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

Wrapping Up

Okay, this was a fast and furious run-through of some of what you can do in R merely by understanding three things:

  • Loading and working with data in R
  • Finding and installing relevant packages
  • Calling functions from those packages on your dataset

Is this all you need to know how to do in R? Nope. I didn't cover writing your own functions, a whole lot of plotting, connecting to databases, the slew of apply() functions available, and so on. But I hope this has given you a taste to learn more. If it has, there are scads of R books out there worth reading as a follow-up to this chapter. Here are a few:

  • Beginning R: The Statistical Programming Language by Mark Gardener (John Wiley & Sons, 2012)
  • R in a Nutshell, 2nd Edition by Joseph Adler (O'Reilly, 2012)
  • Data Mining with R: Learning with Case Studies by Luis Torgo (Chapman and Hall, 2010)
  • Machine Learning for Hackers by Drew Conway and John Myles White (O'Reilly, 2012)

Go forth and tinker in R!