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:

Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Machine Learning with spark and python - Michael Bowles
Neural Networks - A visual introduction for beginners - Michael Taylor
R Deep Learning Essentials - Dr. Joshua F.Wiley
Python Machine Learning Eqution Reference - Sebastian Raschka
Machine Learning with Python for everyone - Mark E.Fenner
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
An introduction to neural networks - Kevin Gurney & University of Sheffield
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Medical Image Segmentation Using Artificial Neural Networks
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Java Deep Learning Essentials - Yusuke Sugomori
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Artificial Intelligence by example - Denis Rothman
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy