Blog Archives

A bit of the agenda of Practical Data Science with R

May 1, 2014
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A bit of the agenda of Practical Data Science with R

The goal of Zumel/Mount: Practical Data Science with R is to teach, through guided practice, the skills of a data scientist. We define a data scientist as the person who organizes client input, data, infrastructure, statistics, mathematics and machine learning to deploy useful predictive models into production. Our plan to teach is to: Order the Related posts:

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Old tails: a crude power law fit on ebook sales

April 18, 2014
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Old tails: a crude power law fit on ebook sales

We use R to take a very brief look at the distribution of e-book sales on Amazon.com. Recently Hugh Howey shared some eBook sales data spidered from Amazon.com: The 50k Report. The data is largely a single scrape of statistics about various anonymized books. Howey’s analysis tries to break sales down by declared category and Related posts:

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You don’t need to understand pointers to program using R

April 1, 2014
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You don’t need to understand pointers to program using R

R is a statistical analysis package based on writing short scripts or programs (versus being based on GUIs like spreadsheets or directed workflow editors). I say “writing short scripts” because R’s programming language (itself called S) is a bit of an oddity that you really wouldn’t be using except it gives you access to superior Related posts:

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Some statistics about the book

March 4, 2014
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Some statistics about the book

The release date for Zumel, Mount “Practical Data Science with R” is getting close. I thought I would share a few statistics about what goes into this kind of book. “Practical Data Science with R” started formal work in October of 2012. We had always felt the Win-Vector blog represented practice and research for such Related posts:

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One day discount on Practical Data Science with R

February 21, 2014
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One day discount on Practical Data Science with R

Please forward and share this discount offer for our upcoming book. Manning Deal of the Day February 22: Half off Practical Data Science with R. Use code dotd022214au at www.manning.com/zumel/. Related posts: Data Science, Machine Learning, and Statis...

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The gap between data mining and predictive models

February 20, 2014
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The gap between data mining and predictive models

The Facebook data science blog shared some fun data explorations this Valentine’s Day in Carlos Greg Diuk’s “The Formation of Love”. They are rightly receiving positive interest in and positive reviews of their work (for example Robinson Meyer’s Atlantic article). The finding is also a great opportunity to discuss the gap between cool data mining Related posts:

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Unprincipled Component Analysis

February 10, 2014
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Unprincipled Component Analysis

As a data scientist I have seen variations of principal component analysis and factor analysis so often blindly misapplied and abused that I have come to think of the technique as unprincipled component analysis. PCA is a good technique often used to reduce sensitivity to overfitting. But this stated design intent leads many to (falsely) Related posts:

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Bad Bayes: an example of why you need hold-out testing

February 1, 2014
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Bad Bayes: an example of why you need hold-out testing

We demonstrate a dataset that causes many good machine learning algorithms to horribly overfit. The example is designed to imitate a common situation found in predictive analytic natural language processing. In this type of application you are often building a model using many rare text features. The rare text features are often nearly unique k-grams Related posts:

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Use standard deviation (not mad about MAD)

January 19, 2014
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Use standard deviation (not mad about MAD)

Nassim Nicholas Taleb recently wrote an article advocating the abandonment of the use of standard deviation and advocating the use of mean absolute deviation. Mean absolute deviation is indeed an interesting and useful measure- but there is a reason that standard deviation is important even if you do not like it: it prefers models that Related posts:

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Generalized linear models for predicting rates

January 1, 2014
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Generalized linear models for predicting rates

I often need to build a predictive model that estimates rates. The example of our age is: ad click through rates (how often a viewer clicks on an ad estimated as a function of the features of the ad and the viewer). Another timely example is estimating default rates of mortgages or credit cards. You Related posts:

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