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 are 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.
Dr. Saroj Mahapatra, Director, KSOM

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 16 billion 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 kinds 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
Prof. Manoj Jena, Program Chairperson, Business Analytics

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 Scientists, Data Engineers, and Data Analysts will also increase further 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. Joydeep Biswas

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

Worked at – ITC, Nokia and IBM
Area: Marketing

Board of Studies


Candidate Eligibility

60% career with graduation in Engineering, Science, Commerce, Economics, Statistics, Mathematics or Business administration only. Apply through KIITEE Management, KIITEE or your CAT/MAT/XAT/CMAT score.
Prior work experience will carry additional weightage in selection. Total Seats – 30

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 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

  • Financial Reporting and Analysis
  • Human Resource Management
  • OB: Individual / Group Dynamics & OT
  • Legal & Ethical Aspects of Business
  • Managerial Computing & Software
  • Economic Environment of Business
  • Analytic Toolbox
  • Data querying, Data processing using SQL
  • Advanced Stat and Probability for Data Science
  • Multivariate Data Analytics using SPSS

Semester – II

  • Transforming Businesses through IT
  • Science & Art of Marketing
  • Business Operations & Value Chain
  • Logistics, Supply Chain & E-commerce
  • Statistics and Business Research
  • Strategic Management
  • Business Analytics
  • Predictive Analytics using SAS
  • Data Mining and Business Intelligence
  • Hadoop & Big Data Management

Semester – III

  • Data Analytics using R
  • Machine Learning & AI
  • Text mining and analytics
  • Elective-I
  • Elective-II
  • Elective-III
  • Elective-IV
  • Elective-V

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