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Any thoughts on the best resources for learning data mining ie. can anyone suggest must read sites, blogs, textbooks on the subject? Thanks.

by sga 17y ago

hack_edu 17y ago
Ben Fry's dissertation 'Computational Information Design' [http://benfry.com/phd/ http://benfry.com/phd/] is a great start. He breaks down many of the skills needed to succeed at information design and processing. He's recently released Mastering Data with O'Reilly, which is essentially an expanded second addition to his dissertation.
- evgen 17y ago
  I think you mean Visualizing Data and not Mastering Data.
keefe 17y ago
first, you need to grok the basics http://academicearth.org/courses/machine-learning http://academicearth.org/courses/machine-learning but data mining can't be learned from a passive standpoint. You need to find a large dataset http://aws.amazon.com/publicdatasets/ http://aws.amazon.com/publicdatasets/ and try to do something with it. I've been pleased with http://neuroph.sourceforge.net/ http://neuroph.sourceforge.net/ for a lot of the stuff I do.
elai 17y ago
Data mining is a lot of statistical work and machine learning, so if your stats knowledge is rusty, basic or non-existent, then I would suggest you read up on that.
aaronblohowiak 17y ago
SCPD at Stanford offers a certificate program in data mining http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=1209602#searchResults http://scpd.stanford.edu/public/category/courseCategoryCerti... (if the above is broken, http://tinyurl.com/stanford-graduate-certs http://tinyurl.com/stanford-graduate-certs )
paraschopra 17y ago
I'd recommend you to start with data visualization, intute how mean, median, variation don't capture the data, then move onto normal distribution approximation of dataset, then linear regression and finally bayesian classification/probability models. Only after you get a good grasp on basics should you move to advanced topics such as neural networks, SVMs, etc.

Any thoughts on the best resources for learning data mining ie. can anyone suggest must read sites, blogs, textbooks on the subject? Thanks.

by sga 17y ago

Any thoughts on the best resources for learning data mining ie. can anyone suggest must read sites, blogs, textbooks on the subject? Thanks.

hack_edu 17y ago

Ben Fry's dissertation 'Computational Information Design' [http://benfry.com/phd/ http://benfry.com/phd/] is a great start. He breaks down many of the skills needed to succeed at information design and processing. He's recently released Mastering Data with O'Reilly, which is essentially an expanded second addition to his dissertation.

evgen 17y ago
I think you mean Visualizing Data and not Mastering Data.

keefe 17y ago

first, you need to grok the basics http://academicearth.org/courses/machine-learning http://academicearth.org/courses/machine-learning but data mining can't be learned from a passive standpoint. You need to find a large dataset http://aws.amazon.com/publicdatasets/ http://aws.amazon.com/publicdatasets/ and try to do something with it. I've been pleased with http://neuroph.sourceforge.net/ http://neuroph.sourceforge.net/ for a lot of the stuff I do.

elai 17y ago

Data mining is a lot of statistical work and machine learning, so if your stats knowledge is rusty, basic or non-existent, then I would suggest you read up on that.

aaronblohowiak 17y ago

SCPD at Stanford offers a certificate program in data mining http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=1209602#searchResults http://scpd.stanford.edu/public/category/courseCategoryCerti... (if the above is broken, http://tinyurl.com/stanford-graduate-certs http://tinyurl.com/stanford-graduate-certs )

paraschopra 17y ago

I'd recommend you to start with data visualization, intute how mean, median, variation don't capture the data, then move onto normal distribution approximation of dataset, then linear regression and finally bayesian classification/probability models. Only after you get a good grasp on basics should you move to advanced topics such as neural networks, SVMs, etc.