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Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science, 1/e


Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science, 1/e
Author(s)  Thomas W. Miller
ISBN  9789353065737
Imprint  Pearson Education
Copyright  2019
Pages  448
Binding  Paperback
List Price  Rs. 735.00
  
 
 

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:"

  • About the Author
  • Contents
  • Features
  • Downloadable Resources

 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.

 

 Preface     v


1  Analytics and Data Science     


2  Advertising and Promotion     


3  Preference and Choice     


4  Market Basket Analysis     


5  Economic Data Analysis     


6  Operations Management     


7  Text Analytics     


8  Sentiment Analysis 1   


9  Sports Analytics     


10  Spatial Data Analysis     


11  Brand and Price     


12  The Big Little Data Game     


A  Data Science Methods     


  A.1 Databases and Data Preparation     


  A.2 Classical and Bayesian Statistics     


  A.3 Regression and Classification     


  A.4 Machine Learning     


  A.5 Web and Social Network Analysis     


  A.6 Recommender Systems     


  A.7 Product Positioning     


  A.8 Market Segmentation     


  A.9 Site Selection     


  A.10 Financial Data Science     


B  Measurement     


C  Case Studies     


  C.1 Return of the Bobbleheads     


  C.2 DriveTime Sedans     


  C.3 Two Month's Salary     


  C.4 Wisconsin Dells     


  C.5 Computer Choice Study     


D  Code and Utilities     


Bibliography     


Index     
 

 Today's definitive, comprehensive guide to using predictive analytics to overcome business challenges - now updated and reorganized for more effective learning!


Teaches modeling techniques conceptually, with words and figures - and then mathematically, with the powerful Python language


Restructured standalone chapters provide fast access to all the knowledge you need to solve any category of problem


Covers segmentation, brand positioning, product choice modeling, pricing, finance, sports analytics, Web/text analytics, social network analysis, and more


Helps you leverage traditional techniques, machine learning, data visualization, and statistical graphics


Designed for wide applicability and ease of use: requires no linear algebra or advanced math


Contains updated source material throughout


Now leads directly into Pearson's pioneering Data Science Series: cutting-edge texts on advanced modeling for business managers, modelers, and programmers alike"
 
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