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M-SC in Mathematics And Computing at Indian Institute of Technology (Indian School of Mines), Dhanbad
Dhanbad, Jharkhand
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About the Specialization
What is Mathematics and Computing at Indian Institute of Technology (Indian School of Mines), Dhanbad Dhanbad?
This M.Sc. Mathematics and Computing program at IIT (ISM) Dhanbad focuses on developing advanced mathematical and computational skills essential for contemporary technology and research. It bridges theoretical mathematics with practical computing applications, preparing students for roles in data science, artificial intelligence, financial modeling, and scientific computing, all highly sought after in India''''s booming tech sector.
Who Should Apply?
This program is ideal for engineering or science graduates with a strong foundation in mathematics, seeking to deepen their analytical and computational expertise. It targets fresh graduates aiming for cutting-edge technology roles and working professionals in IT or R&D looking to upskill with advanced mathematical tools and computing paradigms relevant to India''''s digital transformation journey.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles as data scientists, machine learning engineers, quantitative analysts, and research scientists. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly more. The strong theoretical foundation combined with practical skills positions them for rapid growth in Indian and international tech companies, often aligning with professional certifications in AI/ML or data analytics.
Student Success Practices
Foundation Stage
Master Core Mathematical Concepts- (Semester 1-2)
Dedicate significant time to thoroughly understand advanced abstract algebra, real analysis, and complex analysis. These form the bedrock for all subsequent computational and theoretical courses. Form study groups to discuss complex problems and clarify doubts, focusing on rigorous problem-solving techniques.
Tools & Resources
NPTEL courses for relevant topics, Standard textbooks (e.g., Dummit & Foote for Algebra, Rudin for Analysis), Online problem-solving platforms like Stack Exchange for mathematical queries
Career Connection
A strong grasp of fundamentals is crucial for advanced problem-solving in data science, quantitative finance, and research roles, enabling deeper understanding and innovation in algorithms and models.
Build Robust Programming Skills- (Semester 1-2)
Beyond coursework, actively practice C/C++ programming and data structures by solving competitive programming problems. Focus on efficient algorithm implementation and understanding time/space complexity. This proactive approach will solidify your coding prowess for technical interviews.
Tools & Resources
CodeChef, HackerRank, LeetCode for practice, GeeksforGeeks for concepts and solutions, Visual Studio Code or any modern IDE
Career Connection
Essential for cracking technical interviews for software development, data engineering, and machine learning engineer roles in India''''s competitive IT job market, and for building practical applications.
Engage in Early Research Exploration- (Semester 1-2)
Attend departmental seminars, read introductory research papers in areas of interest (e.g., numerical analysis, algorithms). Proactively seek out faculty members for small projects or informal guidance to understand research methodologies and current trends.
Tools & Resources
Google Scholar, arXiv, ResearchGate, Departmental research group meetings
Career Connection
Develops critical thinking, academic writing, and research aptitude, valuable for higher studies (PhD) or R&D roles in technology companies and specialized government labs in India.
Intermediate Stage
Specialization through Electives and Applied Projects- (Semester 3)
Strategically choose electives that align with your long-term career goals (e.g., Machine Learning for AI roles, Optimization Techniques for Quant roles). Apply learned concepts by undertaking mini-projects or term papers related to these electives, building a practical portfolio.
Tools & Resources
Kaggle for datasets and competitions, GitHub for project hosting, Python libraries (NumPy, SciPy, Pandas, Scikit-learn, TensorFlow/PyTorch) for implementation
Career Connection
Builds a specialized portfolio and practical experience, making you a more attractive candidate for targeted roles in areas like AI/ML, data analytics, or cybersecurity within Indian startups and MNCs.
Network and Seek Industry Internships- (Semester 3)
Actively participate in workshops, conferences, and career fairs organized by the institute. Connect with alumni and industry professionals on LinkedIn. Secure a summer internship (if applicable to a 2-year MSc) to gain real-world exposure and apply academic knowledge in a professional setting.
Tools & Resources
LinkedIn, Company career portals, IIT ISM''''s Training and Placement Cell, Industry-specific professional meetups
Career Connection
Crucial for practical experience, understanding industry demands, and often leads to pre-placement offers (PPOs) in Indian companies, significantly streamlining the job search process.
Prepare for National Level Examinations/Advanced Studies- (Semester 3-4)
If considering higher studies (PhD) in India or specific government/PSU roles, begin targeted preparation for competitive exams like UGC-NET/CSIR-NET, GATE, or other specialized tests, leveraging your strong M.Sc. foundation.
Tools & Resources
Previous year question papers and mock tests, Specific coaching materials if deemed necessary, NPTEL for in-depth subject revision and conceptual clarity
Career Connection
Opens pathways for academic careers, research positions in national labs, or specialized roles in public sector organizations in India, providing diverse career options beyond corporate roles.
Advanced Stage
Execute a High-Impact Project- (Semester 4)
The final project (MAC 604) should be a culmination of your learning. Choose a challenging problem, ideally with real-world implications or research potential. Document your work meticulously, aiming for a robust prototype or a research paper submission.
Tools & Resources
Project management tools (Trello, Jira), Version control systems (Git), Cloud platforms (AWS, Azure, GCP) for scalable implementations, LaTeX for professional thesis writing
Career Connection
A strong, well-documented project serves as a powerful resume booster and an impressive talking point during interviews, directly showcasing your problem-solving, innovation, and implementation skills to potential Indian employers.
Refine Interview Skills and Portfolio- (Semester 4)
Intensively practice technical questions, behavioral interviews, and quantitative aptitude. Build a comprehensive portfolio of your projects on GitHub and curate a professional resume. Actively participate in mock interviews and group discussions offered by the placement cell.
Tools & Resources
Online interview platforms (Pramp, InterviewBit), Career services at IIT ISM Dhanbad, Professional resume builders, Optimized LinkedIn profile
Career Connection
Directly prepares you for the placement season, significantly increasing your chances of securing a desirable job offer from top Indian companies and leading MNCs, ensuring a smooth transition into your career.
Cultivate Domain-Specific Expertise & Mentorship- (Semester 4)
Continuously engage with thought leaders in your chosen domain through conferences, webinars, and online communities. Seek mentorship from faculty or industry experts to gain deeper insights into career growth trajectories and emerging trends specific to the Indian market and global landscape.
Tools & Resources
Industry association memberships (e.g., ACM India), Specialized online communities (e.g., Kaggle forums, Stack Overflow), Alumni network platform
Career Connection
Fosters lifelong learning and professional development, providing a competitive edge, and guides your long-term career trajectory within the evolving Indian technology landscape, ensuring continuous relevance and growth.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed a three-year/four-year Bachelor’s degree with Mathematics as one of the subjects/major/core subject for at least two years/four semesters. Minimum 60% aggregate marks (6.0 on a 10-point scale) for General/EWS/OBC-NCL candidates and 55% aggregate marks (5.5 on a 10-point scale) for SC/ST/PwD candidates in the qualifying degree. Candidates must have qualified JAM 2024 in Mathematics (MA) or Mathematical Statistics (MS) subject.
Duration: 4 semesters / 2 years
Credits: 74 Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 501 | Advanced Abstract Algebra | Core | 4 | Group Theory, Ring Theory, Field Theory, Modules |
| MAC 503 | Real Analysis | Core | 4 | Metric Spaces, Continuity and Differentiability, Riemann-Stieltjes Integral, Sequences and Series of Functions, Functions of Several Variables |
| MAC 505 | Programming using C & Data Structures | Core | 4 | C Programming Fundamentals, Pointers and Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs |
| MAC 507 | Computer Organization & Architecture | Core | 4 | Digital Logic Circuits, Computer Arithmetic, CPU Organization, Memory System, Input/Output Organization |
| MAC 509 | Data Structures Lab | Lab | 2 | Implementation of Stacks and Queues, Linked List Operations, Tree Traversals, Sorting and Searching Algorithms, Graph Representations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 502 | Advanced Linear Algebra | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Canonical Forms, Inner Product Spaces |
| MAC 504 | Complex Analysis | Core | 4 | Complex Numbers and Functions, Analytic Functions, Complex Integration, Series Expansions, Residue Theorem |
| MAC 506 | Design & Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms, NP-Completeness |
| MAC 508 | Operating Systems | Core | 4 | OS Structures, Process Management, CPU Scheduling, Memory Management, File Systems, I/O Systems |
| MAC 510 | Algorithms Lab | Lab | 2 | Implementation of Sorting and Searching, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithms, Data Structure Applications |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 601 | Functional Analysis | Core | 4 | Normed Linear Spaces, Banach Spaces, Hilbert Spaces, Linear Operators, Dual Spaces |
| MAC 603 | Database Management Systems | Core | 4 | Relational Model, SQL Query Language, ER Modeling and Normalization, Transaction Management, Concurrency Control |
| MAC XXX | Elective I | Elective | 4 | Student''''s choice from approved list |
| MAC YYY | Elective II | Elective | 4 | Student''''s choice from approved list |
| MAC 605 | Database Lab | Lab | 2 | SQL Commands and Queries, Database Schema Design, PL/SQL Programming, Transaction Implementation, Relational Algebra Operations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 602 | Seminar | Project | 2 | Literature Review, Research Proposal, Presentation Skills, Technical Report Writing, Critical Analysis |
| MAC 604 | Project | Project | 10 | Problem Identification, System Design and Architecture, Implementation and Testing, Performance Evaluation, Project Documentation and Thesis |
| MAC ZZZ | Elective III | Elective | 4 | Student''''s choice from approved list |
| MAC WWW | Elective IV | Elective | 4 | Student''''s choice from approved list |
Semester subjects
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAC 611 | Numerical Analysis | Elective | 4 | Error Analysis, Solution of Nonlinear Equations, Interpolation and Approximation, Numerical Differentiation and Integration, Numerical Solution of ODEs |
| MAC 613 | Optimization Techniques | Elective | 4 | Linear Programming, Simplex Method, Duality Theory, Transportation and Assignment Problems, Non-linear Optimization |
| MAC 615 | Number Theory & Cryptography | Elective | 4 | Divisibility and Congruences, Prime Numbers and Factorization, Public Key Cryptography (RSA), Symmetric Key Cryptography (AES), Hash Functions and Digital Signatures |
| MAC 617 | Graph Theory | Elective | 4 | Basic Graph Concepts, Trees and Connectivity, Euler and Hamiltonian Graphs, Planar Graphs and Graph Coloring, Matchings and Coverings |
| MAC 619 | Advanced Data Structures | Elective | 4 | Balanced Trees (AVL, Red-Black), B-Trees, Heaps and Priority Queues, Hashing Techniques, Disjoint Set Union, String Algorithms |
| MAC 621 | Theory of Computation | Elective | 4 | Finite Automata and Regular Languages, Pushdown Automata and Context-Free Languages, Turing Machines, Decidability and Undecidability, Complexity Classes (P, NP) |
| MAC 623 | Advanced Operating Systems | Elective | 4 | Distributed Operating Systems, Network Operating Systems, Real-time Operating Systems, Mobile Operating Systems, Operating System Security |
| MAC 625 | Advanced Database Management Systems | Elective | 4 | Distributed Databases, Object-Oriented Databases, Data Warehousing Concepts, Data Mining Techniques, Big Data Technologies |
| MAC 627 | Artificial Intelligence | Elective | 4 | Introduction to AI Agents, Search Algorithms, Knowledge Representation and Reasoning, Machine Learning Fundamentals, Natural Language Processing Basics |
| MAC 629 | Computer Networks | Elective | 4 | OSI and TCP/IP Models, Physical Layer and Data Link Layer, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS) |
| MAC 631 | Distributed Systems | Elective | 4 | Distributed System Architectures, Interprocess Communication, Naming Services, Consistency and Replication, Fault Tolerance, Distributed System Security |
| MAC 633 | Software Engineering | Elective | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing and Maintenance, Software Project Management |
| MAC 635 | Data Mining & Warehousing | Elective | 4 | Data Preprocessing, Data Warehousing Architecture, OLAP Operations, Association Rule Mining, Classification and Clustering Techniques |
| MAC 637 | Machine Learning | Elective | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Reinforcement Learning, Neural Networks Fundamentals, Model Evaluation and Selection |
| MAC 639 | Information Security | Elective | 4 | Symmetric and Asymmetric Cryptography, Access Control Mechanisms, Network Security Protocols, Web Security, Malware and Vulnerabilities |
| MAC 641 | Internet of Things | Elective | 4 | IoT Architecture and Paradigms, IoT Communication Protocols, IoT Devices and Sensors, IoT Data Management, IoT Security and Privacy |
