Overview
M.Sc. Data Science is a post-graduate degree program in the School of Basic and Applied Sciences of SGT University, which prepares advanced skills in the area of data-driven analysis, artificial intelligence, and intelligent decision support systems. The program combines the statistical background, advanced programming, machine learning, artificial intelligence, and industry-focused analytics and students can solve complicated real-life problems through data science practices.
The M.Sc. Data Science program takes 2 years (4 semesters) and adheres to a strict academic program that incorporates a balanced approach to theoretical in-depth, computational skills, research, and experience in the industry. It focuses on higher-level Python programming, machine learning, deep learning, database and query languages, and AI-driven analytics, which combine to equip graduates with professional practice and doctoral-level research.
The program will support responsible AI activities, reproducible research, and ethical use of data, as well as innovation, interdisciplinary thinking, and collaboration with the industry.
M.Sc. Data Science Modules
The M.Sc. Data Science curriculum is carefully structured to provide progressive learning from core concepts to advanced applications and industry integration. Key areas of course modules include:
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- Advanced Probability and Statistical Learning
- Advanced Python Programming for Data Science
- Machine Learning, Deep Learning and Artificial Intelligence
- Database Systems and Query Languages (SQL/NoSQL)
- Generative AI, Large Language Models, and Prompt Engineering
- Data Analytics & Applications (DAA) Laboratory with Industry Experts
- Research Methodology and Applied Data Science Project
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M.Sc. Data Science Subjects
The program includes theoretical courses, advanced laboratories, and applied research components. Some of the core subjects include:
- Advanced Statistical Methods for Data Science
- Advanced Python Programming and Scientific Computing
- Machine Learning Algorithms and Model Evaluation
- Artificial Intelligence and Intelligent Systems
- Deep Learning (CNNs, RNNs, Transformers)
- Database Management Systems and Query Languages
- Generative AI and Prompt Engineering Techniques
- Data Analytics and Applications (DAA) Lab (Industry-led)
- Research Methodology, Scientific Writing, and AI-assisted Research
- Dissertation / Industry-oriented Capstone Project
