Data Science and Big Data Analytics – EMC Education Services

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.

Related posts:

Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Learn Keras for Deep Neural Networks - Jojo Moolayil
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Machine Learning with spark and python - Michael Bowles
The hundred-page Machine Learning Book - Andriy Burkov
Java Deep Learning Essentials - Yusuke Sugomori
Introduction to Scientific Programming with Python - Joakim Sundnes
Medical Image Segmentation Using Artificial Neural Networks
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
R Deep Learning Essentials - Dr. Joshua F.Wiley
Python Machine Learning Eqution Reference - Sebastian Raschka
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Deep Learning Cookbook - Indra den Bakker
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Introduction to Deep Learning - Eugene Charniak
Deep Learning with Theano - Christopher Bourez
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning for Natural Language Processing - Jason Brownlee
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning in Python - LazyProgrammer
Amazon Machine Learning Developer Guild Version Latest
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar