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 8
Forecasting: Breathe Easy, You Can't Win

As you saw in Chapters 3, 6 and 7, supervised machine learning is about predicting a value or classifying an observation using a model trained on past data. Forecasting is similar. Sure, you can forecast without data (astrology, anyone?). But in quantitative forecasting, past data is used to predict a future outcome. Indeed, some of the same techniques, such as multiple regression (introduced in Chapter 6), are used in both disciplines.

But where forecasting and supervised machine learning differ greatly is in their canonical problem spaces. Typical forecasting problems are about taking some data point over time (sales, demand, supply, GDP, carbon emissions, or population, for example) and projecting that data into the future. And in the presence of trends, cycles, and the occasional act of God, the future data can be wildly outside the bounds of the observed past.

And that's the problem with forecasting: unlike in Chapters...