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Big Data Analytics requires involvement of Data Scientists
"We live in a data-driven world. Increasingly, the efficient operation of organizations across sectors relies on the effective use of vast amounts of data. Making sense of big data is a combination of organizations having the tools, skills and more importantly, the mindset to see data as the new "oil" fueling a company. Unfortunately, the technology has evolved faster than the workforce skills to make sense of it and organizations across sectors must adapt to this new reality or perish."
 Andreas Weigend, Ph.D Stanford, Head of the Social Data Lab at Stanford, former Chief Scientist Amazon.com

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"The industry is facing a bold new world with the amazing technological developments of the Cloud and Big Data, and the challenges and the opportunities this presents are unprecedented. EMC's extensive Cloud training and certifications help IT practitioners develop the skills that are crucial as businesses embark on this massive transformation. The new EMC Data Science and Big Data Analytics training and certification provides an industry credential for business analysts, data warehouse experts or other professionals with similar backgrounds to help them transform into the world of Data Science. IDC believes these training and certifications are a positive step to help both the industry and businesses move forward and quickly take advantage of the benefits that the cloud and Big Data present."
Ben Woo, Vice President of Storage Systems and Lead Analyst on Big Data, IDC

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EMC Proven Professional is the #1 certification program in the information storage and management industry. Being Proven means investing in yourself and formally validating your knowledge, skills, and expertise by the industry's most comprehensive learning and certification program. The Data Science and Big Data Analytics course prepares you for Data Scientist Associate (EMCDSA) Certification. For more information, visit: http://education.EMC.com/Certification

Course Outline
Big Data Overview State of the practice in analytics The role of the Data Scientist Big Data Analytics in
Industry Verticals
Introduction to Big Data Analytics
Key roles for a successful analytic project Main phases of the lifecycle Developing core deliverables for stakeholders
End-to-end data analytics lifecycle
Introduction to R Analyzing and exploring data with R Statistics for model building and evaluation
Using R to execute basic analytic methods
Naïve Bayesian Classifier K-Means Clustering Association Rules Decision Trees Linear and Logistic Regression Time Series Analysis Text Analytics
Advanced analytics and statistical modeling for Big Data – Theory and Methods
Using MapReduce/Hadoop for analyzing unstructured data Hadoop ecosystem of tools In-database Analytics MADlib and Advanced SQL Techniques
Advanced analytics and statistical modeling for Big Data – Technology and Tools
How to operationalize an analytics project Creating the Final Deliverables Data Visualization Techniques Hands-on Application of Analytics
Lifecycle to a Big Data Analytics Problem
Communication of Results and Big Data Analytics Life Cycle Lab
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Unleash the Power of
Big Data Analytics


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