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Data Smart

Data Smart

By : John W. Foreman
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Data Smart

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)
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1
Cover
2
Credits
3
About the Author
4
About the Technical Editors
5
Acknowledgments
18
End User License Agreement

Multiplicative Holt-Winters Exponential Smoothing

Multiplicative Holt-Winters Smoothing is the logical extension of Holt's Trend-Corrected Smoothing. It accounts for a level, a trend, and the need to adjust the demand up or down on a regular basis due to seasonal fluctuations. Note that the seasonal fluctuation needn't be every 12 months like in this example. In the case of MailChimp, we have periodic demand fluctuations every Thursday (people seem to think Thursday is a good day to send marketing e-mail). Using Holt-Winters, we could account for this 7-day cycle.

Now, in most situations you can't just add or subtract a fixed amount of seasonal demand to adjust the forecast. If your business grows from selling 200 to 2,000 swords each month, you wouldn't adjust the Christmas demand in both those contexts by adding 20 swords. No, seasonal adjustments usually need to be multipliers. Instead of adding 20 swords maybe it's multiplying the forecast by 120 percent....

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Data Smart
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