Book Image

Data Smart

By : John W. Foreman
Book Image

Data Smart

By: John W. Foreman

Overview of this book

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, math and the magic, behind big data.
Table of Contents (18 chapters)
Free Chapter
1
Cover
2
Credits
3
About the Author
4
About the Technical Editors
5
Acknowledgments
18
End User License Agreement

A Workable Definition of Data Science

To an extent, data science is synonymous with or related to terms like business analytics, operations research, business intelligence, competitive intelligence, data analysis and modeling, and knowledge extraction (also called knowledge discovery in databases or KDD). It's just a new spin on something that people have been doing for a long time.

There's been a shift in technology since the heyday of those other terms. Advancements in hardware and software have made it easy and inexpensive to collect, store, and analyze large amounts of data whether that be sales and marketing data, HTTP requests from your website, customer support data, and so on. Small businesses and nonprofits can now engage in the kind of analytics that were previously the purview of large enterprises.

Of course, while data science is used as a catch-all buzzword for analytics today, data science is most often associated with data mining techniques such as artificial...