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MBA in Artificial Intelligence (AI) and Data Science (DS)

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Level
Postgraduate
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Duration
2 Years
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MBA in Artificial Intelligence (AI) and Data Science (DS)

Admission Procedure

Merit prepared on basis of the qualifying exam

Provisional Admission

2 years Duration, in accordance with UGC/AICTE norms.

facilities Scheme of Teaching and Evaluation

About The Program

Welcome to the Department of Management Studies at Graphic Era (Deemed to be) University, where we are celebrating 25 years of excellence in research and education! The Department of Management Studies was established in 2003.

Ranked 52 among the top management college in India by NIRF 2024 and awarded a Diamond rating by QS I-Gauge, the Department of Management Studies, at Graphic Era (Deemed to be University), is a renowned department that provides a transformative educational experience to its students and prepares them for successful careers.

The department encourages a multi-disciplinary perspective by providing opportunities to learn from other departments of the University. With a focus on global business practices and international exposure, Department of Management Studies (DOMS), Graphic Era (Deemed to be University), as the top-notch management institute in India prepares students to become future leaders who can navigate and succeed in diverse cultural and economic environments.

Graphic Era is shaping the future of learning through its world-class Centres of Excellence, designed to equip students with industry-ready skills and hands-on expertise:

  • AWS Skill Builder University Campus and the nation's First Future Ready GenAI Campus, enabling next-generation learning in Cloud Computing, Generative AI, and scalable digital solutions
  • Centre for Artificial Intelligence & High-Performance Computing, established in collaboration with NVIDIA, fostering innovation in AI, Deep Learning, and advanced computing technologies
  • iOS Development Center, powered by Apple and Infosys, empowering students to design and develop cutting-edge applications for the Apple ecosystem

Eligibility

  • Bachelor's degree in any discipline from a recognized university
  • Minimum 50% aggregate marks (45% for SC/ST candidates)
  • Valid CAT/MAT/XAT/CMAT/GMAT/GEU Entrance Test score

Admission will be granted by the university on the basis of entrance test scores, and personal interview.as per merit decided by the university.

Teaching Pedagogies

The Department of management studies adopts innovative, student-centric teaching pedagogies designed to enhance conceptual understanding, practical proficiency, and industry readiness. Our approach integrates modern educational practices with advanced technological tools to create an engaging and outcome-driven learning environment. Key pedagogical practices include:

  • Outcome-Based Education (OBE): All courses are structured around clearly defined learning outcomes to ensure measurable academic and professional growth.
  • Experiential & Hands-On Learning : Emphasis on laboratory work, real-time projects, coding sessions, and technical workshops to strengthen practical understanding.
  • Project-Based & Problem-Based Learning : Students work on industry-relevant problems, case studies, mini-projects, and capstone projects to develop analytical and solution-oriented skills.
  • Flipped Classroom & Blended Learning : Classroom time is optimized through pre-class digital content, interactive discussions, and collaborative problem-solving activities.
  • Industry-Academia Engagement : Regular expert lectures, masterclasses, internships, and certifications through collaborations with leading MNCs enhance industry exposure.
  • Use of Modern ICT Tools : Smart classrooms, LMS platforms, virtual labs, simulation tools, and coding environments support interactive and technology-driven learning.
  • Research-Oriented Teaching : Students are encouraged to explore emerging technologies, publish research papers, participate in hackathons, and engage in innovation-driven initiatives.
  • Continuous Assessment & Feedback : Frequent quizzes, assignments, reviews, presentations, and peer evaluations help monitor progress and encourage continuous improvement.
  • Skill Development & Holistic Learning : Focus on communication skills, teamwork, professional ethics, and lifelong learning to prepare students for global professional environments.

Program Educational Objectives (PEOs)

PEO1

To produce graduates having knowledge, ability and skill to apply basic principles of Management, Artificial Intelligence & Data Science to plan, execute, monitor and evaluate business decisions.

PEO2

To develop analytical aptitude among students for effective coordination and communication for managing business organizations.

PEO3

To inculcate leadership qualities, interpersonal & techno-managerial skills and professional traits among the students to work individually and in team.

PEO4

To sensitize the students towards issues like personal and professional ethics, environment conservation, culture and socio-political settings of immediate surroundings.

Program Outcomes (POs)

PO1

Apply knowledge of management theories and practices, and data science to solve business problems.

PO2

Foster Analytical and critical thinking abilities for data-based decision making.

PO3

Ability to develop and exercise Value based Leadership.

PO4

Ability to understand, analyze and communicate global, economic, legal, and ethical aspects.

PO5

Ability to lead themselves and others in the achievement of organizational goals, contributing effectively to a team environment.

PO6

Ability to develop enterprising skills using innovative practices.

Program Outcomes (POs)

PSO1

Developing resourcefulness through understanding of important elements and interventions of AI&DS for data driven decision making.

PSO2

Analyzing and applying Artificial Intelligence & Data Science tools and techniques in various functional areas of management, to solve complex business problems.

PSO3

Exposure to evolving global trends in Artificial Intelligence & Data Science for effective and efficient business administration and sustainability practices.

Career Prospects

With the ever-growing demand for highly skilled professionals in the IT sector, M.Tech CSE graduates can explore a wide range of advanced and leadership-oriented career roles across research organizations, private industries, government sectors, consulting firms, and academia. In addition to the roles listed earlier, graduates may pursue the following opportunities:

  • AI Product Manager
  • Data Science Manager / Lead
  • MLOps Engineer / Manager
  • Applied Data Scientist
  • Business Intelligence (BI) Manager
  • Computer Vision / NLP Specialist (Business Focus)
  • AI Solutions Architect
  • Strategic Analytics Consultant
  • Chief of Staff / Strategy Officer (Analytics Focus)
  • Business Analytics Manager
  • Customer Analytics Manager
  • Revenue Manager / Pricing Analyst (AI-Driven)
  • Digital Transformation Lead
  • Head of AI / Chief AI Officer (CAIO)
  • Director of Data & Analytics
  • Venture Capital / Private Equity Associate (Deep Tech)
  • Entrepreneur / Founder
  • Analytics Product Lead
  • FinTech Product Manager (BFSI)
  • Healthcare Analytics Manager (Healthcare)
  • Supply Chain Analytics Director (Retail/Manufacturing)
  • Growth Analytics Manager (Tech/SaaS)
  • Marketing Analytics Head (E-commerce/CPG)
Graphic Era (Deemed to be University)

Placements

Graphic Era Deemed to be University has a strong connection to various industries, and its track record for successfully placing students in reputable positions is outstanding, with graduates being placed in internships and permanent roles. The university has formed valuable relationships with globally recognized companies such as Amazon, Microsoft, Google, Walmart, Adobe, and many more, providing students with ample opportunities to kick-start their careers.

Graduates from Graphic Era Deemed to be University can be confident in their ability to succeed in the workforce due to the exceptional training and real-world experience they gain from their internships and placements with these top-tier companies.

Notes: Semester 1 and 2 are applicable only for regular entry students. Lateral entry students begin from Semester 3.

Course Curriculum

Trimester 1

  • Foundation of Python
  • Managerial Economics
  • Management Concepts and Organizational Behaviour
  • Math Foundation and Statistical Analysis with Python
  • Database Management and Data Warehousing-PBL
  • PDP and Business Aptitude-I#
  • MOOC/Certification-I*

Trimester 2

  • Marketing Management
  • Financial Statement Analysis and Reporting
  • Big Data Analytics
  • Machine Learning Foundation
  • Cloud Computing#
  • Project I (PBL)
  • PDP and Business Aptitude-II
  • MOOC on Functional & Industry-Focused Course

Trimester 3

  • Corporate Finance
  • Strategic Management
  • Machine Learning-II (PBL)#
  • Business Intelligence
  • Project II (PBL)
  • PDP and Business Aptitude-III
  • MOOC on Research Methodology

Trimester 4

  • Human Resource Management
  • Quantitative Finance
  • Leveraging GEN AI and Prompt Engineering #
  • Data Engineering (PBL)
  • AI in Business Transformation
  • Summer Internship Program
  • PDP and Business Aptitude-IV
  • MOOC/Certification-IV*

Trimester 5

  • Data Governance and Ethics
  • BlockChain for Managers#
  • NoSQL (PBL)
  • Project Management for Business Excellence
  • SAP/ERP Lab (Operations/Finance Module)
  • MOOC/Certification-V*

Trimester 6

  • Capstone Project (Dissertation/On Job Project/Freelancing/Incubator) (PBL)

Frequently Asked Questions

The program is of 2 years (6 trimesters) duration, in accordance with UGC/AICTE norms.

Candidates must have completed graduation from UGC-approved institution. Admission is based on merit and a personal interview conducted by the University.

Completing an MBA in artificial intelligence and data science opens a singular and strong career path at the crossroads of business leadership and cutting-edge technology. For their combined capacity to develop data-driven commercial strategy and grasp the technical implementation of AI/ML solutions, graduates are much wanted. This hybrid profile provides opportunities for positions requiring strategic influence, including Chief of Staff for Data & Analytics, Strategic Analytics Consultant, and AI Product Manager, where they turn complicated insights into practical plans for the C-suite. Equally qualified for leadership roles like Data Science Manager, Director of Analytics, and Head of AI, they help to unite technical teams with business goals. Furthermore, the degree opens doors in entrepreneurship, Venture Capital (focused on deep tech), and Digital Transformation, hence enabling graduates to propel innovation from within existing companies or by starting their own AI-driven ventures. Graduates are finally positioned as the vital "translators" who can use artificial intelligence to address actual corporate problems and gain great competitive edge across high-growth sectors from tech and finance to healthcare and retail.

The syllabus for an MBA in AI & Data Science is typically a rigorous, interdisciplinary curriculum designed to forge a hybrid professional skilled in both strategic business management and advanced data technologies. The program is usually structured into a core business foundation—covering traditional MBA subjects like Marketing, Finance, Operations, Strategy, and Organizational Behavior—and a specialized AI/DS core that dives into topics such as Programming for Data Science (Python/R), Statistics & Probability, Machine Learning, Deep Learning, Natural Language Processing, and Big Data Technologies. A critical bridge is formed through applied courses like Data-Driven Decision Making, Business Analytics, AI Strategy & Governance, and Analytics for Marketing/Finance/Operations. The program emphasizes practical, hands-on learning through capstone projects, live industry case studies, and internships, where students solve real business problems using AI tools. Ethics, data privacy, and the strategic implementation of AI in organizations are also integral components, ensuring graduates are prepared not just to build models, but to lead responsible, impactful, and scalable AI initiatives within a business context.

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