M.Sc
Master of Science
M.Sc Syllabus 2026
The Master of Science (M.Sc) syllabus covers a structured programme spanning 2 Years designed to build both foundational knowledge and specialised expertise. The curriculum varies by specialisation, with 21 specialisations available including Applied Geology, Applied Geophysics, Applied Mathematics. Below is the detailed semester-wise subject breakdown and programme structure.
M.Sc Semester-wise Subjects
The M.Sc syllabus varies significantly by specialisation. Below is a representative overview of core and elective subjects across popular M.Sc specialisations:
M.Sc Physics
| Semester | Core Subjects |
|---|---|
| Semester 1 | Classical Mechanics, Mathematical Physics, Electronics, Quantum Mechanics I |
| Semester 2 | Statistical Mechanics, Electrodynamics, Quantum Mechanics II, Atomic & Molecular Physics |
| Semester 3 | Solid State Physics, Nuclear & Particle Physics, Elective I, Lab Course |
| Semester 4 | Elective II, Elective III, Project/Dissertation |
M.Sc Chemistry
| Semester | Core Subjects |
|---|---|
| Semester 1 | Inorganic Chemistry I, Organic Chemistry I, Physical Chemistry I, Mathematics for Chemists |
| Semester 2 | Inorganic Chemistry II, Organic Chemistry II, Physical Chemistry II, Analytical Chemistry |
| Semester 3 | Advanced Organic Synthesis, Spectroscopy, Elective I, Lab Course |
| Semester 4 | Elective II, Elective III, Project/Dissertation |
M.Sc Mathematics
| Semester | Core Subjects |
|---|---|
| Semester 1 | Real Analysis, Linear Algebra, Ordinary Differential Equations, Topology |
| Semester 2 | Complex Analysis, Abstract Algebra, Partial Differential Equations, Measure Theory |
| Semester 3 | Functional Analysis, Numerical Methods, Elective I, Elective II |
| Semester 4 | Elective III, Elective IV, Project/Dissertation |
M.Sc Biotechnology
| Semester | Core Subjects |
|---|---|
| Semester 1 | Molecular Biology, Biochemistry, Microbiology, Biostatistics |
| Semester 2 | Genetics, Immunology, Cell Biology, Bioinformatics |
| Semester 3 | Genetic Engineering, Industrial Biotechnology, Elective I, Lab Course |
| Semester 4 | Elective II, Plant/Animal Biotechnology, Research Project/Dissertation |
M.Sc Programme Structure & Credit Distribution
Programme Structure
The M.Sc programme follows a semester-based structure across two years with a progressive emphasis on specialisation and research:
| Year | Semester | Focus Area | Components |
|---|---|---|---|
| Year 1 | Semester 1 | Core Foundation | 4–5 core theory papers + practical labs |
| Semester 2 | Advanced Core | 4–5 core papers + practical labs + seminars | |
| Year 2 | Semester 3 | Specialisation & Electives | 2–3 core papers + 1–2 electives + lab course |
| Semester 4 | Research & Electives | 1–2 electives + dissertation/research project |
Credit Distribution (Typical)
| Component | Credits | Percentage |
|---|---|---|
| Core Theory Courses | 40–50 | 50–55% |
| Elective Courses | 12–16 | 15–18% |
| Laboratory / Practical | 12–16 | 15–18% |
| Dissertation / Project | 8–12 | 10–14% |
| Seminars & Workshops | 2–4 | 2–4% |
| Total | 80–96 | 100% |
Assessment Pattern
- Continuous Assessment (30–40%): Mid-semester exams, assignments, quizzes, lab reports
- End-Semester Exam (50–60%): Written theory examination
- Practical/Lab Exam (10–15%): Viva voce and practical assessment
- Dissertation (Semester 4): Evaluated through thesis submission, presentation, and viva by internal and external examiners
Skills Developed in M.Sc
Technical & Scientific Skills
- Advanced knowledge of subject-specific theories, principles, and methodologies
- Laboratory techniques: chromatography, spectroscopy, microscopy, cell culture, PCR, and other discipline-specific methods
- Data analysis using statistical software (R, SPSS, MATLAB, Python)
- Scientific computing, simulation, and modelling
- Instrumentation and equipment operation for experimental research
Research & Analytical Skills
- Research design, hypothesis formulation, and experimental methodology
- Critical evaluation of scientific literature and peer-reviewed publications
- Quantitative and qualitative data interpretation
- Scientific writing: research papers, reports, grant proposals
- Intellectual property awareness and ethical research practices
Digital & Computational Skills
- Bioinformatics tools and genomic databases (for Life Sciences)
- Programming in Python, R, C++, or MATLAB depending on specialisation
- Machine learning and data science applications in scientific research
- LaTeX for academic document preparation
Professional & Transferable Skills
- Scientific presentation and communication skills
- Team-based research collaboration and project management
- Critical thinking, problem-solving, and logical reasoning
- Time management and self-directed learning ability
M.Sc Specialisations
The syllabus and curriculum differ by specialisation. Explore all 21 available specialisations: