In today's world, properly leveraged data can give organizations of all types a competitive advantage. Companies now handle vast amounts of data on a daily basis, and sorting, storing, and analyzing this data is more challenging than ever. Big Data and Analytics professionals can extract useful information from data and increase the ROI of a business. The demand for these professionals is steadily increasing.

Introduction

·         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

·           Naive 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