MAY 15
2 Years
4 Sem
- Online
MBA Data Science& Analytics
Learning
Outcomes
- Develop strong foundations in
data science, statistics, and business analytics
- Gain hands-on experience with
tools like Python, R, SQL, Excel, and big data platforms
- Learn data mining, AI
applications, and machine learning fundamentals
- Build the ability to convert
data into actionable business insights
- Enhance decision-making skills
using data-driven strategies
Teaching
Approach
- Practical learning through
real-world datasets and case studies
- Hands-on training with industry
tools and software
- Interactive sessions with data
science experts and professionals
- Continuous assessment through
projects, presentations, and analytics tasks
- Focus on solving real business
problems using data
Industry
Relevance
- Curriculum aligned with current
trends in data science, AI, and analytics
- Exposure to real-time business
data and analytical challenges
- Training in modern tools like
Python, R, Hadoop, Spark, and SQL
- High demand across industries
such as IT, finance, healthcare, and e-commerce
- Organizational Behaviour and
Human Resource Management
- Managerial Economics
- Statistics and Analytics
Foundations for Business
- Corporate Governance and
Business Ethics
- Financial Accounting and
Reporting
- Marketing Management
- Legal and Business Environment
- Entrepreneurship
- Banking, Financial Services and
Insurance
- Acquisition and Management of
Talent
- Management of Banking and
Financial Services
- Integrated Marketing
Communications & Branding
- Employee Reward Management
- Operations Management
- Business Strategy and Leadership
- Data Mining
- AI for Business
- Web and Social Media Analytics
- Final Project / Dissertation
- Machine Learning for Business
- Big Data Analytics (Hadoop &
Spark)
- Data Visualization (Power BI /
Tableau)
- Predictive Analytics
- Business Intelligence &
Dashboarding
Academic
Qualification Requirements
- Bachelor’s degree in any
discipline from a recognized university
- Minimum 50% aggregate marks (45%
for reserved categories)
English
Language Requirements
- Basic proficiency in English for
understanding technical and business concepts
- Ability to interpret data,
reports, and presentations
Accepted
Equivalents
- Equivalent qualifications
recognized by universities or institutions
- Basic knowledge of mathematics
or statistics is an added advantage
Application
Review Notes
- Admission based on academic
performance and interview
- Evaluation of analytical
thinking, problem-solving ability, and interest in data
- Final selection subject to
qualifying admission and job interview
Career
Pathways
- Data Science & Analytics
- Business Intelligence &
Reporting
- AI & Machine Learning
Applications
- Data Engineering & Big Data
- Analytics Consulting
Employability
Support
- Resume building with project
portfolio (data-based projects)
- Training in tools like Python,
SQL, Power BI, and analytics platforms
- Mock interviews and technical
interview preparation
- Mentorship from industry experts
Internships
& Placement Options
- Internship opportunities in IT
companies, analytics firms, and startups
- Placement support with
organizations hiring for data roles
- Exposure to real-time datasets
and analytics projects
Typical
Job Roles after Graduation
- Data Analyst
- Business Analyst
- Data Scientist (Entry-Level)
- BI Analyst / Dashboard Developer
- Data Engineer (Junior Level)
- Analytics Consultant
