First, this is an excellent book. Why 4 stars? It presupposes a fair amount of knowledge of R, Python and the analytic techniques used for the data science approach of marketing analytics. Newbies will struggle with the concepts in this book. It dives right into the code, the techniques, and the statistical measures used in the first example of how Miller made the decision of which mobile company to use after a move based on 16 product profiles using linear models and conjoint analysis. (a brief explanation of the sum contrasts and fitted regression coefficient follows with code for R and Python). In many of the chapters, there are appendices that have additional descriptions of statistical techniques and references.That being said, this is an excellent reference for programmers, statisticians, modelers, marketers and data scientists. Props to Miller for calling out in the intro the definition of data science, "the new statistics, a blending of modeling techniques, information technology, and business savvy" while also reminding readers that it doesn't mean abandoning scientific research, surveys, inference, or primary data collection. Not all of the information that we need or want is available.The pros: readers familiar with how to program in R or Python will be able to follow along. All the sample code tested have been correct so far. Python scripts are notoriously problematic in books because of the spacing requirements, but all those tested have worked. Since it's focused on using Python and R (both of which are free and have robust user communities), people who are familiar with the analytic techniques but who want to learn to apply it can do so without having to pay for software.Miller also includes concepts of data visualizations (often done through R, which has a number of great packages he references), data base systems and data preparation, all of which are major components of being able to efficiently answer the question. Data curation is often a huge undertaking that non-database people misunderstand.To get a sense of the comprehensiveness of the appendices, in the marketing data source appendix B, Miller also describes options for sampling (simple random, convenience, probability samples, types of sampling frame, cluster sampling, adaptive sampling, on-line surveys, telephone surveys, random-digit dial, and discusses some of the pros and cons of each (with words, not formulas). He also discusses the concept of a panel sample and the accuracy of the panel in being able to generalize the responses back to the population. The one discussion area that is missed when he asks " How large a ample is needed to obtain useful information?" is a power calculation - looking at what is the smallest needed sample to detect a difference in what measure is being used (improvement on an indicator/survey question; differences between specific populations; or to determine how wide a standard error is around the point estimate to see  what a detectable difference would be.Overall, though, it's got a lot of great information. It's dense and takes time to work through the examples and code (especially for someone like me who is just an R and Python tinkerer without a ton of expertise in those languages), but is a good primer that presents the concepts in a neutral way, allowing the data scientist to make up their own mind about how to approach the situation.