You are previewing Analytics in a Big Data World: The Essential Guide to Data Science and its Applications.
O'Reilly logo
Analytics in a Big Data World: The Essential Guide to Data Science and its Applications

Book Description

The guide to targeting and leveraging business opportunities using big data & analytics

By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments.

The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.

  • Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics

  • Offers the results of research and the author's personal experience in banking, retail, and government

  • Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business

  • Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis

  • For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

    Table of Contents

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