By Gordon S. Linoff
ISBN-10: 0470099518
ISBN-13: 9780470099513
Worthy company research calls for you to successfully rework information into actionable details. This ebook is helping you employ SQL and Excel to extract company info from relational databases and use that info to outline company dimensions, shop transactions approximately shoppers, produce effects, and extra. every one bankruptcy explains while and why to accomplish a selected form of enterprise research with the intention to receive precious effects, tips to layout and practice the research utilizing SQL and Excel, and what the consequences may still seem like.
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However, making mistakes on columns where NULL values are present is easy. WA R N I N G Designing databases is different from analyzing the data inside them. For example, NULL columns can cause unexpected — and inaccurate — results when analyzing data and make reading queries difficult. Be very careful when using columns that allow them. qxd:WileyRed 6 8/27/07 Chapter 1 ■ 9:14 PM Page 6 A Data Miner Looks at SQL Column Types The second important attribute of a column is its type, which tells the database exactly how to store values.
The results of even the most sophisticated techniques are only as good as the data being used. Data is central to the task of understanding customers, understanding products, and understanding markets. The chapters in this book discuss different aspects of data and several different analytic techniques. The analytic techniques range from exploratory data analysis to survival analysis, from market basket analysis to naïve Bayesian models, from simple animations to regression. Of course, the potential range of possible techniques is much larger than can be presented in one book.
It continues by explaining how to put these pieces together into a forecast. Chapter 7 extends the discussion in three different areas. First, it addresses a key problem in many customer-oriented databases, called left truncation. Second, it explains a very interesting idea in survival analysis called competing risks. This idea incorporates the fact that customers leave for different reasons. The third idea is to use survival analysis for before-and-after analysis. That is, how can we quantify what happens to customers when something happens during their lifetime — such as quantifying the effect of enrollment in a loyalty program or of a major billing fiasco.
Data Analysis Using SQL and Excel by Gordon S. Linoff
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