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

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* 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,eexample).
* 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. Althought 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 answerinig questions.
 
AfterwardAfterwards, 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.
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