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:

Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
R Deep Learning Essentials - Dr. Joshua F.Wiley
An introduction to neural networks - Kevin Gurney & University of Sheffield
Python Data Structures and Algorithms - Benjamin Baka
Pattern recognition and machine learning - Christopher M.Bishop
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Fundamentals of Deep Learning - Nikhil Bubuma
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Introduction to Scientific Programming with Python - Joakim Sundnes
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Coding Theory - Algorithms, Architectures and Application
Java Deep Learning Essentials - Yusuke Sugomori
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning with PyTorch - Vishnu Subramanian
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Medical Image Segmentation Using Artificial Neural Networks
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning in Python - LazyProgrammer
Python Machine Learning Eqution Reference - Sebastian Raschka
Machine Learning with spark and python - Michael Bowles