Objective
Given a dataset, is it possible to automatically discover insights?
Can a machine analyse data and identify the most interesting patterns?
This talk walks through the basics of automated analysis techniques in Python that you can apply to most datasets.
Description
The talk will cover:
- How to identify the numeric and non-numeric fields in a dataset
- The kinds of analyses that can be performed -- e.g. correlation, comparing means, etc
- How to identify the "interestingness" of each result, and show only the most interesting ones.
The analysis will entirely by in Python. NumPy and SciPy, in particular. Some samples of functions that'll end up being used are: scipy.stats.pearsonr, scipy.stats.kruskal, scipy.stats.ttest_ind, etc
Speaker bio
Anand is a data scientist at Gramener, a data visualisation company. For more about Anand, visit http://s-anand.net/
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Thanks for nice presentation.. please upload the slides
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loved the presentation..would love to have a full time tutorial from your side :)
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I too, would love to see a blog post series or a video series that covers this fairly in depth :)
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Anand, not sure how the talk is about anything to do with Python, since from the description this looks like a generic data analysis talk. Please give information or links to the Python tools/libraries you are going to mention in this talk.
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The analysis'll entirely by in Python, Anand. NumPy and SciPy, in particular. Some samples of functions that'll end up being used are: scipy.stats.pearsonr, scipy.stats.kruskal, scipy.stats.ttest_ind, etc
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Ok - Sounds good. Thanks for clarifying. Kindly update the description to reflect it since evaluators may not look at comments.
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Hi anand, I missed it in the two days confusing competition. Caught 10-15 mins of a late running session and wanted to catch more. Can you put up the code/examples/presentation somewhere online?
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The slides are at http://www.slideshare.net/gramener/automated-data-analysis-with-python and the code at https://github.com/sanand0/pyconindia2012-autolysis