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

How Much Is an Edge Worth? Points and Penalties in Graph Modularity

Pretend that I'm a customer hanging out in my graph, and I want to know who belongs in a community with me.

How about that lady who's connected to me by an edge? Maybe. Probably. We are connected after all.

How about the guy on the other side of the graph who shares no edge with me? Hmmm, it's much less likely.

Graph modularity quantifies this gut feeling that communities are defined by connections. The technique assigns scores to each pair of nodes. If two nodes aren't connected, I need to be penalized for putting them in a community. If two nodes are connected, I need to be rewarded. Whatever community assignment I make, the modularity of the graph is driven by the sum of those scores for each pair of nodes that ends up in a community together.

Using an optimization algorithm (you knew Solver was coming!), you can “try out” different community assignments on the graph and see which...