Data Science and Big Data Analytics
Discovering, Analyzing, Visualizing and Presenting Data
By EMC Education Services
Publisher: Wiley
Final Release Date: December 2014
Pages: 432

Data Science and Big Data Analytics

Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are relevant to any industry and technology environment, and the learning is supported and explained with illustrative examples using open-source software.

This book will help you:

  • Become a contributor on a data science team
  • Deploy a structured lifecycle approach to data analytics problems
  • Apply appropriate analytic techniques and tools to analyze big data
  • Learn how to tell a compelling story with data to drive business action
  • Prepare for EMC ProvenTM Professional Data Scientist certification

EMC Proven™ Professional is a leading education and certification program in the IT industry, providing comprehensive coverage of information storage technologies, virtualization, cloud computing, data science/big data analytics, and more…

Being Proven means investing in yourself and formally validating your expertise!

This book prepares you for the Data Science Associate exam E20-007 leading to EMC Proven Professional Data Science Associate (EMCDSA) certification.

Visit for details.

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oreillyData Science and Big Data Analytics

(based on 1 review)

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Good for Data Science, Big Data less so

By Accidental Numbers Guy

from Silicon Valley, CA

Verified Buyer


  • Helpful examples
  • Well-written


  • Not comprehensive enough

Best Uses

  • Novice
  • Student

Comments about oreilly Data Science and Big Data Analytics:

When I got this, I was hoping to bridge the gap between my background in R and working with larger data sets, i.e. big data. This book does a very nice job presenting what data science is all about and showing how to work through key concepts using R and Python. Like so many books, though, it stumbles when it gets to the big data world of Hadoop. The examples are sparser, the explanations less concise. If you need a good overview of Data Science and to pick up some R in the process of learning them, the first 9 chapters of this book are great. If you already have a handle on data science basics and want to move into big data, Learning Spark might be a better next step.

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Ebook:  $60.00
Formats:  ePub, Mobi, PDF