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The evening session ran from 6 to 9PM, and involved self-guided completion of setup and introductory exercises. The rest of the sessions followed this approximate structure:
 
* '''Morning, 10:30 AM - 12:00 PM:''' ALecture 1.5with houra lecture15-minute break in the middle.
* '''Lunch, 12:00 PM - 1:00 PM:''' Lunch is served.
* '''Afternoon, 1:00 PM - 1:15 PM:''' Afternoon sessions are introduced.
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We had about 230 participants apply to attend the sessions. About 100 of those were immediately filtered out for eligibility: no math or engineering undergrads were permitted to attend the workshops, as their programs have significant required programming components (often 2-3 classes in far more depth than we covered). We selected on programming skill (to ensure that all attendees were complete beginners), enthusiasm, and overall application quality, and I capped the total at 50 participants given our budget.
 
Sessions 0 and 1 had full attendance, but we lost about half our students for Session 2, which was held four weeks later during midterm season (initially planned to be a week earlier but there was a room booking conflict). Session 3 retained those students that attended Session 2. We attribute this rententionretention to poor timing (the heart of midterm season) and to the long space between the sessions. Session 3 retained most students that attended Session 2.
 
We collected detailed feedback from users at five points using the following Google forms (these are copies):
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=== Morning Lectures ===
 
The CDSW in Seattle began each full day with 2ha 120-minute lectureslecture with no breaks. This was a little too intense for the students, so I decided to reduce the length to 1.5h and break things up with short, self-directed exercises. These went over very well. Furthermore, I'm not as experienced of a lecturer as Mako, so rather than lecturing freeform, I chose to use slides and distribute them to students, who told me it made it easier to follow along.
 
In the Session 3 survey, 35% of respondents said the lectures were "Good", 35% called them "Very Good" and 18% called them "Excellent". 94% of students rated the instructor positively (12% "Good", 47% "Very Good", 35% "Excellent") and the curriculum positively (35% "Good", 41% "Very Good", 18% "Excellent").
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In Sessions 1 and 2, the self-directed projects were based on working through examples from [http://www.codecademy.com/ Code Academy] that we had put from material already online on the website. In the self-directed track, students could work at their own pace with mentors on hand to work with them when they became stuck.
 
In Session 3, one of our session leads did not show up; at the behestrequest of students, I held a single afternoon session that involved working through various data science examples together as a class, and answered general questions about Python programming. It ended up being more of an extension of the morning lecture and a discussion of next steps thanvs. the projects we had imagined.
 
In the other tracks, student would download a prepared example in the form a of a <code>zip</code> file or <code>tar.gz</code> file. In each case, these projects would include:
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* All of the data necessary to run the example programs (e.g., a full English word list for the Wordplay example).
* Any other necessary code or libraries we had written for the example.
* A series of small numbered example programs (~5-10 examples). Each example program attempts to be sparse, well documented, and not more than 10-15 lines of Python code. Each program tried both to do something concrete but also provide an example for learners to modify. AlthoughtAlthough it was not always possiiblepossible, the example programs tried to only used Python concepts we had covered in class.
 
On average, the non-self-directed afternoon tracks constituted of about 30% impromptu lecture where a designated lead mentor would walk through one or more of the examples explaining the code and concepts in detail and answering questions.
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