Synopses & Reviews
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
Synopsis
Today, successful firms win by understanding their data more deeply than competitors do.
They compete based on analytics. In
Modeling Techniques in Predictive Analytics, the Python edition
, the leader of Northwestern University's prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics.
Thomas W. Miller's balanced approach combines business context
and quantitative tools, appealing to managers, analysts, programmers, and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, Web and text analytics, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains:
- Why the problem is significant
- What data is relevant
- How to explore your data
- How to model your data -- first conceptually, with words and figures; and then with mathematics and programs
Miller walks through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. Extensive example code is presented in Python, a new and extremely popular language for applied statistics, statistical research, and predictive modeling; all code is set apart from other text so it's easy to find for those who want it (and easy to skip for those who don't).
Synopsis
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
Synopsis
Compete on analytics: win by understanding your data more deeply than your competitors do! In Modeling Techniques in Predictive Analytics, the Python edition , the leader of Northwestern University’s prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics.
Thomas W. Miller’s balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, Web and text analytics, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains:
- Why the problem is significant
- What data is relevant
- How to explore your data
- How to model your data – first conceptually, with words and figures; and then with mathematics and programs
Miller walks through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. Extensive example code is presented in Python, a new and extremely popular language for applied statistics, statistical research, and predictive modeling; all code is set apart from other text so it’s easy to find if you want it (and easy to skip if you don’t).
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.
Table of Contents
Preface v
1 Analytics and Data Science 1
2 Advertising and Promotion 16
3 Preference and Choice 33
4 Market Basket Analysis 43
5 Economic Data Analysis 61
6 Operations Management 81
7 Text Analytics 103
8 Sentiment Analysis 1 35
9 Sports Analytics 187
10 Spatial Data Analysis 211
11 Brand and Price 239
12 The Big Little Data Game 273
A Data Science Methods 277
A.1 Databases and Data Preparation 279
A.2 Classical and Bayesian Statistics 281
A.3 Regression and Classification 284
A.4 Machine Learning 289
A.5 Web and Social Network Analysis 291
A.6 Recommender Systems 293
A.7 Product Positioning 295
A.8 Market Segmentation 297
A.9 Site Selection 299
A.10 Financial Data Science 300
B Measurement 301
C Case Studies 315
C.1 Return of the Bobbleheads 315
C.2 DriveTime Sedans 316
C.3 Two Month’s Salary 321
C.4 Wisconsin Dells 325
C.5 Computer Choice Study 330
D Code and Utilities 335
Bibliography 379
Index 413