Big Data, Data Mining, and Machine Learning

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

Part One The Computing Environment
Chapter 1 Hardware
Chapter 2 Distributed Systems
Chapter 3 Analytical Tools

Part Two Turning Data into Business Value
Chapter 4 Predictive Modeling
Chapter 5 Common Predictive Modeling Techniques
Chapter 6 Segmentation
Chapter 7 Incremental Response Modeling
Chapter 8 Time Series Data Mining
Chapter 9 Recommendation Systems
Chapter 10 Text Analytics

Part Three Success Stories of Putting It All Together
Chapter 11 Case Study of a Large U.S.-Based Financial Services Company
Chapter 12 Case Study of a Major Health Care Provider
Chapter 13 Case Study of a Technology Manufacturer
Chapter 14 Case Study of Online Brand Management
Chapter 15 Case Study of Mobile Application Recommendations
Chapter 16 Case Study of a High-Tech Product Manufacturer
Chapter 17 Looking to the Future