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2018/09/13
BITS Pilani
In association with

PG Program in Big Data Engineering

Online with Offline Workshops11 MonthsStarts December 2018Rs. 2,35,000 (Incl. Taxes)

Successfully launched

5 batches
1000+Enrollments
250+Recruitment partners

Our Student Profile

Job Profiles
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Software Developers & Engineers
54%
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Technical Leads and Managers
23%
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Senior Management roles
18%
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Data Analysts, Engineers & Scientists
5%
Work Experience
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Companies they work at

PG Certification from BITS Pilani

Complete all courses successfully and receive Post-Graduate certificate. Become a part of the Big Data community with the PG Alumni status from BITS Pilani

Why Learn Big Data Engineering?

High Impact Projects

Real-time Aadhar verification, Amazon's User Recommendations, Facebook's newsfeed suggestions - all are driven by Big Data!


Wide ranging Applications

Be it Manufacturing to E-commerce to Public Sector to Healthcare to Agriculture- Big Data is applicable everywhere! And its use cases are growing rapidly!


High demand for skills

Thousands of job openings from top companies with 30-60% salary hikes for skilled Big Data professionals




Program Syllabus

The curriculum has been developed by BITS faculty and leading Big Data companies. Most courses have an independent industry-sourced project that will be deployed by you on AWS Cloud. This syllabus will teach you end to end skills - a thorough understanding of fundamental concepts and thinking beyond tools!

If you don’t have previous experience in programming or databases (SQL) , don't worry! By enrolling for the program, you get access to completely free, pre-program preparatory sessions which will augment your skills in fundamental Computer Science concepts.

Topics Covered:

  • Object Oriented Programming (OOP) using JAVA
  • Data Structures
  • Design and Analysis of Algorithms
  • Relational Database Management Systems (SQL)

Prep Sessions will be available to students upon enrolment.

Duration : 8 weeks

In this course you will be given an introduction to Big Data and its common industry applications. You will also develop important foundations in data structures and algorithms that form the basis of the Big Data Systems used in the industry.

Topics Covered:

  • Introduction to Big Data and its Applications
  • Data Abstraction
  • Linear data structures like Hashtables, Hashmaps, Bloom Filters
  • Non-linear data structures like Binary Search Trees, KD Trees
  • Distributed Algorithm Design
  • Algorithm Design using MapReduce

Course Outcomes:

You will be able to select and implement appropriate data structures to solve big data problems and also write Map and Reduce codes for distributed processing of data.

Programming Language Used: Java

Duration: 8 weeks

In this course, you will be exposed to the different platforms used for processing Big Data. Additionally, you will also learn how to set up a virtual machine for processing Big Data on your own computer as well as on the cloud.

Topics Covered:

  • Distributed Computing Environment for Big Data
  • Distributed Processing of data using MapReduce & Pig
  • In-memory distributed processing using Apache Spark
  • Data Storage on Cloud Dynamo DB.

Course Outcomes:

You will be able to perform batch processing operations on Big data on your own computer as well as on an Amazon EC2 instance. You will be able to retrieve and store data in HDFS using MapReduce & Apache Pig

Tools & Technologies Used: Hadoop, Apache Pig, Apache Spark & Dynamo DB

Duration : 7 weeks

Learn about collecting and processing structured and unstructured data by performing ETL operations. Use workflow manager tools to learn automation of task flows

Topics Covered:

  • Performing ETL Operations
  • Concepts in Data Warehousing and its Relevance for Big Data.
  • Ingesting data into Big Data Platforms using Apache Sqoop & Flume.
  • NoSQL databases for Big Data Storage Applications (HBase)
  • Workflow management for Hadoop using OOZIE

Course Outcomes:

You will learn to choose and use tools to ingest structured and unstructured data into big data processing systems and use Hive to perform data transformations. You will use OOZIE for managing your workflow.

Tools & Technologies Used: Sqoop, Apache Flume, Apache Hive and HBase.

Duration : 4 weeks

Ever wondered how you receive a notification based on your location? The answer lies in exploiting Real Time & Streaming Data. This course will expose you to the exciting world of processing real time data.

Topics Covered:

  • Applications of Streaming Data in Industry
  • Sourcing Streaming data using Apache Flume
  • Building real-time data pipeline using Apache Storm
  • Streaming on Apache Spark

Course Outcomes:

You will be able to build real time data processing systems using Apache Storm and Apache Spark

Tools & Technologies Used: Apache Storm, Apache Flume, Apache Spark

Duration : 5 weeks

In this course you will be introduced to the field of Big Data Analytics and you will learn about the libraries in Apache Spark used to perform Regression, Classification, Clustering on Big Data.

Topics Covered:

  • Regression, Clustering & Classification using Spark MLLib
  • Building visualizations using Big Data
  • Case Studies on applications of Big Data Analytics

Course Outcomes:

  • You will be able to perform analytics on the big data using Spark MLLib and get knowledge of tools to visualize results.
  • Interested students will also have an opportunity to learn the basics of functional programming in Scala*

Tools & Technologies used:

Spark (MLLib) and Scala*

Duration : 6 weeks

Apply lessons learnt in the program in an industry relevant project by ingesting, processing and analyzing data on a big data platform in cloud.

Click here to know more about Capstone Project.

View more

* signifies optional/additional learning material for interested students

You will receive the download link in your email.

Student Reviews


Program Vitals

Program Fee

Rs. 2,35,000
EMI starts at INR 8,038/- month.
(Inclusive of all taxes)
EMI Plans

Course Duration

Dec'18 - Nov'1911 months

We recommend

10 hoursper week
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