Every company has Big Data in its future and every company will eventually be in the data business.

– By Thomas H. Davenpor

Make a Career in the Future of Management

Why MBA in Business (Big Data) Analytics?

Today’s market requires skills in big data technologies, advanced statistics, machine learning, data security, cloud application development, and innovative thinking. From ordering food to entertainment to autonomous vehicles, each of us will be engulfed by more data than we are neurologically equipped to handle. The scale and ubiquity of data is already forcing corporations to find ingenious ways to analyze, track, and leverage data for strategy, and operations. As data volumes continue to explode, businesses need to continually upgrade the education and skill level of their employees to fully utilize the power of data for a competitive edge in the market. Listed below are the few reasons you should think of career in Business (Big Data) Analytics

1. Exponential growth of Big Data market

The Big Data market is predicted to grow exponentially across the world and it shows no signs of deceleration. In step with NASSCOM, the Indian Big Data analytics sector is expected to grow to achieve USD 16billion by 2025 from the present level of USD 2 billion.

2. Big Data is used in every industry

Data professionals are not restricted to work for just a few industry segments but their contribution is for all kind of industry verticals. You can work in any of the domains like finance, manufacturing, information technology, communications, retail, logistics, and automobiles. Each industry uses Big Data for taking a competitive advantage and making data driven decisions

3. Better career opportunities & High salaries

With the increase in the datasets across the universe, the demand for Big data analytics is very hot. According to estimates, the data will further grow to zettabytes in 2025. This means the need for Data Scientist, Data Engineer, and Data Analysts will also increase well in the future.

Faculty Profile

Prof. Manoj Kumar Jena

B.Tech. (CET Bhubaneswar), M.Tech. (IIT Bombay), PGDM (IIM Calcutta)


Worked at: IiAS(Capital markets), Dana Automobiles, Geometric Global, Wipro
Area: Analytics & Operations

Brajaballav Kar

B.Tech, PGDM (XIMB), Ph D


Worked at: SAIL, Sonata Software Ltd, Oracle India, Quark India, Four-Soft and Karak Technologies
Area: Operations & Analytics

Prof. Surya Narayan Mishra

B.Arch (IIT Kharagpur), PGPM (IIM Lucknow)


Worked at: ITC Limited and Nokia India Pvt. Ltd
Area: Marketing

Prof. Piyusa Das

B.Tech (CET Bhubaneswar), PGDM (IIM Lucknow)


Worked at: Hindustan Petroleum, Ingersoll Rand
Area: Operations & Analytics

Prof. Joydeep Biswas

PGDM (XLRI Jamshedpur), B. Tech (IIT – BHU, Varanasi)


Worked at – ITC, Nokia and IBM
Area: Marketing

Candidate Eligibility

Graduates in Engineering, Science, Commerce, Economics, Statistics, Mathematics, Business administration. CAT. MAT, XAT, CMAT scores are desirable. Working professionals can apply too.

Course Module

MBA in Business Analytics is covered in 4 semesters. The subject allocation is done as per the course requirement of the respective institutions. The first year syllabus is common to all the streams, with specializations being formally divided and focused upon in the final year with a 16-20 months of on the job internship in the 4th semester..

The table below lists the subjects taught in MBA in Business Analytics syllabus.

Semester – I

  • Accounting for Managers (BM 5204)
  • Human Resource Management
  • Marketing Management – I
  • Managerial Economics – I
  • Productions & Operations Management -I
  • Information Technology for Managers
  • Analytic Toolbox
  • Data querying, Data processing using SQL
  • Quantitative Techniques – I
  • Multivariate Data Analytics using SPSS

Semester – II

  • Corporate Finance – I
  • Marketing Management – II
  • Productions & Operations Management -II
  • Business Ethics and CSR (BM6703)
  • Communication and Information Management
  • Quantitative Techniques – II
  • Business Analytics
  • Predictive Analytics using SAS
  • Data Mining and Business Intelligence
  • Hadoop & Big Data Management

Semester – III

  • Data Analytics using R
  • Machine Learning & Artificial Intelligence
  • Advanced Stat and Probability for Data Science
  • Text mining and analytics
  • Elective-I
  • Elective-II
  • Elective-III
  • Elective-IV

Electives for Semester-III

  • Credit Risk Analytics
  • Digital Marketing Analytics
  • Financial Time Series & Analysis
  • HR Analytics
  • Internet of Things
  • Introduction to Marketing analytics
  • Quantitative Finance using R
  • Supply chain analytics
  • Retail Analytics
  • Advanced Business Analytics

Semester – IV

Semester IV: 16-20 WEEKS OF IMMERSION INTERNSHIP