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Analytics In A Big Data World: The Essential Guide To Data Science And Its Applications

Analytics In A Big Data World: The Essential Guide To Data Science And Its Applications


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About the Book

The book discusses the topic of Big Data and Analytics, which is now more relevant and actual than ever before. It is written with a strong practitioner focus, not overly stressing the mathematical underpinnings but the business application instead. It consists of real-life examples from the author's personal consulting and research experience (banking, retailing, government). The book includes an overview of visionary ideas and recent developments in the field with a tremendous potential to create strategic leverage in any business setting.

About the Author

Bart Baesens is an associate professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g., Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, and the Journal of Machine Learning Research) and presented at international conferences. He regularly advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.



Table of Contents:
Preface Acknowledgments Chapter 1 Big Data and Analytics Example Applications Basic Nomenclature Analytics Process Model Job Profiles Involved Analytics Analytical 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 Data Quality Software Privacy Model Design and Documentation Corporate Governance 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


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Product Details
  • ISBN-13: 9788126550890
  • Publisher: Wiley India Pvt Ltd
  • Binding: Hardback
  • No of Pages: 252
  • ISBN-10: 8126550899
  • Publisher Date: 2014
  • Language: English


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