Community Data Science Workshops (Spring 2014)/Saturday May 31st lecture: Difference between revisions

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{{CDSW Moved}}
 
== Material for the lecture ==
 
For the lecture, you really will need two files. Download both of these to your computer by using right or control click on the link and then using ''Save as'' or ''Save link as''. Keep track of where you put the files:
 
* http://mako.cc/teaching/2014/cdsw/build_hpwp_dataset.py
* http://makonada.cccom.washington.edu/~mako/hp_wiki.csv
 
 
== Overview of the day ==
 
* Lecture
** New tools!
** Our philosophy around data visualization: use the tools you have
** We're going to walk through some analysis of edits to Harry Potter in Wikipedia, start to finish
** We'll focus on manipulating data in Python
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** break
** string.join()
* My philosophy about data analysis: ''use the tools you have''
* Walk-through of <code>get_hpwp_dataset.py</code>
* Look at dataset with <code>more</code> and/or in spreadsheet
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** csv.DictReader()
* Basic counting
** Answer question: ''What areproportion theof mostedits editedto articles onWikipedia Harry Potter articles are minor?''
** Count the number of minor edits/proportion
*** Count the number of anonymousminor edits/ and calculate proportion
** Answer question: ''What proportion of edits to Wikipedia Harry Potter articles are made by "anonymous" contributors?''
* Advanced counting
*** Count the number of anonymous edits perand calculate articles?proportion
 
** Answer question: What are the most edited articles on Harry Potter?
We mostly worked on these questions in the afternoon:
** Count the number of edits per use?
 
** Answer question: Who are the most active editors on articles in Harry Potter?
* AdvancedMore advanced counting
** Answer question: ''What are the most edited articles on Harry Potter?''
*** Count the number of edits per use?articles
** Answer question: ''Who are the most active editors on articles in Harry Potter?''
*** Count the number of minor edits/proportion per user
* Looking at time series data
** "Bin" data by day to generate the trend line
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