The Field Guide to Data Science

The Field Guide to Data Science by Booz Allen Hamilton.

From “The Story of the Field Guide:”

While there are countless industry and academic publications describing what Data Science is and why we should care, little information is available to explain how to make use of data as a resource. At Booz Allen, we built an industry-leading team of Data Scientists. Over the course of hundreds of analytic challenges for dozens of clients, we’ve unraveled the DNA of Data Science. We mapped the Data Science DNA to unravel the what, the why, the who and the how.

Many people have put forth their thoughts on single aspects of Data Science. We believe we can offer a broad perspective on the conceptual models, tradecraft, processes and culture of Data Science. Companies with strong Data Science teams often focus
on a single class of problems – graph algorithms for social network analysis and recommender models for online shopping are two notable examples. Booz Allen is different. In our role as consultants, we support a diverse set of clients across a variety of domains. This allows us to uniquely understand the DNA of Data Science. Our goal in creating The Field Guide to Data Science is to capture what we have learned and to share it broadly. We want this effort to help drive forward the science and art of Data Science.

This is a great example of what can be done with authors, professional editors and graphic artists putting together a publication.

While it is just a “field guide,” it has enough depth to use it as a starting point for exploring data science projects.

Imagine that you have senior staff who have read and have a grasp of the field guide. I can easily imagine taking the appropriate parts of the field guide to serve as “windows” onto further steps for a particular project. Which would enable senior staff to remain grounded in what they understand and how further steps related back to that understanding. Overall I think this is an excellent field guide/introduction to data science.

BTW, the “Analytic Connection in the Data Lake” graphic on page 28 is similar to topic maps pointing into an infoverse.

I first saw this in a tweet by Kirk Borne.


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