Analytics in a Big Data World: The Essential Guide to Data Science and its Applications

Praise for Analytics in a Big Data World: The Essential Guide to Data Science and its Applications Just by continuously exploiting masses of data, companies like Google, Facebook, Uber, Waze, Zillow, etc. have been able to shake up traditional operating models and industries. Putting the required effort and investment in collecting and exploiting new sets of data is simply a must for competitive advantage. The good news is that today, thanks to the rapidly evolving field of technology, we can collect, store and analyze any type of data at lower cost and faster than ever. With this book, the author provides a unique blend of research and business insights into data science and/or analytics, making it a must read for anyone using these technologies to gain sustainable strategic leverage! Sabine Everaet, Europe CIO, The Coca–Cola Company Technology companies today, such as eBay, Amazon, and Facebook, touch large volumes of users and generate massive amounts of data, from transactional to behavioral. An understanding of how to extract value from these massive datasets is critical for all of these companies ability to compete for customers. Building upon his profound business expertise and knowledge, the author describes the real–world application of varied data science and analytical techniques that would serve as an excellent guide for analytics professionals as they attempt to use the insights residing in the stores of company data to drive decision–making in their organizations. Steve Metz, Senior Director, Global Customer Experience Finance/Analytics Collections, eBay Turn Big Data into Big Opportunities Where do we start? More and more businesses are asking this question as the need to strategically manage data intensifies. Analytics in a Big Data World addresses the seemingly Herculean task of coming to grips with multiple channels of data and sculpting them into quantifiable value. This book is for business professionals who want a focused, practical approach to big data analytics. Analytics researcher Bart Baesens focuses on case studies, real–world application, and steps for implementation, using theory and mathematical formulas only when necessary. The number of strategic applications for big data is constantly expanding. Analytics in a Big Data World provides an approach to data that can be used in customer relationship management, social media, risk management, and beyond. Past behavior can predict future trends so that you can react more effectively. Learn how to begin describing and predicting customers complex behavioral patterns, and find out how to apply your analysis in ways that have been proven to add value and target the bottom line. Big data sets are assets that can be leveraged quickly and inexpensively. As the science of analytics penetrates every industry in every sector, businesses that fail to use their data assets wisely could fall behind the competition. The flood of new information available to businesses has changed the rules of identifying new business opportunities. Analytics in a Big Data World will help you harness the innovations in data science and address the challenges involved in taming big data.

Preface Acknowledgments Chapter 1 Big Data and Analytics Example Applications Basic Nomenclature Analytics Process Model Job Profiles Involved Analytics Analytic Model Requirements Notes Chapter 2 Data Collection, Sampling, and Preprocessing Types of Data Sources Sampling Types of Data Elements Visual Data Exploration and Exploratory Statistical Analysis Missing Values Outlier Detection and Treatment Standardizing data Categorization Weights of Evidence coding Variable Selection Segmentation Notes Chapter 3 Predictive Analytics Target Definition Linear Regression Logistic Regression Decision Trees Neural Networks Support Vector Machines Ensemble Methods Multiclass Classification Techniques Evaluating Predictive Models Notes Chapter 4 Descriptive Analytics Association Rules Sequence Rules Segmentation Notes Chapter 5 Survival analysis Survival Analysis Measurements Kaplan Meier Analysis Parametric Survival Analysis Proportional Hazards Regression Extensions of Survival Analysis Models Evaluating Survival Analysis Models Notes Chapter 6 Social Network Analytics Social Network Definitions Social Network Metrics Social Network Learning Relational Neighbor Classifier Probabilistic Relational Neighbor Classifier Relational Logistic Regression Collective Inferencing Egonets Bigraphs Notes Chapter 7 Analytics: Putting it All to Work Backtesting Analytical Models Benchmarking Notes Chapter 8 Example applications Credit Risk Modeling Fraud Detection Net Lift Response modeling Churn Prediction Recommender systems Web Analytics Social Media Analytics Business Process Analytics Notes About the Author Index

BART BAESENS is an associate professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom), as well as an internationally known data analytics consultant. He is a foremost researcher in the areas of web analytics, customer relationship management, and fraud detection. His findings have been published in well–known international journals including Machine Learning and Management Science . Baesens is also co–author of the book Credit Risk Management: Basic Concepts (Oxford University Press, 2008).