Academic Degree Structure • 160 Credit Framework

4-Year Curriculum & Unit Credit Matrix

Comprehensive Semester Syllabi Breakdown & University Credit Distribution

The Bachelor of Technology (B.Tech) program in Artificial Intelligence and Data Science is structured over eight academic semesters spanning four degree tiers. This matrix details the distribution of basic science, core engineering, professional electives, and capstone research requirements mandated by university accreditation standards.

Tier 1: First Year (FY) - Semester 1 & Semester 2

The foundational year establishes core competencies in computational programming, applied physical sciences, linear vector algebra, and discrete network architectures.

Semester 2 Core Credit Allocations

Fundamentals of Python Programming (FPP): 4 Credits (3 Hrs Theory + 2 Hrs Practical) — Object-oriented execution, file memory models, and exception handling.
Applied Engineering Physics: 4 Credits (3 Hrs Theory + 2 Hrs Practical) — Quantum wave mechanics, laser physics, and semiconductor devices.
Vector Calculus & Matrix Algebra: 4 Credits (3 Hrs Theory + 1 Hr Tutorial) — Eigenvalues, gradient fields, and vector line/surface integrals.
Introduction to Artificial Intelligence: 3 Credits (3 Hrs Theory) — State-space search, heuristic functions ($A^*$), and logic unification.
Foundations of Computer Security & Networks: 3 Credits (3 Hrs Theory) — OSI model, TCP/IP protocols, routing algorithms, and RSA cryptography.

Tier 2: Second Year (SY) - Semester 3 & Semester 4

The intermediate tier transitions students into low-level data structures, algorithmic complexity, relational database optimization, and operating system kernels.

              Academic Schema
              Credit Allocation System
            

Semester 3:
- Advanced Data Structures (ADS)      : 4 Credits | ETE Weightage: 60%
- Database Management Systems (DBMS)  : 4 Credits | ETE Weightage: 60%
- Discrete Mathematics (DMS)          : 3 Credits | ETE Weightage: 70%
- Digital Electronics & Logic         : 3 Credits | ETE Weightage: 60%

Semester 4:
- Operating Systems & Kernels (OS)    : 4 Credits | ETE Weightage: 60%
- Design & Analysis of Algorithms     : 4 Credits | ETE Weightage: 60%
- Theory of Computation (TOC)         : 3 Credits | ETE Weightage: 70%
- Probability & Statistics for Data   : 3 Credits | ETE Weightage: 70%
            

Tier 3: Third Year (TY) - Semester 5 & Semester 6

The advanced tier focuses on specialized statistical machine learning models, deep learning neural networks, distributed cloud compute frameworks, and big data streaming pipelines.

Tier 4: Final Year - Semester 7 & Semester 8

The terminal year is dedicated to applied industry capstone projects, natural language processing (NLP), generative LLM fine-tuning, computer vision systems, and peer-reviewed thesis publications.

"A well-structured engineering curriculum ensures that students master physical and mathematical fundamentals before tackling abstract algorithmic pipelines." — Sarthak Pawar, Lead Curator