Big Data Analytics Training Course

Big Data Analytics Training Course

Big Data Analytics Training Course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It establishes a baseline of skills that can be further enhanced with additional training and real-world experience. The course provides an introduction to big data and a Data Analytics Lifecycle Process to address business challenges that leverage big data.

It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. The course has extensive labs throughout to provide practical opportunities to apply these methods and tools to real-world business challenges and includes a final lab in which students address a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle. The course prepares the student for the Proven™ Professional Data Scientist Associate (EMCDSA) certification exam.


This course is intended for individuals seeking to develop an understanding of Data Science from the perspective of a practicing Data Scientist, including:

  • Managers of teams of business intelligence, analytics, and big data professionals.
  • Current Business and Data Analysts looking to add big data analytics to their skills.
  • Data and database professionals looking to exploit their analytic skills in a big data environment.
  • Recent college graduates and graduate students looking to move into the world of Data Science and big data
  • Individuals seeking to develop their big data analytic skills to take advantage of the EMC Proven™ Professional Data Scientist Associate (EMCDSA) certification

The following modules and lessons included in this course are designed to support the course objectives:

  • Introduction and Course Agenda
  • Introduction to Big Data Analytics
    • Big Data Overview
    • State of the Practice of Analytics
    • The Data Scientist Role
    • Big Data Analytics in Industry Verticals
  • Overview of Data Analytics Lifecycle
    • Discovery
    • Data Preparation
    • Model Planning
    • Model Building
    • Communicating Results and Findings
    • Operationalizing
  • Using R for Initial Analysis of the Data
    • Introduction to Using R
    • Initial Exploration and Analysis of the Data Using R
    • Basic Data Visualization Using R
  • Advanced Analytics and Statistical Modeling for Big Data – Theory and Methods
    • Candidate Selection Using Naïve Bayesian Classifier
    • Categorization Using K Means Clustering and Association Rules
    • Predictive Modeling Using Decision Trees, Linear and Logistic Regression, and Time Series Analysis
    • Text Analytics
  • Advanced Analytics and Statistical Modeling for Big Data – Technology & Tools
    • Survey of Tools for Analytics
    • Analytics for Unstructured Data (MapReduce/Hadoop)
    • The Hadoop Eco-system
    • In-Database Analytics with SQL Extensions and Other Advanced SQL Techniques
    • MADlib Functions for In-Database Analytics
  • Endgame – Operationalizing an Analytics Project
    • Core Deliverables for Key Stakeholders and Others
    • Creating the Final Deliverables

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Key Features -

  • Cloud Server Access

  • Training = Enterprise Scale

  • Advanced Technology Coverage + PoC Project Work

  • 24/7 Technical Support

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