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
About the Author
About the Technical Editors
End User License Agreement

Who Are You?

No, I haven't been using data science to spy on you. I have no idea who you are, but thanks for shelling out some money for this book. Or supporting your local library. You can do that, too.

Here are some archetypes (or personas for you marketing folks) I had in mind when writing this book. Maybe you are:

  • The vice president of marketing who wants to use her transactional business data more strategically to price products and segment customers. But she doesn't understand the approaches her software developers and overpriced consultants are recommending she try.
  • The demand forecasting analyst who knows his organization's historical purchase data holds more insight about his customers than just the next quarter's projections. But he doesn't know how to extract that insight.
  • The CEO of an online retail start-up who wants to predict when a customer is likely to be interested in buying an item based on their past purchases.
  • The business intelligence analyst...