Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics) 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
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
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.
- Publisher : Pearson FT Press; 1st edition (October 1, 2014)
- Language : English
- Hardcover : 448 pages
- ISBN-10 : 0133892069
- ISBN-13 : 978-0133892062
- Item Weight : 1.99 pounds
- Dimensions : 7.4 x 1.35 x 9.55 inches
- Best Sellers Rank: #1,045,802 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
There was a problem filtering reviews right now. Please try again later.
This book isn't for studying, it's for satisfying some ignorant professors' requirement that you buy a book for them to ask irrelevant questions on a test or cherry-pick some random study to be referenced in class.
I'm returning the book as it's written with such a callous disregard for academic rigor and has no real-world value, other than firestarter. I wish I never had to spend my time to buy it, waste amazons employee time to package it and gas to deliver such a flaming piece of trash that jumps on the "me too" bandwagon of "write a book about analytics" It like he book was written to make you less intelligent by ignoring the subject matter details they say they will cover and instead the author thinks his view on everything is more important that details of the subject his is covering.
Also, its misleading in many ways . The book says `Python Edition` - and then the author uses Python wrapper scripts to call R. That's not what I thought it would mean by `Python edition`. I regret this purchase.
Programs include extensive comments, as well as suggestions for students.
Top reviews from other countries
for Data Science, Kindled Edition) was advertised. Is this not an act of syphoning people's money? I don't kind Amazon read the individual review of their service, especially those that rate them low.