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Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

Book Description

With big data analytics comes big insights into profitability

Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.

With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes:

  • A complete overview of big data and its notable characteristics

  • Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases

  • Comprehensive coverage of data mining, text analytics, and machine learning algorithms

  • A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes

  • Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

    Table of Contents

    1. Foreword
    2. Preface
      1. Notes
    3. Acknowledgments
    4. Introduction
      1. Big Data Timeline
      2. Why This Topic Is Relevant Now
      3. Is Big Data a Fad?
      4. Where Using Big Data Makes a Big Difference
      5. Notes
    5. Part One The Computing Environment
      1. Chapter 1 Hardware
        1. Storage (Disk)
        2. Central Processing Unit
        3. Memory
        4. Network
        5. Notes
      2. Chapter 2 Distributed Systems
        1. Database Computing
        2. File System Computing
        3. Considerations
        4. Notes
      3. Chapter 3 Analytical Tools
        1. Weka
        2. Java and JVM Languages
        3. R
        4. Python
        5. SAS
        6. Notes
    6. Part Two Turning Data into Business Value
      1. Chapter 4 Predictive Modeling
        1. A Methodology for Building Models
        2. sEMMA
        3. Binary Classification
        4. Multilevel Classification
        5. Interval Prediction
        6. Assessment of Predictive Models
        7. Notes
      2. Chapter 5 Common Predictive Modeling Techniques
        1. RFM
        2. Regression
        3. Generalized Linear Models
        4. Neural Networks
        5. Decision and Regression Trees
        6. Support Vector Machines
        7. Bayesian Methods Network Classification
        8. Ensemble Methods
        9. Notes
      3. Chapter 6 Segmentation
        1. Cluster Analysis
        2. Distance Measures (Metrics)
        3. Evaluating Clustering
        4. Number of Clusters
        5. K-means Algorithm
        6. Hierarchical Clustering
        7. Profiling Clusters
        8. Notes
      4. Chapter 7 Incremental Response Modeling
        1. Building the Response Model
        2. Measuring the Incremental Response
      5. Chapter 8 Time Series Data Mining
        1. Reducing Dimensionality
        2. Detecting Patterns
        3. Time Series Data Mining in Action: Nike+ FuelBand
        4. Notes
      6. Chapter 9 Recommendation Systems
        1. What Are Recommendation Systems?
        2. Where Are They Used?
        3. How Do They Work?
        4. Assessing Recommendation Quality
        5. Recommendations in Action: SAS Library
        6. Notes
      7. Chapter 10 Text Analytics
        1. Information Retrieval
        2. Content Categorization
        3. Text Mining
        4. Text Analytics in Action: Let’s Play Jeopardy!
        5. Notes
    7. Part Three Success Stories of Putting It All Together
      1. Qualities of Successful Projects
      2. Chapter 11 Case Study of a Large U.S.-Based Financial Services Company
        1. Traditional Marketing Campaign Process
        2. High-Performance Marketing Solution
        3. Value Proposition for Change
      3. Chapter 12 Case Study of a Major Health Care Provider
        1. CAHPS
        2. HEDIS
        3. HOS
        4. IRE
      4. Chapter 13 Case Study of a Technology Manufacturer
        1. Finding Defective Devices
        2. How They Reduced Cost
      5. Chapter 14 Case Study of Online Brand Management
      6. Chapter 15 Case Study of Mobile Application Recommendations
      7. Chapter 16 Case Study of a High-Tech Product Manufacturer
        1. Handling the Missing Data
        2. Application beyond Manufacturing
      8. Chapter 17 Looking to the Future
        1. Reproducible Research
        2. Privacy with Public Data Sets
        3. The Internet of Things
        4. Software Development in the Future
        5. Future Development of Algorithms
        6. In Conclusion
        7. Notes
    8. About the Author
    9. Appendix Nike+ Fuelband Script to Retrieve Information
    10. References
      1. Introduction
      2. Chapter 2
      3. Chapter 3
      4. Chapter 4
      5. Chapter 5
      6. Chapter 6
      7. Chapter 7
      8. Chapter 8
      9. Chapter 9
      10. Chapter 10
      11. Chapter 17
    11. Index
    12. End User License Agreement