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

Chapter 5
Cluster Analysis Part II: Network Graphs and Community Detection

This chapter continues the discussion on cluster identification and analysis using the wholesale wine dataset from Chapter 2. Although it's perfectly fine to jump around in this book, in this case I recommend at least skimming Chapter 2 before reading this chapter, because I don't repeat the data preparation steps, and you're going to be using cosine similarity, which was discussed at the end of Chapter 2.

Also, the techniques used here rely on the “Big M” constraint optimization techniques introduced in Chapter 4, so some familiarity with that will be helpful.

This chapter continues addressing the problem of detecting interesting groups of customers based on their purchases, but it approaches the problem from a fundamentally different direction.

Rather than thinking about customers huddling around flags planted on the dance floor to assign them to groups, as you did with k-means clustering...