Includes bibliographical references and index.

1. Introduction to big data analytics -- 2. Data analytics lifecycle -- 3. Review of basic data analytic methods using R -- 4. Advanced analytical theory and methods: clustering -- 5. Advanced analytical theory and methods: association rules -- 6. Advanced analytical theory and methods: regression -- 7. Advanced analytical theory and methods: classification -- 8. Advanced analytical theory and methods: time series analysis -- 9. Advanced analytical theory and methods: text analysis -- 10. Advanced analytics, technology and tools: MapReduce and Hadoop -- 11. Advanced analytics, technology and tools: in-database analytics -- 12. The endgame, or putting it all together.

This is a book about harnessing the power of data for new insights and covers the breadth of activities and methods and tools that data scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. It will help you become a contributor on a data science team; deploy a structured lifecycle approach to data analytics problems; apply appropriate analytic techniques and tools to analyzing big data; learn how to tell a compelling story with data to drive business action; prepare for EMC Proven Professional data science certification. -- Edited summary from book.

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