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:

Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning for Natural Language Processing - Jason Brownlee
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning in Python - LazyProgrammer
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
R Deep Learning Essentials - Dr. Joshua F.Wiley
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Introduction to the Math of Neural Networks - Jeff Heaton
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning and Neural Networks - Jeff Heaton
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Introduction to Deep Learning - Eugene Charniak
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Java Deep Learning Essentials - Yusuke Sugomori