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:

Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Data Structures and Algorithms - Benjamin Baka
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning with Python - Francois Cholletf
Deep Learning with Python - Francois Chollet
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Introduction to Deep Learning - Eugene Charniak
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Intelligent Projects Using Python - Santanu Pattanayak
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Machine Learning with Python for everyone - Mark E.Fenner
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Java Deep Learning Essentials - Yusuke Sugomori
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Deep Learning with Theano - Christopher Bourez
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning with PyTorch - Vishnu Subramanian
Learn Keras for Deep Neural Networks - Jojo Moolayil
An introduction to neural networks - Kevin Gurney & University of Sheffield