Introduction

Big Data is creating significant new opportunities for organizations to derive new value and create competitive advantage from their most valuable asset: information. For businesses, Big Data helps drive efficiency, quality, and personalized products and services, producing improved levels of customer satisfaction and profit. For scientific efforts, Big Data analytics enable new avenues of investigation with potentially richer results and deeper insights than previously available. In many cases, Big Data analytics integrate structured and unstructured data with real-time feeds and queries, opening new paths to innovation and insight.

This book provides a practitioner's approach to some of the key techniques and tools used in Big Data analytics. Knowledge of these methods will help people become active contributors to Big Data analytics projects. The book's content is designed to assist multiple stakeholders: business and data analysts looking to add Big Data analytics skills to their portfolio; database professionals and managers of business intelligence, analytics, or Big Data groups looking to enrich their analytic skills; and college graduates investigating data science as a career field.

The content is structured in twelve chapters. The first chapter introduces the reader to the domain of Big Data, the drivers for advanced analytics, and the role of the data scientist. The second chapter presents an analytic project lifecycle designed for the particular characteristics and challenges of hypothesis-driven analysis with Big Data.

Chapter 3 examines fundamental statistical techniques in the context of the open source R analytic software environment. This chapter also highlights the importance of exploratory data analysis via visualizations and reviews the key notions of hypothesis development and testing.

Chapters 4 through 9 discuss a range of advanced analytical methods, including clustering, classification, regression analysis, time series and text analysis.

Chapters 10 and 11 focus on specific technologies and tools that support advanced analytics with Big Data. In particular, the MapReduce paradigm and its instantiation in the Hadoop ecosystem, as well as advanced topics in SQL and in-database text analytics form the focus of these chapters.

Chapter 12 provides guidance on operationalizing Big Data analytics projects. This chapter focuses on creating the final deliverables, converting an analytics project to an ongoing asset of an organization's operation, and creating clear, useful visual outputs based on the data.

EMC Academic Alliance

University and college faculties are invited to join the Academic Alliance program to access unique “open” curriculum-based education on the following topics:

• Data Science and Big Data Analytics
• Information Storage and Management
• Cloud Infrastructure and Services
• Backup Recovery Systems and Architecture

The program provides faculty with course resources to prepare students for opportunities that exist in today's evolving IT industry at no cost. For more information, visit http://education.EMC.com/academicalliance.

EMC Proven Professional Certification

EMC Proven Professional is a leading education and certification program in the IT industry, providing comprehensive coverage of information storage technologies, virtualization, cloud computing, data science/Big Data analytics, and more.

Being proven means investing in yourself and formally validating your expertise.

This book prepares you for Data Science Associate (EMCDSA) certification. Visit http://education.EMC.com for details.