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Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science

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Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science

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  • About eBook Formats
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    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

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

Description

  • Copyright 2015
  • Dimensions: 7" x 9-1/4"
  • Pages: 448
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-389206-9
  • ISBN-13: 978-0-13-389206-2

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

Downloads

Downloads

Download individual code files by chapter.

Sample Content

Online Sample Chapter

Modeling Techniques in Predictive Analytics with Python and R: Analytics and Data Science

Sample Pages

Download the sample pages (includes Chapter 1 and Index)

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

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