B.Tech AI & Data Science Curriculum Pathway Map

Specialization Pathway

Highlight Domain

Completed Semesters

Total Earned / Target Credits 160 / 160 ✓ Accredited AICTE / ABET Standard

Hands-on Laboratory Hours 480 hrs Across 12 Practical Lab Modules

Math & Stats Foundation 22.5% Calculus, Linear Algebra, Probability

Core AI / ML Core Weight 38.7% Algorithms, Neural Nets, Vision, NLP

Legend:

Math & Stats

CS & Programming

Data Engineering

AI & Machine Learning

Elective Track

Capstone & Industry Lab

💡 Click any course card to inspect dependencies

CS-301 AI Core

Machine Learning Algorithms

Supervised, unsupervised, and reinforcement learning principles. Mathematical formulation of gradient descent, SVMs, decision trees, ensemble methods, and validation metrics.

Course Credit Weight

4 Credits

Prerequisites (Must Complete First)

✓ Linear Algebra & Optimization (MA-102)
✓ Probability & Statistics for Data (MA-201)
✓ Data Structures & Algorithms (CS-201)

Direct Downstream Dependents

➔ Deep Learning & Neural Architectures (CS-401)
➔ Natural Language Processing (CS-403)
➔ Capstone Industry Project I (PR-401)

Practicum Lab & Frameworks

Python 3.11 Scikit-learn NumPy/Pandas JupyterLab

Academic Architecture & AICTE / IEEE Curriculum Standard

How Data Science and Artificial Intelligence intersect across the 4-year undergraduate engineering trajectory.

1. Data Science Lifecycle (Extraction & Insight)

Covers data ingestion, noisy pipeline cleaning, distributed SQL/NoSQL storage (Hadoop, Spark), statistical variance testing, and business intelligence dashboards. Data Science fuels the feature store required for AI training.

2. AI Engineering (Autonomous Decisioning)

Focuses on mathematical optimization, backpropagation, convolutional and recurrent networks, transformer architectures, reinforcement learning agents, and model quantization for edge deployment.

3. Mathematical Rigor & Foundations

Unlike standard IT degrees, 22-25% of coursework is dedicated to Multivariable Calculus, Probability Density, Linear Algebra, and Discrete Math to ensure deep understanding of gradient optimization mechanics.