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B.Sc (Hons.) Computer Science- AI Applications

B.Sc. (Hons.) Computer Science – AI Applications emphasizes on computer science fundamentals, artificial intelligence, machine learning, data science, algorithms, programming, intelligent systems, automation, and emerging AI-driven technologies used to build smart applications and real-world digital solutions.

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The proposed Bachelor of Computer Applications (B.Sc.) in Artificial Intelligence Applications is a 4-year undergraduate program designed to equip students with a strong foundation in computer science while enabling them to specialize in emerging areas of Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, and Intelligent Systems. This program aims to produce AI-savvy software professionals and solution developers capable of addressing complex business, industrial, and societal challenges.

 

The program is designed to:

  • Build a solid foundation in computer applications, programming, and data structures.

  • Introduce AI, ML, Data Science, and Deep Learning concepts integrated with practical use cases.

  • Train students to develop intelligent business solutions, automation tools, and smart applications.

  • Align with NEP 2020 by offering multiple entry/exit pathways (Certificate, Diploma, Degree, Honours with Research).

  • Foster employability across IT, analytics, AI product development, fintech, edtech, healthtech, and start-ups.

Course Features

A. Application-first AI journey (not just theory)

  • Strong programming and Computer Science foundation first (C, Data Structures, DBMS, OS, Java), then a guided transition into Python for AI applications, ML, Deep Learning, Generative AI, AI Agents, deployment, cloud and vector databases.

B. Hands-on learning with strong lab support

  • Dedicated labs across key technical subjects (C Lab, Data Structures Lab, DBMS Lab, OS Lab, Java Lab, Python Lab, Prompt Engineering Lab, ML Lab, AI Automation Lab, Generative AI Lab, etc.).

  • Build-oriented assessments: frequent practical tasks, mini-projects and demonstrations.

C. Modern AI stack coverage (career-aligned)

  • Prompt Engineering and AI Interaction Design for LLM-ready workflows.

  • AI Automation with no-code tools and practical pipelines.

  • Applied AI APIs and deployment fundamentals.

  • Cloud Computing for AI applications (AWS/GCP/Azure overview and practice).

  • Vector Databases for retrieval-based AI applications (RAG foundations).

  • Generative AI & Large Language Models plus AI Agents for real-world tasks.

D. Multiple career pathways via electives and domain options

  • Electives such as Creative Entrepreneurship, Digital Marketing (domain-focused), and AR/VR Applications.

  • Final-year minor options like AI in AR/VR & Gaming, AI in Digital Communication, and AI for Business Analytics.

E. Employability and holistic skill development

  • Employability Skills, experiential soft competency, communication, and professional development embedded across semesters.

F. Internship, Capstone and final-year tracks

  • Summer internship and later industry training to build workplace readiness.

  • Capstone Project Phase I followed by a choice of Research Track (Applied AI dissertation) or Industry Track (final project, portfolio and industrial internship).



Program Outcome

On successful completion of the program, students will be able to:

  • Apply programming skills (C/C++/Java/Python) to build complete software solutions.
  • Design efficient solutions using data structures, algorithms and database concepts.
  • Understand operating system fundamentals and system-level thinking relevant to deployments.
  • Build applied Machine Learning solutions and evaluate models using appropriate metrics and validation.
  • Develop Deep Learning solutions and connect them to practical use-cases.
  • Create effective prompts and AI interactions for LLM-based applications and workflows.
  • Build automation pipelines using AI + no-code tools and integrate them into business processes.
  • Deploy AI services using APIs and practical hosting patterns; understand cloud fundamentals for AI applications.
  • Work with vector databases to support retrieval-based AI applications.
  • Develop Generative AI applications and agent workflows for real tasks.
  • Communicate and collaborate professionally through labs, documentation, presentations and portfolios.
  • Demonstrate readiness for industry or research through internship and capstone/dissertation completion.

Course Details

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Course Curriculum

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Fees Structure

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Course Fact Sheet

Eligibility

10+2 (Any Stream) with minimum 45% score

8 Semester

(4 YEARS FULL TIME)

Tuition Fee

INR 60,000 (Per Semester**)