Big Data Analytics

About This Course:

While Analytics as a term has been around for some time now, ‘Big Data’ is a more recent phrase. It has come into existence because of the sheer volume of data that is being generated today in almost every aspect of our lives. Big Data is data that is too large and complex for conventional data tools to capture, store and analyze. When put to good use, Big Data allows analysts to spot trends, extract insights and make predications.


  • 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

Course Duration : 20 Days (80 Hrs)