Windows, Mac OSX, and GNU/Linux
* We will make it more clear to mentors whether participants should self-report they’d completed the steps or whether the mentor should verify that the steps were all taken (the latter). In future, we will email mentors ahead of time to let them know.
* In a related issue, not everybody loves the checkout step. Maybe there's a way we can make it more fun?
* We need to do a better job of modeling sticky notes so folks use them more effectively.
* The sticky notes we bought were small and ambiguous color. We should get large red sticky notes next time.
* We are going to try writing additional installation instructions that do not rely on Anaconda so people have a fully open source option.
* Once again, not a single person outside of
* We
We also had [[Community Data Science Workshops (Fall 2014)/Reflections#Mentorship|a bunch of general feedback on how we could improvement mentorship]] that is particularly relevant to this session.
== Session 1: Introduction to Python ==
The goal of this session was to teach the basic of programming in Python. The basic curriculum was originally built off the [[Boston Python Workshop]] curriculum which has been used many times and is well tested. Unsurprisingly, it worked well for us as well.
That said, we made several major changes this time around. The biggest is that we retained only the [[Wordplay]] project. We also created a new project, [[Baby Names]], that uses Social Security Administration data on the frequency of Baby Names.
=== Afternoon sessions ===
We felt that that the new [[Baby Names]] project was excellent and feedback was
Suggestions based on feedback include:
* Do a better job of
* Consider simply having two smaller rooms doing [[Baby Names]] and perhaps
* Prepare questions before hand, list them all up front, and let folks choose what to work on.
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=== Morning lecture ===
The [[Community Data Science Workshops (Fall 2014)/Day 2 lecture|morning lecture]] was given by Frances Hocutt and it
Frances used excellent slides which are shared [[Community Data Science Workshops (Fall 2014)/Day 2 lecture|on the wiki page]] and which we will reuse. About half found
Since many people felt the lecture was on the slower side, we want to use this time to introduce function
=== Afternoon sessions ===
There were three parallel afternoon sessions on '''Twitter''', '''Wikipedia API''' and '''SQL'''.
'''Twitter''':
* Once again, the session had too many people for the room and we should consider splitting it if we have mentors who are comfortable
*
* A bunch of people found the Twitter session too fast
* TweePy continues to be both poorly documented and opaque. The opaqueness of TweePy was a problem and we may want to create an interface to TweePy that just gives users raw JSON.
'''Wikipedia''' workshop:
* In terms of delivery, there was mixed feedback including some excellent feedback and some who felt that it was too detailed and slow. This mirrored some of our feedback from last time. One approach would be to make the Wikipedia room be a designated "slower" room.
*
'''SQL workshop''':
Jonathan ran a session on using SQL. Although this was a diversion from the strong Python focus, it was well attended and appreciated by students trying to build up this skill.
* Generally the session was was very successful and seemed to do a good job of giving people an overview of a data science and a way to hook themselves in to it.
* Next session, if we do this again, we should consider integrating Python more closely into this. We may either close the loop in this session or perhaps split into two sessions: (1) introduction to SQL; and (2) using Python to bring data back into Python (e.g., in Pandas).
* We should consider hosting an open SQL database somewhere.
== Session 3: Data Analysis and Visualization ==
The goal of the lecture was to walk people through the actual mess of writing code from scratch and focused on a single example of code that builds a dataset from Wikipedia.
In general, goals were clearer this time and the use of Anaconda meant that we could use <code>requests</code> which cleaned up several problems last time and led to more clear code.
One challenge, pointed out in a question at the end of the final lecture, is that we don't actually do very much actual data analysis during the lecture. Next time, we should make this much more clear up front. The reality is that we were doing analysis from the very first day and that where analysis starts and where data cleaning and munging ends can be fluid, fuzzy, and subjective. We should foreground this in the beginning of the lecture or even at the beginning of the workshops.
=== Afternoon sessions ===
We ran two sessions this time.
An '''analysis with spreadsheets session''' similar to what we taught last time. This was improved and more effective. By the end, many participants were modifying the code to build their own datasets and doing their own visualizations. One student built a time series of edits to articles about death by police and another to articles about the NFL. In both cases, real patterns driven by current events became clearly visible.
We also ran a session on '''matplotlib''' which was taught by two mentors we brought in specifically to teach it but who had limited experience with the CDSW. Some people in the session were lost. Because the mentors who taught it were not at the other sessions, they therefore didn’t go in with a good sense of where the participants were at. In the future, we should loop in teachers better to where the participants are at. For example, we might encourage new mentors do a practice session with some friendly folks before they let loose.
Also, next session, we are going to consider using [https://pypi.python.org/pypi/seaborn/0.1 SeaBorn] instead of matplotlib which Tommy seemed excited about.
== General Feedback ==
* Generally, there was a sense that we should stop creating pages in the
* We should try to schedule the workshop not
* Mentors should post the code generated in the break-
* There was general interest in pair programming or more team based
* There was a need for several on-the-fly corrections of the instructions and files on the wiki during the workshop. Better planning and testing for this will be very useful.
=== Mentorship ===
Last time through, most of our observation were focused on improving the experience of attendees and we think we didn't spend as much time on helping mentors have a great experience and helping them prepare effectively. We had many new mentors this round. One general concern was the relative lack of mentor training, especially before the first sessions. We had a series of pieces of feedback on how to improve this.
* Arrange a pre-CDSW mentors meeting (perhaps a day or two before to over material) and maybe at a bar or other social environment with beer and pizza. We
* Perhaps meet 15-20 minutes early before Session 0 to get to know each other and over things.
* Create some easier way to distinguish mentors from students (e.g., t-shirts, buttons, paper them head to foot in sticky notes).
* Send out
**
** Explicitly encourage mentors to reach out to students and ask them how things are going by walking around to every single person to ask, “How are you doing? What are you working on? Show me what you’re doing.”
=== More Projects or Better Projects ===
Arguments for smaller groups of the best break-out session include:
* Focus on a known good thing.
*
Arguments against include:
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* Diversity of projects inspires people to do the kinds of things that people can do with this new knowledge.
We should pursue other ways to encourage creativity with code. For
example, we might give participants creative/flexible moments within sessions and lectures might be empowering in similar ways. We can also continue to call out participants who are doing creative things.
== Budget ==
We spent a total of $3280 on the CDSW. We spent approximately $280 on coffee. About $350 of this funded food and refreshments during post-session meetings among the mentors. About $280 was spent on coffee,
The rest (the large majority) was spent on food. Because were better able to model retention this time around, we did a much better job of ordering the "right" amount of food. We ordered:
* Session 1: Pizza from Jet City Pizza
* Session 2: Indian (four entrees) from Jewel of India
* Session 3: Greek food (e.g., salad, hummus, spinach pies, souvlaki) from Costas
Because [[Mako]] did the ordering, everybody ate vegetarian. At least one person complained about the lack of meat in Session 2 (but seemed to be confused into thinking it was present in Session 1).
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'''The ethnographers get the last word:'''
Some observations about the culture of mentoring from a first time mentor: There are some distinct values that came through strongly. There is a clear vision of empowerment through programming. The degree of inclusivity is impressive. The culture of feedback, iteration, and reflection was really surprising such as the amount of effort that goes into improving the materials and the teaching. As is the way that other organizations are able to (and are) using the materials. The way that this is building the community. For example, how
The pragmatism of what is taught demonstrates a clear value. It would be helpful to make sure that all mentors are clear that part of what is expected of them they give pragmatic coaching. That is they should lead
-->
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