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

Let's Get Going

In the first chapter, I'm going to fill in a few holes in your Excel knowledge. After that, you'll move right into use cases. By the end of this book, you'll not only know about but actually have experience implementing from scratch the following techniques:

  • Optimization using linear and integer programming
  • Working with time series data, detecting trends and seasonal patterns, and forecasting with exponential smoothing
  • Using Monte Carlo simulation in optimization and forecasting scenarios to quantify and address risk
  • Artificial intelligence using the general linear model, logistic link functions, ensemble methods, and naïve Bayes
  • Measuring distances between customers using cosine similarity, creating kNN graphs, calculating modularity, and clustering customers
  • Detecting outliers in a single dimension with Tukey fences or in multiple dimensions with local outlier factors
  • Using R packages to “stand on the shoulders” of other analysts...