Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.

This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

• A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
• A new section on ethical issues in data mining
• Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
• More than a dozen case studies demonstrating applications for the data mining techniques described
• End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
• A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”

―Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R 

Table of Contents

PART I PRELIMINARIES

CHAPTER 1 Introduction

CHAPTER 2 Overview of the Data Mining Process

PART II DATA EXPLORATION AND DIMENSION REDUCTION

CHAPTER 3 Data Visualization

CHAPTER 4 Dimension Reduction

PART Ill PERFORMANCE EVALUATION

CHAPTER 5 Evaluating Predictive Performance

PART IV PREDICTION AND CLASSIFICATION METHODS

CHAPTER 6 Multiple Linear Regression

CHAPTER 7 k-Nearest Neighbors (kNN)

CHAPTER 8 The Naive Bayes Classifier

CHAPTER 9 Classification and Regression Trees

CHAPTER 10 Logistic Regression

CHAPTER 11 Neural Nets

CHAPTER 12 Discriminant Analysis

CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling

PART V MINING RELATIONSHIPS AMONG RECORDS

CHAPTER 14 Association Rules and Collaborative Filtering

CHAPTER 15 Cluster Analysis

PART VI FORECASTING TIME SERIES

CHAPTER 16 Handling Time Series

CHAPTER 17 Regression-Based Forecasting

CHAPTER 18 Smoothing Methods

PART VII DATA ANALYTICS

CHAPTER 19 Social Network Analytics

CHAPTER 20 Text Mining

PART VIII CASES

CHAPTER 21 Cases

About the Authors

GALIT SHMUELI, PHD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 100 publications including books.

PETER C. BRUCE is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O'Reilly).

PETER GEDECK, PHD, is a Senior Data Scientist at Collaborative Drug Discovery, where he helps develop cloud-based software to manage the huge amount of data involved in the drug discovery process. He also teaches data mining at Statistics.com.

NITIN R. PATEL, PhD, is cofounder and board member of Cytel Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.