Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
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David Dietrich heads the data science education team within EMC Education Services, where he leads the curriculum, strategy and course development related to Big Data Analytics and Data Science. He co-authored the first course in EMC's Data Science curriculum, two additional EMC courses focused on teaching leaders and executives about Big Data and data science, and is a contributing author and editor of this book. He has filed 14 patents in the areas of data science, data privacy, and cloud computing.
David has been an advisor to several universities looking to develop academic programs related to data analytics, and has been a frequent speaker at conferences and industry events. He also has been a a guest lecturer at universities in the Boston area. His work has been featured in major publications including Forbes, Harvard Business Review, and the 2014 Massachusetts Big Data Report, commissioned by Governor Deval Patrick.
Involved with analytics and technology for nearly 20 years, David has worked with many Fortune 500 companies over his career, holding multiple roles involving analytics, including managing analytics and operations teams, delivering analytic consulting engagements, managing a line of analytical software products for regulating the US banking industry, and developing Software-as-a-Service and BI-as-a-Service offerings. Additionally, David collaborated with the U.S. Federal Reserve in developing predictive models for monitoring mortgage portfolios.
Barry Heller is an advisory technical education consultant at EMC Education Services. Barry is a course developer and curriculum advisor in the emerging technology areas of Big Data and data science. Prior to his current role, Barry was a consultant research scientist leading numerous analytical initiatives within EMC's Total Customer Experience organization. Early in his EMC career, he managed the statistical engineering group as well as led the data warehousing efforts in an Enterprise Resource Planning (ERP) implementation. Prior to joining EMC, Barry held managerial and analytical roles in reliability engineering functions at medical diagnostic and technology companies. During his career, he has applied his quantitative skill set to a myriad of business applications in the Customer Service, Engineering, Manufacturing, Sales/Marketing, Finance, and Legal arenas. Underscoring the importance of strong executive stakeholder engagement, many of his successes have resulted from not only focusing on the technical details of an analysis, but on the decisions that will be resulting from the analysis. Barry earned a B.S. in Computational Mathematics from the Rochester Institute of Technology and an M.A. in Mathematics from the State University of New York (SUNY) New Paltz.
Beibei Yang is a Technical Education Consultant of EMC Education Services, responsible for developing several open courses at EMC related to Data Science and Big Data Analytics. Beibei has seven years of experience in the IT industry. Prior to EMC she worked as a software engineer, systems manager, and network manager for a Fortune 500 company where she introduced new technologies to improve efficiency and encourage collaboration. Beibei has published papers to prestigious conferences and has filed multiple patents. She received her Ph.D. in computer science from the University of Massachusetts Lowell. She has a passion toward natural language processing and data mining, especially using various tools and techniques to find hidden patterns and tell stories with data.
Data Science and Big Data Analytics is an exciting domain where the potential of digital information is maximized for making intelligent business decisions. We believe that this is an area that will attract a lot of talented students and professionals in the short, mid, and long term.
EMC Education Services embarked on learning this subject with the intent to develop an “open” curriculum and certification. It was a challenging journey at the time as not many understood what it would take to be a true data scientist. After initial research (and struggle), we were able to define what was needed and attract very talented professionals to work on the project. The course, “Data Science and Big Data Analytics,” has become well accepted across academia and the industry.
Led by EMC Education Services, this book is the result of efforts and contributions from a number of key EMC organizations and supported by the office of the CTO, IT, Global Services, and Engineering. Many sincere thanks to many key contributors and subject matter experts David Dietrich, Barry Heller, and Beibei Yang for their work developing content and graphics for the chapters. A special thanks to subject matter experts John Cardente and Ganesh Rajaratnam for their active involvement reviewing multiple book chapters and providing valuable feedback throughout the project.
We are also grateful to the following experts from EMC and Pivotal for their support in reviewing and improving the content in this book:
| Aidan O'Brien | Joe Kambourakis |
| Alexander Nunes | Joe Milardo |
| Bryan Miletich | John Sopka |
| Dan Baskette | Kathryn Stiles |
| Daniel Mepham | Ken Taylor |
| Dave Reiner | Lanette Wells |
| Deborah Stokes | Michael Hancock |
| Ellis Kriesberg | Michael Vander Donk |
| Frank Coleman | Narayanan Krishnakumar |
| Hisham Arafat | Richard Moore |
| Ira Schild | Ron Glick |
| Jack Harwood | Stephen Maloney |
| Jim McGroddy | Steve Todd |
| Jody Goncalves | Suresh Thankappan |
| Joe Dery | Tom McGowan |
We also thank Ira Schild and Shane Goodrich for coordinating this project, Mallesh Gurram for the cover design, Chris Conroy and Rob Bradley for graphics, and the publisher, John Wiley and Sons, for timely support in bringing this book to the industry.
Nancy Gessler
Director, Education Services, EMC Corporation
Alok Shrivastava
Sr. Director, Education Services, EMC Corporation
Technological advances and the associated changes in practical daily life have produced a rapidly expanding “parallel universe” of new content, new data, and new information sources all around us. Regardless of how one defines it, the phenomenon of Big Data is ever more present, ever more pervasive, and ever more important. There is enormous value potential in Big Data: innovative insights, improved understanding of problems, and countless opportunities to predict—and even to shape—the future. Data Science is the principal means to discover and tap that potential. Data Science provides ways to deal with and benefit from Big Data: to see patterns, to discover relationships, and to make sense of stunningly varied images and information.
Not everyone has studied statistical analysis at a deep level. People with advanced degrees in applied mathematics are not a commodity. Relatively few organizations have committed resources to large collections of data gathered primarily for the purpose of exploratory analysis. And yet, while applying the practices of Data Science to Big Data is a valuable differentiating strategy at present, it will be a standard core competency in the not so distant future.
How does an organization operationalize quickly to take advantage of this trend? We've created this book for that exact purpose.
EMC Education Services has been listening to the industry and organizations, observing the multi-faceted transformation of the technology landscape, and doing direct research in order to create curriculum and content to help individuals and organizations transform themselves. For the domain of Data Science and Big Data Analytics, our educational strategy balances three things: people—especially in the context of data science teams, processes—such as the analytic lifecycle approach presented in this book, and tools and technologies—in this case with the emphasis on proven analytic tools.
So let us help you capitalize on this new “parallel universe” that surrounds us. We invite you to learn about Data Science and Big Data Analytics through this book and hope it significantly accelerates your efforts in the transformational process.
Thomas P. Clancy
Vice President, Education Services, EMC Corporation
January 2015
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.
University and college faculties are invited to join the Academic Alliance program to access unique “open” curriculum-based education on the following topics:
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 [URL inactive].
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.