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

In Chapter 2, you looked at k-means clustering. Using the same data in this chapter, you tackled network graphs and clustering via modularity maximization. You should feel pretty good about your data mining chops by now. In more detail, here are some items you learned:

  • How network graphs are visually represented as well as how they're represented numerically using adjacency and affinity matrices
  • How to load a network graph into Gephi to augment Excel's visualization deficiencies
  • How to prune edges from network graphs via the r-neighborhood graph. You also learned the concept of a kNN graph, which I recommend you go back and tinker with.
  • The definitions of node degree and graph modularity and how to calculate modularity scores for grouping two nodes together
  • How to maximize graph modularity using a linear optimization model and divisive clustering
  • How to maximize graph modularity in Gephi and export the results

Now, you may be wondering, “John, why in the...