The book provides nine tutorials on optimization, machine learning, data mining, and forecasting all within the confines of a spreadsheet. Each tutorial uses a real-world problem and the author guides the reader using query’s the reader might ask as how to craft a solution using the correct data science technique. Hosting these nine spreadsheets for download will be necessary so that the reader can work the problems along with the book.
Important topics covered by the book:
- Linear and integer programming
- K-nearest neighbors graphs and clustering
- Logistic regression
- Demand forecasting with seasonal adjustments
- Price sensitivity, revenue optimization, and price-sensitive forecasting
- Naïve Bayes classification
- Outlier detection using graphs and Local Outlier Factors
- Multi-criteria decision analysis
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Page Count (est.): 336
Pub Date: 10/28/2013