M.Sc. Artificial Intelligence and Data Science
Programme Overview
The M.Sc. in Artificial Intelligence and Data Science is a future-focused postgraduate programme designed to develop skilled professionals capable of solving complex real-world challenges using Artificial Intelligence, Machine Learning, Data Analytics, and emerging technologies. The programme combines strong theoretical foundations with hands-on practical learning, industry-oriented projects, and research-driven innovation. Students gain expertise in data-driven decision-making, intelligent systems, deep learning, computer vision, and big data analytics through advanced coursework and laboratory experiences. With a balanced emphasis on academic excellence, industry exposure, and research competency, the programme prepares graduates for successful careers in industry, entrepreneurship, higher studies, and research.🏛 Academic & Infrastructure Excellence
The M.Sc. Artificial Intelligence and Data Science programme is designed in accordance with contemporary academic standards and emerging technological trends. The curriculum integrates advanced concepts in Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Computer Vision, Natural Language Processing, and Big Data Analytics. Students benefit from a robust academic ecosystem supported by modern infrastructure, digital learning resources, and research-oriented facilities that foster innovation and academic excellence.
🤝 Industry-Relevant Learning Environment
The programme emphasizes industry-aligned education through a curriculum developed with consideration of current technological demands and workforce requirements. Students are exposed to real-world case studies, industry-standard tools, cloud platforms, and analytics frameworks. Regular interactions with industry professionals, workshops, certification programmes, and collaborative projects ensure that graduates are equipped with the skills required by today’s AI and data-driven industries.
👨🏫 Faculty & Teaching Approach
The programme is delivered by experienced faculty members possessing expertise in Artificial Intelligence, Data Science, Machine Learning, Computer Vision, and interdisciplinary research domains. The teaching methodology combines classroom instruction, case-based learning, project-based pedagogy, research-oriented assignments, seminars, and collaborative learning approaches. Faculty members actively engage students in critical thinking, innovation, and problem-solving activities to promote deeper understanding and practical application of concepts.
💼 Practical Experience & Career Readiness
A strong focus is placed on experiential learning through laboratory sessions, mini-projects, internships, dissertation work, and industry-oriented assignments. Students gain hands-on experience in developing AI models, analyzing complex datasets, designing intelligent systems, and solving domain-specific problems. The programme nurtures technical competencies, analytical thinking, communication skills, and professional ethics, ensuring graduates are well-prepared for diverse career opportunities and research pursuits.
🌍 Global Expert Engagement
To provide students with a global perspective on emerging technologies, the programme regularly organizes expert talks, webinars, workshops, and technical sessions delivered by renowned academicians, researchers, and industry leaders from national and international organizations. These interactions expose students to cutting-edge developments, contemporary research trends, and global best practices in Artificial Intelligence and Data Science.
🚀 Holistic Development
Beyond technical education, the programme focuses on the overall personality development of students through participation in technical clubs, innovation challenges, hackathons, seminars, community engagement activities, and interdisciplinary projects. The curriculum encourages leadership, teamwork, creativity, ethical responsibility, and lifelong learning, enabling students to become competent professionals and responsible contributors to society.
🎯 Placement Support
The programme provides comprehensive career development support through placement assistance, internship opportunities, aptitude training, resume-building workshops, mock interviews, and career guidance sessions. Strong industry connections and institutional support help students explore career opportunities in Artificial Intelligence, Data Science, Machine Learning, Business Analytics, Research & Development, and emerging technology sectors. Students are also encouraged to pursue higher studies, research careers, and entrepreneurial ventures.
🖥️ State-of-the-Art Laboratories
Students have access to advanced computing facilities and specialized laboratories equipped with modern hardware and software platforms for Artificial Intelligence and Data Science applications. The laboratories support practical learning in Machine Learning, Deep Learning, Data Analytics, Computer Vision, Cloud Computing, and Research Methodology. Access to high-performance computing resources, GPU-enabled systems, open-source frameworks, and industry-standard tools enables students to gain valuable hands-on experience and conduct innovative research.
- Bachelor’s degree in Computer Science, IT, Engineering, Mathematics, Statistics, or related field
- Admission Information
Programme
M.Sc. Artificial Intelligence & Data Science
Duration
2 Years
Intake
30 Seats
Annual Fees
Rs. 80,000
Mode of Admission
As per ACPC / University Norms
Degree Awarded
Master of Science (M.Sc.)
Meet Our Distinguished Faculty
Our experienced faculty members bring together academic rigor, research expertise, and industry insights to prepare students for successful careers in the rapidly evolving world of technology. ✨
- Programme Curriculum
The M.Sc. Artificial Intelligence and Data Science programme is a two-year postgraduate programme comprising four semesters. The curriculum is carefully designed to provide strong foundations in Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Generative AI, and domain-specific applications through specialized elective tracks.
Semester I
Foundation Courses
- AI Fundamentals
- Statistical Methods
- Machine Learning
- Data Visualization & Modelling
- Responsible AI
- Entrepreneurship Development
Semester II
Advanced AI and Data Science Courses
- Natural Language Processing
- Computer Vision
- Time Series Analysis and Forecasting
- Deep Learning
- Big Data Analytics
Total Credits: 20
Semester III
Emerging Technologies and Specialization
- Generative AI
- Agentic AI Systems
- Elective I
- Elective II
- Elective III
- Major Project – I
Total Credits: 22
Semester IV
Specialization and Research
- Elective IV
- Major Project – II
Total Credits: 20
- Specialization Tracks
Students can pursue one of the following domain-specific specialization tracks during Semester III and IV.
Track 1
Healthcare Analytics
Elective I
- Transforming Healthcare Analytics
Elective II
- Biomedical Image Analysis
Elective III
- Artificial Intelligence in Drug Discovery
Elective IV
- Explainable Artificial Intelligence (XAI) in Healthcare
Track 2
Agriculture Analytics
Elective I
- Precision Agriculture Analytics
Elective II
- Computer Vision & Remote Sensing for Agriculture
Elective III
- IoT and Edge AI for Smart Farming
Elective IV
- Autonomous Agricultural Systems and Robotics
Track 3
Smart Infrastructure & Urban Analytics
Elective I
- AI for Smart Infrastructure & Urban Analytics
Elective II
- Structural Health Monitoring using AI
Elective III
- AI-based Disaster Prediction and Risk Analytics
Elective IV
- AI for Smart Transportation and Traffic Systems
Program Educational Objectives (PEOs)
- PEO 1: Equip graduates with advanced knowledge and practical skills in Artificial Intelligence, Machine Learning, Data Science, and related emerging technologies to address complex real-world challenges.
- PEO 2: Enable graduates to design, develop, and deploy intelligent data-driven solutions using modern computational tools, analytical techniques, and research methodologies.
- PEO 3: Prepare graduates for successful careers in industry, academia, entrepreneurship, and multidisciplinary research through continuous learning and professional development.
- PEO 4: Foster ethical values, leadership qualities, teamwork, effective communication, and social responsibility while applying Artificial Intelligence and Data Science solutions.
- PEO 5: Encourage innovation, research aptitude, publication culture, intellectual property creation, and lifelong learning to contribute to technological advancement and societal development.
Program Outcomes (POs)
- PO1 – Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to solve complex engineering problems.
- PO2 – Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems to reach substantiated conclusions using principles of mathematics, natural sciences, and engineering sciences.
- PO3 – Design/Development of Solutions: Design solutions for complex engineering problems and develop systems, components, or processes that meet specified needs while considering public health, safety, societal, cultural, and environmental factors.
- PO4 – Conduct Investigations of Complex Problems: Use research-based knowledge and methodologies including experimentation, data analysis, interpretation, and synthesis to provide valid conclusions.
- PO5 – Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools, including modeling and prediction, while understanding their limitations.
- PO6 – The Engineer and Society: Apply contextual knowledge to assess societal, health, safety, legal, and cultural issues and understand the responsibilities relevant to professional engineering practice.
- PO7 – Environment and Sustainability: Understand the impact of engineering solutions in societal and environmental contexts and demonstrate knowledge of sustainable development principles.
- PO8 – Ethics: Apply ethical principles and commit to professional ethics, responsibilities, and standards of engineering practice.
- PO9 – Individual and Team Work: Function effectively as an individual and as a member or leader in diverse teams and multidisciplinary environments.
- PO10 – Communication: Communicate effectively with the engineering community and society through reports, documentation, presentations, and clear instructions.
- PO11 – Project Management and Finance: Demonstrate knowledge of engineering and management principles and apply them to manage projects and work effectively in multidisciplinary teams.
- PO12 – Life-Long Learning: Recognize the need for and engage in independent and continuous learning to adapt to technological advancements and professional challenges.
Pragya AI Center of Excellence (CoE)
Empowering Innovation Through Artificial Intelligence, Research, and Industry-Oriented Learning
The Pragya AI Center of Excellence is dedicated to advancing knowledge, research, and practical applications of AI technologies. Through hands-on training, industry partnerships, and innovative projects, we prepare students to become future-ready professionals in the rapidly evolving world of Artificial Intelligence.