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

Congratulations! You just built a classification model in a spreadsheet. Two of them actually. Maybe even two and a half. And if you took me up on my median regression challenge, then you're a beast.

Let's recap some of the things we covered:

  • Feature selection and assembling training data, including creating dummy variables out of categorical predictors
  • Training a linear regression model by minimizing the sum of squared error
  • Calculating R-squared, showing a model is statistically significant using an F test, and showing model coefficients are individually significant using a t test
  • Evaluating model performance on a holdout set at various classification cutoff values by calculating precision, specificity, false positive rate, and recall
  • Graphing a ROC curve
  • Adding a logistic link function to a general linear model and reoptimizing
  • Maximizing likelihood in a logistic regression
  • Comparing models with the ROC curve

And while I'll be the first to admit that the...