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Master predictive analytics, from start to finish
Start with strategy and management
Master methods and build models
Transform your models into highly-effective code—in both Python and R
This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math.
Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value.
Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code.
If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more.
All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/
Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage.
Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have.
Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data.
You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights.
You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods.
Use Python and R to gain powerful, actionable, profitable insights about:
- Advertising and promotion
- Consumer preference and choice
- Market baskets and related purchases
- Economic forecasting
- Operations management
- Unstructured text and language
- Customer sentiment
- Brand and price
- Sports team performance
- And much more
- Sales Rank: #169861 in Books
- Published on: 2014-10-11
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.40" w x 7.40" l, .0 pounds
- Binding: Hardcover
- 448 pages
From the Back Cover
This uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will help you master crucial skills you don't yet have.
Unlike most books on predictive analytics, this guide illuminates the discipline through practical case studies, realistic vignettes, and intuitive data visualizations–not complex mathematics. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through every step: defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more.
Each chapter focuses on one of today’s most important applications for predictive analytics, giving you the skills and knowledge to put models to work–and gain maximum value from them.
About the Author
THOMAS W. MILLER is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. 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 co-founder and director of product development at ToutBay, a publisher and distributor of data science applications. 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 over 30 years. Miller’s books include Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.
Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin–Madison.
He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota, and an MBA and master’s degree in economics from the University of Oregon.
Most helpful customer reviews
56 of 59 people found the following review helpful.
More like a collection of magazine/newspaper articles than a book
By Prof Ed U. Cate
I purchased this book before I had a chance to read any sample chapter and was disappointed after I went through the book.
Every chapter is dedicated to an application of a particular model of predictive analytics, where a (more or less) real problem is described and discussed, name of a model to use is mentioned, chart outputs are shown and used for a conclusion. In very much the same format and content of an article that you would see in for example Bloomberg business magazine. There is no substantial discussion of any of the models, and without a good understanding of such models you cannot conduct predictive Analytics.
The content of this book could be used in the first 2-3 weeks of an introductory course in Analytics discussing what is Analytics and what are some example applications. I ended up keeping the book mostly due to hassle of a return, and partly for using it as a list of major models to read elsewhere and learn.
19 of 19 people found the following review helpful.
Good book for end-users
By JoeT
This is a good book on using R for predictive modeling.
The books website contains all the code that is used in the book.
I tried all of the downloadable R files and they all worked as advertised.
I admit not trying the text processing though (Chapter 7) only because I don't like R for text processing.
Rather use perl or Rapidminer.
Pros:
1. All the code works
2. A good sample space of topics, so you get a feel of predictive modeling in different situations.
3. You really don't need an extensive math background, since there is virtually no math described at all.
Cons:
1. If there was one thing I wish was better done is the analysis of the results. Some of the results, unless you are already familiar with the statistical technique used, might seem foreign and will require you to do some additional research.
Summary:
Overall a good book, minus the 1-Con above.
Hint: If you do download the R programs, go through each one a piece at a time, to see what's going on. I found it's better than just "running the code". You'll have a better understanding of what's going on.
10 of 11 people found the following review helpful.
Data/Code is available
By Shawn Mehan
Why people are whinging that they can't find the downloadable programs and data sets is beyond me. http://www.ftpress.com/promotions/modeling-techniques-in-predictive-analytics-139480
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