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Data Science for Business by Foster Provost, Tom Fawcett

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Appendix A. Proposal Review Guide

Effective data analytic thinking should allow you to assess potential data mining projects systematically. The material in this book should give you the necessary background to assess proposed data mining projects, and to uncover potential flaws in proposals. This skill can be applied both as a self-assessment for your own proposals and as an aid in evaluating proposals from internal data science teams or external consultants.

What follows contains a set of questions that one should have in mind when considering a data mining project. The questions are framed by the data mining process discussed in detail in Chapter 2, and used as a conceptual framework throughout the book. After reading this book, you should be able to apply these conceptually to a new business problem. The list that follows is not meant to be exhaustive (in general, the book isn’t meant to be exhaustive). However, the list contains a selection of some of the most important questions to ask.

Throughout the book we have concentrated on data science projects where the focus is to mine some regularities, patterns, or models from the data. The proposal review guide reflects this. There may be data science projects in an organization where these regularities are not so explicitly defined. For example, many data visualization projects initially do not have crisply defined objectives for modeling. Nevertheless, the data mining process can help to structure data-analytic thinking about such ...

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