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BIGDATA Training

Big Data will help to create new career growth opportunities for job seekers and growth for entirely new categories of companies, such as those that aggregate and analyses industry data. Many of these will be companies that sit in the middle of large information flows where data about products and services, buyers and suppliers, consumer preferences and intent can be captured and analyzed. Forward-thinking leaders across sectors should begin aggressively to build their organizations’ Big Data capabilities.

Module 1: BigData courses include: R, Hadoop, Pig, Hive, HBase, Sqoop, Storm, Spark, MongoDB, Casandra, Sqoop, Mahout

Module 2: Database, Tableua, R, Python Programming, Numpy, Pandas, Linear Problem Solving, Forecasting, Statistics

Students are encouraged to combine module 1 &  2 for the better career success and build a strong knowledge base for the current market tools/technology.

BIG DATA Programming.


Learn how to implement ETL and machine learning algorithms using BIGDATA technologies


Key concepts:


Module 1:  Big Data use cases with Hadoop Technology 


Time: ~32 hours


BIG DATA Foundation

  • Database – overview, Oracle PL/SQL   [OCP]

  • Data warehouse, ETL

  • Volume, Velocity, Verocity 

  • Data Warehouse vs BIG DATA

  • BIG DATA – Use cases,   OLAP vs OLTP


BIG DATA Programming

  • Hadoop Architecture 

  • Linux Shell Scripting

  • Hadoop Map Reduce 

  • Sqoop – Data import and export

  • Programming with: 

  • PIG

  • Hive

  • Mahout

  • MySQL


  • Zookeeper

  • Storm/Spark – Real-time Analytics

  • Java/Python - UDF (User Define Functions)

  • Theory topics are complemented by hands-on LAB

  • + E2E Project 

Module 2: Data Driven Decision By Managers


Time: 32 hours


  • Database and Data Analysis

  • Database vs DataWare house vs Big Data

  • Managerial decisions based on data - Introduction

  • Product Valuation [NPV, DCF, FV, PV, WAAC]

  • Statistic Overview

  • Forecasting, and Projection Algorithm [R, Excel]

  • Classification, Clustering, Regression Algorithm

  • Descriptive and Visual Data Analysis [tableau]

  • Machine Learning [Python, R]

  • Linear Problem [LP]

  • Excel Solver

  • R Programming

  • Python Statistical Library [NumPy, SciPy, Panda, PyLab]

  • Theory topics are complemented by hands-on LAB

  • Project Work/Case Study:  Real Industrial problem solution in a classroom setting


Staffing Support
  • Resume Preparation

  • Mock Interview Preparation

  • Phone Interview Preparation

  • Face to Face Interview Preparation

  • Project/Technology Preparation

  • Internship with internal project work

  • Externship with client project work

Our Salient Features:
  • Hands-on Labs and Homework

  • Group discussion and Case Study

  • Course Project work

  • Regular Quiz / Exam

  • Regular support beyond the classroom

  • Students can re-take the class at no cost

  • Dedicated conf. rooms for group project work

  • Live streaming for the remote students

  • Video recording capability to catch up the missed class

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