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M-SC in Mathematics at SRM Institute of Science and Technology (Deemed to be University)
Chengalpattu, Tamil Nadu
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About the Specialization
What is Mathematics at SRM Institute of Science and Technology (Deemed to be University) Chengalpattu?
This M.Sc. Mathematics program at SRM Institute of Science and Technology focuses on equipping students with advanced mathematical knowledge and problem-solving skills crucial for research, academia, and industry. It emphasizes a blend of theoretical foundations and computational applications, addressing the growing demand for mathematical expertise in India''''s technology and data-driven sectors. The curriculum is designed to foster analytical thinking and innovation.
Who Should Apply?
This program is ideal for Bachelor''''s degree holders in Mathematics or related fields seeking entry into specialized roles requiring strong quantitative abilities. It also suits working professionals, such as educators or data analysts, aiming to enhance their mathematical foundations or transition into research and development. Individuals with a keen interest in logical reasoning and abstract concepts are well-suited.
Why Choose This Course?
Graduates of this program can expect to pursue diverse career paths in India, including data scientists, research analysts, quantitative financial analysts, and academicians. Entry-level salaries typically range from INR 4-7 LPA, with significant growth potential up to INR 15+ LPA for experienced professionals. The program provides a solid base for advanced studies and prepares students for competitive exams like NET/SET.
Student Success Practices
Foundation Stage
Master Core Mathematical Concepts- (Semester 1-2)
Dedicate significant time to thoroughly understand foundational subjects like Real Analysis, Linear Algebra, and Abstract Algebra. Focus on proofs, definitions, and problem-solving techniques. Utilize textbooks, lecture notes, and online resources for deeper comprehension.
Tools & Resources
NPTEL courses, MIT OpenCourseware, Reference textbooks (e.g., Rudin, Hoffman & Kunze), Peer study groups
Career Connection
A strong grasp of fundamentals is essential for advanced topics, research, and for excelling in quantitative roles in finance, data science, and academia.
Develop Computational Skills with Python- (Semester 1-2)
Actively engage with the Python Programming course, applying mathematical concepts to coding exercises. Practice implementing algorithms from Data Structures and Algorithms using Python to build a robust computational foundation.
Tools & Resources
HackerRank, LeetCode, Jupyter Notebooks, NumPy, SciPy libraries
Career Connection
Proficiency in Python is highly valued in data science, quantitative finance, and scientific computing roles in Indian tech companies and startups.
Engage in Peer Learning and Problem Solving- (Semester 1-2)
Form study groups with peers to discuss challenging problems, review concepts, and teach each other. Collaboratively solve numerical and theoretical problems, preparing for continuous assessments and end-semester examinations.
Tools & Resources
Whiteboards, Online collaboration tools (e.g., Google Docs), Past year question papers
Career Connection
Enhances problem-solving abilities, communication skills, and builds a professional network, which are crucial for team-based projects and future job roles.
Intermediate Stage
Apply Theory to Real-World Problems- (Semester 3)
Focus on applying theoretical knowledge from Operations Research and Numerical Methods to practical scenarios. Seek out opportunities for mini-projects or case studies that involve optimization, simulation, or data analysis.
Tools & Resources
MATLAB, R, Excel Solver, Industry case studies, Kaggle datasets
Career Connection
Demonstrates practical problem-solving capabilities to potential employers in analytics, logistics, and research sectors, increasing employability.
Explore Elective Specializations Strategically- (Semester 3)
Carefully choose elective subjects that align with your career interests, such as Financial Mathematics, Data Analytics, or Cryptography. Deep dive into the chosen area to gain specialized knowledge and skills.
Tools & Resources
Online courses (Coursera, edX) in specific elective areas, Domain-specific journals, Faculty consultation
Career Connection
Specialization helps in targeting specific job roles, distinguishing you in the competitive Indian job market for roles like Quant Analyst, Data Scientist, or Cryptographer.
Network with Faculty and Industry Experts- (Semester 3)
Attend department seminars, workshops, and guest lectures. Actively interact with professors to discuss research interests and project ideas. Seek mentorship and build connections with visiting industry professionals.
Tools & Resources
LinkedIn, University career fairs, Department events
Career Connection
Opens doors to internship opportunities, research collaborations, and informs career choices, providing valuable insights into industry expectations and networking leads for placements.
Advanced Stage
Undertake a High-Impact Project Work- (Semester 4)
Choose a challenging research or application-oriented project in your area of interest. Work diligently on literature review, methodology, implementation, and rigorous analysis, culminating in a well-written thesis and a strong presentation for the viva.
Tools & Resources
LaTeX for thesis writing, Academic databases (JSTOR, Scopus), Statistical software (e.g., R, Python libraries)
Career Connection
A strong project acts as a portfolio piece, showcasing independent research capabilities and problem-solving skills, crucial for R&D roles, PhD applications, and technical interviews.
Prepare Rigorously for Placements/Higher Studies- (Semester 4)
Begin early preparation for campus placements by honing interview skills, quantitative aptitude, and technical knowledge. Alternatively, if pursuing higher education, prepare for entrance exams like NET/SET or international GRE/TOEFL.
Tools & Resources
Placement training modules, Mock interviews, Online aptitude tests, Previous year''''s question papers for competitive exams
Career Connection
Directly impacts securing desirable job offers in leading Indian companies or gaining admission to prestigious PhD programs or research institutions.
Develop Professional Communication and Presentation Skills- (Semester 4)
Practice presenting your project work, research findings, and technical concepts clearly and concisely. Participate in departmental colloquia or student conferences to refine public speaking and scientific communication.
Tools & Resources
Presentation software (PowerPoint, Google Slides), Toastmasters International (if available), Feedback from faculty and peers
Career Connection
Essential for successful project defense, job interviews, client presentations, and effective communication in any professional or academic setting.
Program Structure and Curriculum
Eligibility:
- A pass in Bachelor’s degree (B.Sc.) in Mathematics / Applied Mathematics / Mathematics with Computer Applications (full time) with an aggregate of 50% marks / A pass in B.Ed. with Mathematics as one of the subjects with an aggregate of 50% marks.
Duration: 4 semesters / 2 years
Credits: 85 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PGM23101 | Real Analysis | Core | 4 | Set theory and functions, Metric spaces and completeness, Continuity and uniform continuity, Riemann-Stieltjes Integral, Sequences and series of functions |
| PGM23102 | Linear Algebra | Core | 4 | Vector spaces and subspaces, Linear transformations and matrices, Eigenvalues and Eigenvectors, Inner product spaces, Bilinear forms and quadratic forms |
| PGM23103 | Ordinary Differential Equations | Core | 4 | Linear differential equations with constant coefficients, System of ordinary differential equations, Existence and uniqueness of solutions, Boundary value problems, Oscillation theory and Sturm-Liouville problems |
| PGM23104 | Data Structures and Algorithms | Core | 4 | Introduction to data structures, Arrays, linked lists, stacks, queues, Trees and graphs, Sorting and searching algorithms, Algorithm analysis and complexity |
| PGM23105 | Probability Theory | Core | 4 | Axiomatic definition of probability, Random variables and distributions, Expectation and moments, Moment generating functions, Limit theorems |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PGM23201 | Complex Analysis | Core | 4 | Complex numbers and functions, Analytic functions and Cauchy-Riemann equations, Conformal mapping, Cauchy''''s integral theorems, Residue calculus and applications |
| PGM23202 | Abstract Algebra | Core | 4 | Group theory and homomorphisms, Rings and ideals, Fields and field extensions, Modules and vector spaces, Galois theory basics |
| PGM23203 | Partial Differential Equations | Core | 4 | First order partial differential equations, Second order linear PDEs, Classification of PDEs, Wave equation and heat equation, Laplace equation and boundary value problems |
| PGM23204 | Graph Theory | Core | 4 | Basic graph concepts and definitions, Trees and connectivity, Euler and Hamiltonian graphs, Graph coloring and planar graphs, Network flows and matching |
| PGM23205 | Python Programming | Core | 4 | Python fundamentals and data types, Control flow and functions, Object-oriented programming in Python, File I/O and exception handling, Introduction to scientific libraries (NumPy, Pandas) |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PGM23301 | Functional Analysis | Core | 4 | Normed linear spaces and Banach spaces, Hilbert spaces and orthonormal sets, Bounded linear operators, Hahn-Banach theorem, Spectral theory of operators |
| PGM23302 | Operations Research | Core | 4 | Linear programming and graphical method, Simplex method and duality, Transportation and assignment problems, Network models (PERT/CPM), Game theory and queuing theory |
| PGM23303 | Numerical Methods | Core | 4 | Solutions of algebraic and transcendental equations, Interpolation and approximation, Numerical differentiation and integration, Numerical solution of ordinary differential equations, Numerical solution of partial differential equations |
| PGM23304 | Cryptography | Core | 4 | Classical cryptosystems, Block ciphers and stream ciphers, Public key cryptography (RSA, ElGamal), Key management and digital signatures, Hash functions and message authentication |
| PGM23EL101 | Discrete Mathematics | Elective Choice I or II | 4 | Set theory and logic, Counting principles and combinatorics, Recurrence relations, Relations and functions, Basic graph theory |
| PGM23EL102 | Mathematical Statistics | Elective Choice I or II | 4 | Sampling distributions, Point estimation and interval estimation, Hypothesis testing, Linear regression and correlation, Non-parametric methods |
| PGM23EL103 | Latex for Scientific Documentation | Elective Choice I or II | 4 | Basic LaTeX document structure, Text formatting and font management, Mathematical typesetting and equations, Tables, figures, and cross-referencing, Presentations with Beamer |
| PGM23EL104 | Fuzzy Logic | Elective Choice I or II | 4 | Fuzzy sets and membership functions, Fuzzy relations and operations, Fuzzy arithmetic and fuzzy numbers, Fuzzy logic and approximate reasoning, Fuzzy control systems |
| PGM23EL105 | Algebraic Topology | Elective Choice I or II | 4 | Topological spaces and continuous functions, Homotopy and path components, Fundamental group, Covering spaces, Simplicial homology groups |
| PGM23EL106 | Financial Mathematics | Elective Choice I or II | 4 | Interest rates and time value of money, Stochastic calculus basics, Options and futures contracts, Black-Scholes model for option pricing, Portfolio theory and risk management |
| PGM23EL107 | Number Theory | Elective Choice I or II | 4 | Divisibility and prime numbers, Congruences and modular arithmetic, Diophantine equations, Quadratic reciprocity, Applications to cryptography |
| PGM23EL108 | Mathematical Biology | Elective Choice I or II | 4 | Population dynamics models, Epidemic models (SIR, SIS), Enzyme kinetics and biochemical reactions, Compartmental models, Mathematical methods in neuroscience |
| PGM23EL109 | Data Analytics for Mathematics | Elective Choice I or II | 4 | Data visualization and exploration, Statistical inference for data analysis, Introduction to machine learning, Regression and classification techniques, Big data concepts |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PGM23401 | Measure and Integration | Core | 4 | Measure spaces and outer measure, Lebesgue measure and measurable functions, Lebesgue integral, Fatou''''s lemma and dominated convergence theorem, Lp spaces and completeness |
| PGM23402 | Calculus of Variations and Integral Equations | Core | 4 | Variational problems and Euler-Lagrange equation, Isoperimetric problems, Fredholm integral equations, Volterra integral equations, Applications in physics and engineering |
| PGM23PJ01 | Project Work | Project | 12 | Problem identification and literature review, Methodology development and data collection, Analysis and interpretation of results, Report writing and documentation, Research ethics and plagiarism |
| PGM23PJ02 | Project Viva-Voce | Project | 5 | Oral presentation of project findings, Defense of methodology and results, Answering questions from examiners, Demonstration of understanding, Communication of research significance |
