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Data Science and Big Data Course from EMC

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

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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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