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

Wrapping Up

This chapter covered all sorts of good stuff. To summarize, you looked at:

  • Euclidean distance
  • k-means clustering using Solver to optimize the centers
  • How to understand the clusters once you have them
  • How to calculate the silhouette of a given K-means run
  • K-medians clustering
  • Manhattan/Hamming distance
  • Cosine similarity and distance

If you made it through the chapter, you should feel confident not only about how to cluster data, but also which questions can be answered in business through clustering, and how to prepare your data to make it ready to cluster.

K-means clustering has been around for decades and is definitely the place to start for anyone looking to segment and pull insights from their customer data. But it's not the most “current” clustering technique. In Chapter 5, you'll explore using network graphs to find communities of customers within this same dataset. You'll even take a field trip outside of Excel, very briefly, to visualize...