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Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (Wiley and SAS Business Series)

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en Limba Engleză Hardback – July 2014

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.

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Specificații

ISBN-13: 9781118892701
ISBN-10: 1118892704
Pagini: 256
Ilustrații: illustrations (black and white)
Dimensiuni: 163 x 243 x 21 mm
Greutate: 0.52 kg
Editura: Wiley
Seria Wiley and SAS Business Series

Locul publicării: Hoboken, United States

Public țintă

CMO, CIO, Chief Analytics Officer, Data analysts

Textul de pe ultima copertă

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.


Cuprins

Preface xiii
Acknowledgments xv
Chapter 1 Big Data and Analytics 1
Example Applications 2
Basic Nomenclature 4
Analytics Process Model 4
Job Profiles Involved 6
Analytics 7
Analytical Model Requirements 9
Notes 10
Chapter 2 Data Collection, Sampling, and Preprocessing 13
Types of Data Sources 13
Sampling 15
Types of Data Elements 17
Visual Data Exploration and Exploratory Statistical Analysis 17
Missing Values 19
Outlier Detection and Treatment 20
Standardizing Data 24
Categorization 24
Weights of Evidence Coding 28
Variable Selection 29
Segmentation 32
Notes 33
Chapter 3 Predictive Analytics 35
Target Definition 35
Linear Regression 38
Logistic Regression 39
Decision Trees 42
Neural Networks 48
Support Vector Machines 58
Ensemble Methods 64
Multiclass Classification Techniques 67
Evaluating Predictive Models 71
Notes 84
Chapter 4 Descriptive Analytics 87
Association Rules 87
Sequence Rules 94
Segmentation 95
Notes 104
Chapter 5 Survival Analysis 105
Survival Analysis Measurements 106
Kaplan Meier Analysis 109
Parametric Survival Analysis 111
Proportional Hazards Regression 114
Extensions of Survival Analysis Models 116
Evaluating Survival Analysis Models 117
Notes 117
Chapter 6 Social Network Analytics 119
Social Network Definitions 119
Social Network Metrics 121
Social Network Learning 123
Relational Neighbor Classifier 124
Probabilistic Relational Neighbor Classifier 125
Relational Logistic Regression 126
Collective Inferencing 128
Egonets 129
Bigraphs 130
Notes 132
Chapter 7 Analytics: Putting It All to Work 133
Backtesting Analytical Models 134
Benchmarking 146
Data Quality 149
Software 153
Privacy 155
Model Design and Documentation 158
Corporate Governance 159
Notes 159
Chapter 8 Example Applications 161
Credit Risk Modeling 161
Fraud Detection 165
Net Lift Response Modeling 168
Churn Prediction 172
Recommender Systems 176
Web Analytics 185
Social Media Analytics 195
Business Process Analytics 204
Notes 220
About the Author 223
Index 225


Notă biografică

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).