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410 Lincoln Road, Miami Beach, Florida

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Rebecca

2013-06-06

O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA95472.

First Edition

Mike Loukides

We used Non-negative Matrix Factorization to perform our Skills andSelf-ID clusterings. NMF attempts to find a matrix factorization whereall elements of the basis vectors are constrained to be non-negative.This is natural in data sets such as our skills rankings, which rangefrom 0 (lowest or missing) to 21 (highest).The R NMF package that we used is not currently available via CRAN,but can be downloaded from the archives.We used the standard Brunet et al. (2004) method, which attempts tominimize KL-divergence. Note that NMF attempts to globally opti‐mize a non-smooth function from a random initial state, and so weused 200 random runs to find a relatively reliable factorization. (Seemain text for several skills/self-ID terms that sometimes fell into othergroups when different random seeds were chosen. These small dif‐ferences did not appreciably affect our overall results.) The ranks of 5and 4 (for Skills and Self-ID, respectively) were chosen to maximizethe informativeness and interpretability (evaluated subjectively) of theresulting basis vectors. Lower ranks yielded vague factors, while higherranks yielded less informative results compared to the raw ranks/ratings.

978-1-449-37176-0

Sean Patrick Murphy

Marck Vaisman

Harlan D. Harris

There are two related issues that we have seen when it comes to mis‐understandings about the roles of data scientists. In one case, excessivehype leads people to expect miracles, and miracle-workers. In the oth‐er case, a lack of awareness about the variety of data scientists leadsorganizations to waste effort when trying to find talent. These casestudies are based on collective experiences from many of our friends,colleagues, and Meetup members.

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