PROGRAM EDUCATIONAL OBJECTIVES - PEO

Immersive Systems Expertise: Graduates will design and develop advanced immersive systems using strong computer science foundations and modern interactive computing practices.

AI-enabled Immersive Innovation: Graduates will integrate AI and ML techniques into immersive applications to create intelligent, adaptive, and personalized experiences.

Research and Industry Leadership: Graduates will contribute to research and assume leadership roles in immersive tech domains including simulation, healthcare, education, manufacturing, and entertainment.

Ethics, Human-Centric Design, and Lifelong Learning: Graduates will practice responsible and inclusive immersive computing with attention to ethics, privacy, accessibility, and continuous professional learning.

PROGRAM OUTCOMES - PO

On completion of M.Sc. Computer Science (AI Based Immersive Technologies) programme, graduates will be able to:

Advanced Domain Knowledge: Apply advanced concepts in computing, mathematics, and informatics to solve real-world problems.

Problem Analysis and Solution Design: Analyze complex problems, define requirements, and design effective scalable solutions.

Research Methods and Scholarly Practice: Apply research methodologies, conduct literature review, formulate hypotheses, and follow rigorous experimental practices.

Advanced Tool Usage and System Development: Utilize modern computing tools, platforms, and architectures to develop, test, and deploy advanced solutions.

Model Evaluation and Evidence-Based Decisions: Design evaluation strategies, interpret results, and make evidence-based decisions using appropriate metrics and validation methods.

Ethics, Governance, and Societal Impact: Demonstrate professional ethics and responsible practices, considering privacy, fairness, sustainability, and societal impact.

Communication, Leadership, and Collaboration: Communicate technical ideas effectively, lead and participate in multidisciplinary teams, and collaborate professionally.

Innovation and Lifelong Learning: Demonstrate innovative thinking and engage in continuous learning to adapt to emerging technologies.

PROGRAM SPECIFIC OUTCOME - PSO

Immersive Experience and Interactive Design: Create immersive experiences using interaction design, HCI principles, usability evaluation, and accessibility standards for immersive applications.

Real-time Immersive System Performance and Optimization: Optimize immersive applications for real-time constraints and validate performance across platforms.

AI-Driven Immersive Perception and Intelligent Interaction: Develop AI components for immersive environments and integrate them into working prototypes.

The Curriculum and Syllabus of computer science programme are designed in line with the current global needs. It is framed based on Outcome Based Education and guidelines. The syllabus facilitates students to inculcate independent designing skill, advanced technology, innovative research and social responsibility.

• M.sc. - Data Science Curriculum and Syllabus - Regulation 2023

PROGRAM EDUCATIONAL OBJECTIVES - PEO

Immersive Competence: Graduates will acquire strong foundations in computer science with integrated competence in AI concepts and immersive technologies.

Design and Build Immersive Solutions: Graduates will design and develop interactive, intelligent, and immersive applications for real-world needs in diverse domains.

Human-Centric and Ethical Technology Practice: Graduates will design and develop human-centered immersive solutions by adhering to ethical AI principles, ensuring accessibility, protecting privacy, and promoting sustainable practices.

Leadership and Lifelong Learning: Graduates will continuously upgrade skills and assume roles in industry, research, higher studies, or entrepreneurship in immersive and intelligent technologies..

PROGRAMME OUTCOMES - PO

On completion of B.Sc. Computer Science (AI Based Immersive Technologies) programme, graduates will be able to:

Foundational Computing Knowledge: Apply core computing concepts including data structures, algorithms, and computational principles to solve problems.

Programming and Software Development: Design, implement, test, and maintain software solutions using appropriate programming languages and development practices.

Problem Analysis and Solution Design: Analyze real-world problems, define requirements, and develop effective computing solutions.

Data and Intelligent Systems: Use data science and AI/ML concepts to build or apply intelligent computing solutions responsibly.

Modern Tools and Emerging Technologies: Select and use suitable modern computing tools, platforms, and emerging technologies relevant to the discipline.

Ethics, Sustainability, and Social Impact: Demonstrate and adopt professional ethics, follow cyber regulations, and evaluate societal and sustainability impacts of computing solutions.

Communication and Teamwork: Communicate effectively and work as an individual / member / leader in diverse teams.

Lifelong Learning: Recognize the need for continuous learning and adapt to technological change.

PROGRAM SPECIFIC OUTCOMES - PSO

User Interaction and Experience Design: Design immersive user experiences with interaction design principles, usability evaluation, and accessibility considerations for immersive applications.

AI-enabled Immersive Computing: Integrate AI components including perception, gesture / voice interfaces, personalization, and intelligent agents into immersive applications for enhanced human-digital interaction.

Immersive Application Deployment and Optimization: Develop and deploy immersive applications on suitable platforms / devices with performance optimization, testing, and responsible usage practices.

PROGRAM EDUCATIONAL OBJECTIVES - PEO

Advanced Analytics Careers and Practice: Graduates will excel in advanced data science roles by applying modern analytics, AI and ML, and computing practices to domain problems.

Research-Driven Thinking: Graduates will pursue research-oriented problem solving, contributing to scholarly work and innovation in data science and allied fields.

Responsible and Ethical Data Practice: Graduates will uphold ethical standards in data handling, governance, privacy, fairness, and compliance, delivering socially responsible solutions.

Professional Growth and Lifelong Learning: Graduates will continuously upgrade skills through higher studies and adapt to evolving tools, platforms, and research trends.

PROGRAMME OUTCOMES - PO

On completion of M.Sc. Data Science programme, graduates will be able to:

Advanced Domain Knowledge: Apply advanced concepts in computing, mathematics, and informatics to solve real-world problems.

Problem Analysis and Solution Design: Analyze complex problems, define requirements, and design effective scalable solutions.

Research Methods and Scholarly Practice: Apply research methodologies, conduct literature review, formulate hypotheses, and follow rigorous experimental practices.

Advanced Tool Usage and System Development: Utilize modern computing tools, platforms, and architectures to develop, test, and deploy advanced solutions..

Model Evaluation and Evidence-Based Decisions: Design evaluation strategies, interpret results, and make evidence-based decisions using appropriate metrics and validation methods.

Ethics, Governance, and Societal Impact: Demonstrate professional ethics and responsible practices, considering privacy, fairness, sustainability, and societal impact.

Communication, Leadership, and Collaboration: Communicate technical ideas effectively, lead and participate in multidisciplinary teams, and collaborate professionally.

Innovation and Lifelong Learning: Demonstrate innovative thinking and engage in continuous learning to adapt to emerging technologies.

PROGRAMME SPECIFIC OUTCOMES - PSO

Data Engineering and Operational Pipelines: Design and implement end-to-end data pipelines suitable for institutional and industry-scale use.

Advanced Statistical Experimentation and Reasoning: Design and execute experiments, apply statistical reasoning, and produce defensible conclusions for decision-making.

Responsible Deployment and Monitoring Solutions: Deploy data science solutions with monitoring and governance, and produce interpretable, audit-ready analytics outputs..