Community Data Science Workshops (Spring 2014)/Reflections: Difference between revisions

In the afternoons, we broken into small groups to work on "projects". In each afternoon we tried to have three afternoon project tracks: Two projects on different substantive topics for learners with different interests and a third project that was much more self-directed.

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, we did not use Code Academy but instead devoted the self-directed room to students working with mentors on data science projects of their choice. Because of issues with the student to mentor ratio, we asked that students only participate in the self-directed track if they felt confident they could be self-sufficient working on their own 70-80% of the time.

In all 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:

* All of the libraries necessary to run the examples (e.g., [http://www.tweepy.org/ Tweepy] for the Session 2 Twitter track).

* All of the data necessary to run the example programs (e.g., a full English word list for the Wordplay exampl,e).

* 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. Althought it was not always possiible, 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 answerinig questions.

Afterward, the lead mentor would then present a list of increasingly difficult challenges which would be listed for the entire group to work on sequentially. These were usually written on a whiteboard or projected and were often added to dynamically based on student feedback and interest.

Learners would work on these challenges at their own pace working with mentors for help. If the group was stuck on a concept or tool, the lead mentor would bring the group back together to walk through the concept using the project in the full group.

In all cases, we gave students red sticky notes they could use to signal that they needed help (a tool borrowed from [http://software-carpentry.org/ SWC]).