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Name: Big Data in Fashion
--client continuum --show pydata_sv_2013 --room a1 2246 --force
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Author(s):	Katherine Chuang
Location	A1
Date	mar Wed 20	Days Raw Files
Start	10:50	First Raw Start	10:48
Duration	00:50:00	Offset	0:01:10
End	11:40	Last Raw End	11:30
Chapters	00:00 0:18:44 0:29:28 0:30:21
Total cuts_time	32 min.

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assets release.pdf Big_Data_in_Fashion.json
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Description:	PythonFashionForecaster is an ongoing open source code project that I'd like to present to the PyData Community in order to initiate discussion about applications of Python in a traditionally non data-centric industry. It will hopefully extend the use of Python and open source to the world of fashion. A quick search of python repositories on github show a lack of true fashion apps, those mostly involving weather forecast or shopping tools rather than specifically fashion styles. On the other spectrum of fashion apps, those highly relevant to fashion styles are commercial. PythonFashionForecaster is different in that the objective is to display fashion style trends as an information resource in an automatic and computational manner. This talk would be of interest to anyone that would like to see a case study on the application of parsing JSON data with Python, a survey of data analysis libraries that can be use to analyze social data, as well as anyone interested in fashion related topics. I believe that indirectly this project will bring exposure to the Python Open Source community in non-traditional domains. markdown
Comment:	production notes

2013-03-20/10:48:50.dv Apply: 10:48:50 - 10:57:41 ( 00:08:51 ) S: 10:48:50 - E: 10:57:41 D: 00:08:51 show more...

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2013-03-20/10:57:42.dv Apply: 10:57:42 - 11:16:26 ( 00:18:44 ) S: 10:57:42 - E: 11:16:26 D: 00:18:44 show more...

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2013-03-20/11:16:26.dv Apply: 11:16:26 - 11:27:10 ( 00:10:44 ) S: 11:16:26 - E: 11:27:10 D: 00:10:44 show more...

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2013-03-20/11:27:10.dv Apply: 11:27:10 - 11:28:03 ( 00:00:53 ) S: 11:27:10 - E: 11:28:03 D: 00:00:53 show more...

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2013-03-20/11:28:04.dv Apply: 11:28:04 - 11:30:32 ( 00:02:28 ) S: 11:28:04 - E: 11:30:32 D: 00:02:28 show more...

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