Data Science

Core Courses

A)     Common Core Curriculum: First Four Semesters

Analysis 1 ,2 & 3

Algebra 1 , 2 & 3

Algorithms and Data Structures 1,2 & 3

Machine Structure

Graph Theory

Information Systems

Computer Architecture (CA)

Probability and Statistics

Introduction to Databases

Introduction to Networks

Theory of Languages

Operating Systems 1 & 2

Mathematical logic

Fondamentals of Electronics

Object-Oriented Programming 1 & 2 (OOP)

B)     Specialization Track (6 Semesters)

1. Foundations of Computing

• Compiler Design
• Software Engineering
• Numerical Methods
• High-Performance Computing (HPC)

2. Data & AI

• AI Foundations
• Machine Learning I & II
• Deep Learning
• Natural Language Processing (NLP)
• Generative AI
• Data Science Foundations: Theory and Practice

3. Advanced Systems & Security

• Advanced Networking
• Cloud Computing
• Blockchain
• Data Security

4. Data Engineering & Analytics

• Data Analysis
• Advanced Data Analysis
• Statistics for Data Science
• Big Data
• Data Visualization

     5. Optimization & Applied Methods

• Operational Research & Optimization
• Nature-Inspired Optimization Algorithms

    6. Development & Applications

• Advanced Software Development
• Advanced Web Application Development
• Digital Image Processing

   7. Management & Strategy

• Innovation Strategy
• Data Science Project Management

Advanced Topics

This program delves into cutting-edge domains through a rigorous blend of theory and applied research:

     1. Core Data Science

·        Advanced Machine Learning: Ensemble methods, Bayesian networks

·        Big Data Architectures: Hadoop/Spark ecosystems, distributed storage

·        Statistical Inference: Causal analysis, experimental design

     2. AI & Deep Learning

·        Generative AI: GANs, diffusion models, LLM fine-tuning

·        Computer Vision: CNN architectures, object detection

·        Reinforcement Learning: Markov decision processes

     3. Engineering Systems

·        MLOps: Model deployment, monitoring, CI/CD pipelines

·        Data Engineering: ETL optimization, stream processing (Kafka/Flink)

·        Cloud-Native AI: AWS SageMaker, Azure ML

 

    4. Specialized Applications

·        NLP in Production: Transformer deployment, RAG systems

·        Blockchain Analytics: Smart contract data forensics

·        IoT Data Fusion: Edge AI for sensor networks

     5. Governance & Strategy

·        AI Ethics: Bias mitigation, EU AI Act compliance

·        Data Governance: GDPR implementation frameworks

·        Tech Transfer: Commercializing academic research