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 Amazon.com

Course Details

Course Overview »Course Description »

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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