Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science
©2015 |Pearson FT Press | Available
Thomas W. Miller
©2015 |Pearson FT Press | Available
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:
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).
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Today's definitive, comprehensive guide to using predictive analytics to overcome business challenges – now updated and reorganized for more effective learning!
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
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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.
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