Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)

C. He holds a Ph.D. He also directed the A. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets and has worked with predictive models for more than 30 years. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.Miller is owner of Research Publishers LLC and its ToutBay

Shanemeister said Great value. Very good book, well written, and the best pas, as with all of Miller's books that I have purchased, is that it comes with real code examples in both Python and R. Great way to get up and running.. richard said wot! no code samples on line?. good book, but.no data sets to work with. Seems critical for a source code heavy book (ie almost every chapter has pages of code). We would prefer not to scan, then try to run the code ourselves. Read the appendix first at that seems to be where the theory is then go back to the chapters for practical work. Borrowed this book from t. Good book, very well explained examples (the R and Good book, very well explained examples (the R and the Python codes are very well written) but if you have read other books from Prof. Miller, you would be able to remember some exacts paragraphs across some books.

Today, marketers must master a new data science and use it to uncover meaningful answers rapidly and inexpensively.This book teaches marketing data science through real-world examples that integrate essential knowledge from the disciplines that have shaped it. You’ll gain realistic experience extending predictive analytics with powerful techniques from web analytics, network science, programming, and marketing research. Using hands-on examples built with R, Python, and publicly available data sets, Thomas W. Miller walks you through the entire process of modeling and answering marketing questions with R and Python, today’s leading open source tools for data science.Using real data sets, Miller covers a full spectrum of marketing applications, from targeting new customers to improving r

Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Coverage includes:The role of analytics in delivering effective messages on the webUnderstanding the web by understanding its hidden structuresBeing recognized on the web – and watching your own competitorsVisualizing networks and understanding communities within themMeasuring sentiment and making recommendationsLeveraging key data science methods: databases/data preparation, classica