Star Innovations

Certification Program on System Integration Big Data

About this Course
In Big Data Analytics course, the participants will learn to:

Work with the problems of Big Data in real life situation.
Develop advanced Algorithms and OO concepts using Java.
Develop MapReduce applications using Java.
Work with Python Scripting.
Handle No-SQL databases.
Work with the process of Data Transfer and Data Handling using Apache Sqoop and Flume.
Handling Workflow Management using Oozie.
Big Data Platforms – Hortonworks and GreenPlum.
Cloudera’s Hadoop Distribution and Management Tools.
Outcomes: The participants will be ready to work with Big Data problems using Apache Hadoop and its frameworks, along with other tools and frameworks of Hadoop and its eco-system.

The Big Data Hadoop training course comprise of a series of lectures aided by demonstrations and followed by hands-on session on the prescribed equipment and/or software that the course is designed to cover.

Some amount of preparation by the participant is expected, for each session, using the course material available in the form of printed books.

Post each session, the participant is expected to revise the concepts and work on exercises provided.

There is a project that will be assigned mid-way through Big data analytics course. The project will carry a significant portion of the marks that will decide the grade obtained by a student on completing the course.

The other form of assessment will be an online exam comprising of Multiple Choice Questions at the end of the module.
Course Syllabus
All learners who register on or after May 1, 2017 will get access to the digitized courseware online on Digital Hub and no hard copy of courseware will be issued.

There are 18 modules

01 MODULE
OS Concepts and Shell Scripting on Linux
Brief Description:
This module familiarizes the participants with the working commands of Linux and its file system.
Recommended Learning Duration:
10 Hours

02 MODULE
Logic Building and Algorithms.
Brief Description:
This module helps the participants to develop Logic Building and Algorithm concepts.
Recommended Learning Duration:
12 Hours

03 MODULE
Basic Java Programming.
Brief Description:
This module helps the participants to learn the concepts of programming using Java.
Recommended Learning Duration:
40 Hours

04 MODULE
Database Fundamentals and SQL Databases.
Brief Description:
This module covers the concepts of Databases and SQL.
Recommended Learning Duration:
20 Hours

05 MODULE
Object Oriented Programming with Java.
Brief Description:
This module covers the concepts of OO analysis and design using Java.
Recommended Learning Duration:
40 Hours

06 MODULE
Fundamentals of Web Technologies.
Brief Description:
This module covers the fundamental concepts of Web Designing using HTML, Javascript, etc.
Recommended Learning Duration:
16 Hours

07 MODULE
Scripting with Python.
Brief Description:
This module covers the concepts of Python scripting language and its usage in developing Big Data Algorithms.
Recommended Learning Duration:
40 Hours

08 MODULE
Software Engineering , Quality & Coding Standards.
Brief Description:
This module covers the concepts of engineering a software.
Recommended Learning Duration:
32 Hours

09 MODULE
Advanced Algorithms.
Brief Description:
This module covers the concepts of designing advanced algorithms to implement mapreduce programs.
Recommended Learning Duration:
10 Hours

10 MODULE
No SQL Databases.
Brief Description:
This module covers the concepts of NoSQL databases and their implementation.
Recommended Learning Duration:
40 Hours

11 MODULE
Introduction to Big Data and Working with Hadoop.
Brief Description:
This module covers the concepts of Big Data Hadoop and its problems.
Recommended Learning Duration:
30 Hours

12 MODULE
Application Development using MapReduce and Java.
Brief Description:
This module covers the programming concept of mapreduce using Java.
Recommended Learning Duration:
20 Hours

13 MODULE
Data Transfer and Data Handling using Apache Sqoop and Flume.
Brief Description:
This module covers the concepts of Data transfer using Sqoop and handling streaming data by using Flume.
Recommended Learning Duration:
20 Hours

14 MODULE
Workflow Management using Oozie.
Brief Description:
This module covers the concepts of handling workflows using OOzie.
Recommended Learning Duration:
10 Hours

15 MODULE
Big Data Platforms – Hortonworks and GreenPlum.
Brief Description:
This module deals with the Big data frameworks.
Recommended Learning Duration:
10 Hours

16 MODULE
Cloudera’s Hadoop Distribution and Management Tools
Brief Description:
This module deals with Cloudera framework of Hadoop.
Recommended Learning Duration:
10 Hours

17 MODULE
Project.
Brief Description:
This module is used to guide and hand hold the candidates for developing a project in Big Data Development.
Recommended Learning Duration:
10 Hours

18 MODULE
Soft Skills
Brief Description:
This module is to groom the candidates and make them market ready.
Recommended Learning Duration:
20 Hours
Final Exam- Online, Proctored
This is the final evaluation module of learning.

Mote Info