Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science
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:"
Table of Content
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
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Salient Features
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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