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B-SC-M-SC in Mathematics And Computing at National Institute of Technology Agartala
West Tripura, Tripura
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About the Specialization
What is Mathematics and Computing at National Institute of Technology Agartala West Tripura?
This Integrated B.Sc.+M.Sc. in Mathematics and Computing program at NIT Agartala focuses on a rigorous blend of advanced mathematical theories and modern computational techniques. In the rapidly evolving Indian tech landscape, graduates with strong analytical foundations combined with programming prowess are highly sought after across sectors like finance, data science, and scientific research. This program stands out by fostering a dual expertise critical for innovation in the digital era.
Who Should Apply?
This program is ideal for high-achieving 10+2 graduates with strong aptitude in Physics, Chemistry, and Mathematics, seeking entry into quantitative and computational fields. It also suits analytical minds aspiring for research careers or those aiming to build complex algorithms and data models. Students with a desire for a comprehensive, five-year academic journey culminating in advanced problem-solving skills will find this program rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue diverse India-specific career paths in data analytics, machine learning engineering, quantitative finance, and scientific computing. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more (INR 15-30+ LPA). Growth trajectories are robust in Indian MNCs and startups, with opportunities to advance into lead data scientist, AI architect, or research roles.
Student Success Practices
Foundation Stage
Master Core Mathematical and Programming Fundamentals- (Semester 1-2)
Dedicate significant effort to building a solid understanding of calculus, linear algebra, discrete mathematics, and C/C++/Python programming. Focus on clarity of concepts rather than rote learning. Actively solve problems from textbooks and online platforms to reinforce understanding.
Tools & Resources
NPTEL courses for theoretical foundations, HackerRank, LeetCode for coding practice, GeeksforGeeks for concepts and solutions
Career Connection
A strong foundation in these areas is crucial for all subsequent advanced courses in mathematics and computing, directly impacting eligibility for advanced projects and entry-level technical roles in software development or analytics.
Develop Robust Problem-Solving Skills- (Semester 1-2)
Engage in competitive programming challenges and participate in university-level coding contests. Work through a variety of problems, breaking them down into smaller, manageable parts. Seek feedback on your approaches and learn from diverse problem-solving strategies.
Tools & Resources
CodeChef, TopCoder, Google Kick Start, ICPC (International Collegiate Programming Contest) participation
Career Connection
Excellent problem-solving abilities are highly valued by recruiters for roles in software engineering, algorithm development, and quantitative analysis, enhancing chances for top placements.
Cultivate Effective Peer Learning and Collaboration- (Semester 1-2)
Form study groups with peers to discuss challenging topics, review assignments, and prepare for exams. Collaborate on small coding projects or academic exercises. Teach concepts to others to solidify your own understanding.
Tools & Resources
WhatsApp groups, Discord servers for academic discussions, Collaborative coding platforms like Repl.it or Google Docs for shared notes
Career Connection
Teamwork and communication skills gained through peer learning are essential for success in professional environments, preparing students for collaborative industry projects and improving interpersonal skills for interviews.
Intermediate Stage
Build Practical Projects and Portfolios- (Semester 3-5)
Start working on individual or group projects that apply concepts learned in Data Structures, OOP, DBMS, and AI. Develop small applications, scripts, or data analysis tools. Document your projects thoroughly and host them on platforms like GitHub.
Tools & Resources
GitHub, VS Code, Python (libraries like Pandas, NumPy, Scikit-learn), SQL databases, Web frameworks (e.g., Flask/Django)
Career Connection
A strong project portfolio is critical for internships and placements, demonstrating practical skills and initiative to potential employers in the IT, data science, and software development sectors.
Seek Early Industry Exposure through Internships- (Summer breaks after Semester 4 and Semester 6)
Actively search for and apply to summer internships in relevant fields such as software development, data analytics, or quantitative research. Even short-term internships or virtual experiences can provide valuable insights and networking opportunities.
Tools & Resources
LinkedIn, Internshala, College placement cell, Company career pages
Career Connection
Internships offer invaluable real-world experience, help in career clarity, provide networking contacts, and often lead to pre-placement offers (PPOs) at top companies, significantly boosting placement prospects.
Specialize and Deepen Knowledge in Key Areas- (Semester 5-7)
Identify areas of interest within Mathematics and Computing (e.g., AI/ML, cryptography, optimization, financial mathematics) and take relevant electives. Supplement coursework with online certifications and advanced textbooks to gain specialized expertise.
Tools & Resources
Coursera, edX, Udemy for specialized courses, Advanced mathematics and computing textbooks, Research papers
Career Connection
Specialization makes you a more attractive candidate for specific roles and industries, such as AI/ML Engineer, Data Scientist, or Quantitative Analyst, allowing for targeted career development.
Advanced Stage
Engage in Research and Advanced Projects/Thesis- (Semester 8-10)
Work closely with faculty mentors on research projects, participate in departmental research activities, and embark on a significant final year project or thesis. Aim for conference presentations or journal publications if possible.
Tools & Resources
Research labs, University library resources, Academic search engines (Google Scholar, IEEE Xplore), LaTeX for report writing
Career Connection
Research experience is highly valued for postgraduate studies (Ph.D.) and R&D roles in industry, showcasing advanced problem-solving, critical thinking, and independent work capabilities.
Prepare Rigorously for Placements and Higher Studies- (Semester 8-10)
Systematically prepare for campus placements by practicing aptitude tests, technical interviews (data structures, algorithms, core subjects), and HR rounds. Simultaneously, if pursuing higher studies, prepare for competitive exams like GATE, GRE, or various entrance exams for M.Tech/Ph.D.
Tools & Resources
Mock interviews, Placement preparation books, Online platforms like InterviewBit, LeetCode, Company-specific preparation guides, GRE/GATE study materials
Career Connection
Comprehensive preparation directly translates into securing desirable job offers from top-tier companies or admission into prestigious national/international universities for advanced degrees.
Build a Professional Network and Personal Brand- (Throughout the program, intensified during Semester 7-10)
Attend workshops, seminars, and industry conferences to network with professionals and domain experts. Create a strong LinkedIn profile showcasing your skills, projects, and achievements. Engage in online technical communities and contribute to open-source projects.
Tools & Resources
LinkedIn, Technical meetups, Alumni networks, Open-source platforms (e.g., GitHub, GitLab)
Career Connection
A robust professional network can lead to referrals, mentorship opportunities, and insights into industry trends, opening doors to unadvertised jobs and future career growth.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics, Chemistry and Mathematics as compulsory subjects from a recognized Board/University, with minimum 75% marks (65% for SC/ST/PwD candidates) or be in the top 20 percentile in the 10+2 examination. Admission through JEE Main followed by JoSAA/CSAB counselling.
Duration: 10 semesters / 5 years
Credits: 202 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA101 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Multiple Integrals, Vector Calculus |
| CS101 | Problem Solving and Programming | Core | 4 | Introduction to Programming, Control Structures, Arrays, Functions, Pointers, Structures and Unions |
| PH101 | Engineering Physics | Core | 4 | Quantum Mechanics, Laser Physics, Fiber Optics, Wave Optics, Solid State Physics |
| PH102 | Engineering Physics Laboratory | Lab | 2 | Experiments on Optics, Electricity, Mechanics, Thermal Physics |
| EE101 | Basic Electrical Engineering | Core | 4 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Power Systems |
| EE102 | Basic Electrical Engineering Laboratory | Lab | 2 | Experiments on DC circuits, AC circuits, Power measurement, Motors |
| ME101 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sections of Solids, Development of Surfaces |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA102 | Engineering Mathematics-II | Core | 4 | Linear Algebra, Differential Equations, Laplace Transforms, Fourier Series, Partial Differential Equations |
| CS102 | Data Structures and Algorithms | Core | 4 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting, Searching |
| CH101 | Engineering Chemistry | Core | 4 | Water Chemistry, Electrochemistry, Corrosion, Fuel and Combustion, Polymers, Nanomaterials |
| CH102 | Engineering Chemistry Laboratory | Lab | 2 | Volumetric analysis, Instrumental methods, Polymer synthesis, Water analysis |
| EC101 | Basic Electronics Engineering | Core | 4 | Semiconductor Diodes, Transistors, Rectifiers, Amplifiers, Digital Electronics |
| EC102 | Basic Electronics Engineering Laboratory | Lab | 2 | Diode characteristics, Rectifiers, Transistor amplifiers, Logic gates |
| HM101 | English for Communication | Core | 3 | Communication skills, Grammar, Report Writing, Presentation Skills, Group Discussion |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Discrete Mathematics | Core | 4 | Set Theory, Logic, Relations, Functions, Graph Theory, Combinatorics, Boolean Algebra |
| MA202 | Numerical Methods | Core | 4 | Error Analysis, Solution of Equations, Interpolation, Numerical Integration, Numerical Differentiation, Solution of ODEs |
| CS201 | Object Oriented Programming | Core | 4 | Classes and Objects, Inheritance, Polymorphism, Abstraction, Encapsulation, Exception Handling |
| CS202 | Operating System | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks, Concurrency |
| EC201 | Digital Electronics | Core | 4 | Logic Gates, Boolean Algebra, Combinational Circuits, Sequential Circuits, Memories, A/D & D/A Converters |
| HM201 | Managerial Economics and Financial Accounting | Core | 3 | Demand and Supply, Market Structures, Macroeconomics, Financial Statements, Cost Accounting, Budgeting |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA203 | Abstract Algebra | Core | 4 | Groups, Rings, Fields, Vector Spaces, Modules, Ideals, Homomorphisms |
| MA204 | Real Analysis | Core | 4 | Metric Spaces, Sequences and Series of Functions, Riemann-Stieltjes Integral, Measure Theory, Lebesgue Integral |
| CS203 | Database Management System | Core | 4 | ER Model, Relational Model, SQL, Normalization, Transaction Management, Concurrency Control, Recovery |
| CS204 | Computer Networks | Core | 3 | Network Models, Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| MA205 | Probability and Statistics | Core | 4 | Probability Axioms, Random Variables, Distributions, Hypothesis Testing, Regression, Correlation |
| HS201 | Environmental Science | Core | 2 | Ecosystems, Biodiversity, Pollution, Renewable Energy, Environmental Impact Assessment |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA301 | Complex Analysis | Core | 4 | Complex Numbers, Analytic Functions, Contour Integration, Series Expansions, Residue Theorem |
| MA302 | Linear Programming | Core | 4 | Graphical Method, Simplex Method, Duality, Transportation Problem, Assignment Problem, Game Theory |
| CS301 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Undecidability |
| CS302 | Artificial Intelligence | Core | 4 | AI Agents, Search Algorithms, Knowledge Representation, Machine Learning, Neural Networks, Natural Language Processing |
| DE-I | Graph Theory | Elective | 3 | Basic Concepts, Trees, Connectivity, Eulerian & Hamiltonian Graphs, Coloring, Planar Graphs |
| HS301 | Indian Culture and Ethics | Core | 2 | Indian Philosophy, Ethics, Values, Heritage, Society, Contemporary Issues |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA303 | Functional Analysis | Core | 4 | Normed Spaces, Banach Spaces, Hilbert Spaces, Linear Operators, Dual Spaces, Spectral Theory |
| MA304 | Differential Geometry | Core | 4 | Curves, Surfaces, First and Second Fundamental Forms, Curvature, Geodesics, Manifolds |
| CS303 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression, Classification, Deep Learning |
| CS304 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Design, Testing, Maintenance, Project Management |
| DE-II | Data Analytics | Elective | 3 | Data Preprocessing, Exploratory Data Analysis, Predictive Modeling, Statistical Inference, Big Data Technologies |
| PROJ-I | Project-I | Project | 2 | Project Planning, Literature Survey, Problem Definition, Initial Implementation, Reporting |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA401 | Topology | Core | 4 | Topological Spaces, Open and Closed Sets, Continuity, Connectedness, Compactness, Product Spaces |
| MA402 | Fuzzy Set Theory and Its Applications | Core | 4 | Fuzzy Sets, Fuzzy Relations, Fuzzy Logic, Fuzzy Numbers, Fuzzy Optimization, Applications |
| CS401 | Data Mining and Data Warehousing | Core | 4 | Data Warehousing Concepts, OLAP, Data Mining Techniques, Association Rules, Classification, Clustering |
| DE-III | Cryptography | Elective | 3 | Classical Cryptography, Symmetric-key Cryptography, Asymmetric-key Cryptography, Hash Functions, Digital Signatures |
| OE-I | Introduction to Management | Open Elective | 3 | Management Principles, Functions, Organizational Structure, Leadership, Motivation, Decision Making |
| PROJ-II | Project-II | Project | 2 | Advanced Project Development, Research Methodology, System Design, Implementation, Testing |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA403 | Mathematical Modeling | Core | 4 | Introduction to Modeling, Difference Equations, Differential Equations, Optimization Models, Simulation, Case Studies |
| MA404 | Financial Mathematics | Core | 4 | Interest Rates, Derivatives, Options Pricing, Black-Scholes Model, Risk Management, Portfolio Optimization |
| DE-IV | Optimization Techniques | Elective | 3 | Linear Programming, Non-linear Programming, Integer Programming, Dynamic Programming, Metaheuristics |
| OE-II | Entrepreneurship Development | Open Elective | 3 | Entrepreneurial Process, Business Plan, Market Analysis, Funding, Legal Aspects, Innovation |
| PROJ-III | Project-III | Project | 3 | Advanced Research, Prototype Development, Comprehensive Testing, Presentation, Report Writing |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA501 | Advanced Numerical Techniques | Core | 4 | Numerical Solution of PDEs, Finite Difference Method, Finite Element Method, Spectral Methods, Error Analysis |
| MA502 | Advanced Algebra | Core | 4 | Field Extensions, Galois Theory, Modules over PID, Noetherian Rings, Commutative Algebra |
| DE-V | Advanced Graph Theory | Elective | 3 | Matching Theory, Network Flows, Spectral Graph Theory, Extremal Graph Theory, Random Graphs |
| DE-VI | Data Science for Business | Elective | 3 | Business Analytics, Data Visualization, Predictive Analytics, Prescriptive Analytics, Big Data Ecosystem |
| THESIS-A | Project/Thesis Part-A | Project | 5 | Literature Review, Problem Identification, Methodology Design, Initial Implementation, Progress Report |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA503 | Operator Theory | Core | 4 | Bounded Linear Operators, Compact Operators, Self-Adjoint Operators, Spectral Theorem, Banach Algebras |
| MA504 | Advanced Optimization | Core | 4 | Convex Optimization, Quadratic Programming, Interior-Point Methods, Global Optimization, Robust Optimization |
| DE-VII | Tensor Analysis | Elective | 3 | Tensors, Tensor Operations, Covariant and Contravariant Tensors, Riemannian Geometry, Applications |
| THESIS-B | Project/Thesis Part-B | Project | 6 | System Implementation, Experimental Evaluation, Result Analysis, Thesis Writing, Defense |
