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B-TECH 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 Mathematics and Computing program at Indian Institute of Technology Indian School of Mines, Dhanbad focuses on equipping students with a robust foundation in mathematical theories alongside advanced computational skills. It is designed to meet the growing demand for professionals who can leverage analytical thinking and computational tools to solve complex real-world problems. The curriculum blends core mathematics with computer science principles, preparing graduates for diverse roles in India''''s technology and research sectors. This program distinguishes itself by integrating theoretical depth with practical application, making it highly relevant for the evolving Indian industry landscape.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into data science, artificial intelligence, and software development roles. It also suits working professionals from allied fields looking to upskill in quantitative analysis and computational techniques, or career changers aiming to transition into data-intensive industries. Specific prerequisite backgrounds typically include strong foundational knowledge in higher secondary mathematics, physics, and an inclination towards problem-solving through algorithmic approaches.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Scientists, Machine Learning Engineers, Quantitative Analysts, Software Developers, and Research Scientists across various sectors like IT, finance, and analytics. Entry-level salaries typically range from INR 7-15 LPA, with experienced professionals earning significantly more (INR 20-50+ LPA) in leading tech and financial companies. The program also prepares students for higher studies (M.Tech, PhD) and aligns with professional certifications in areas like AI/ML and data analytics, enhancing growth trajectories in Indian companies and contributing to technological advancements.
Student Success Practices
Foundation Stage
Strengthen Core Math and Programming Fundamentals- (Semester 1-2)
Dedicate significant time to mastering foundational mathematics (Calculus, Linear Algebra) and programming concepts (C/C++, Python). Regularly solve problems from textbooks and online platforms to build a strong analytical and logical base. Focus on understanding ''''why'''' behind concepts, not just ''''how'''', for robust problem-solving skills.
Tools & Resources
NPTEL courses for Mathematics and Programming, HackerRank, GeeksforGeeks, Khan Academy
Career Connection
A strong foundation is crucial for all advanced subjects and forms the bedrock for technical interviews in both mathematical and computing roles, significantly improving placement prospects in top Indian tech firms.
Cultivate Effective Study Habits and Peer Learning- (Semester 1-2)
Form study groups to discuss complex topics, solve assignments collaboratively, and clarify doubts with peers. Actively participate in class discussions and seek help from professors during office hours. Maintain a consistent study schedule to avoid last-minute cramming and ensure thorough understanding of engineering fundamentals.
Tools & Resources
Study groups, Professor office hours, Departmental common rooms, Online forums for doubt clearing
Career Connection
Developing strong teamwork and communication skills is vital for industry roles, and peer learning also enhances problem-solving abilities, which are highly valued by Indian and global recruiters.
Engage in Early Skill Building Workshops- (Semester 1-2)
Participate in introductory workshops on programming languages, data visualization, or basic software tools offered by the department or student clubs. This exposes students to practical applications early on and helps identify areas of interest for future specialization and project work.
Tools & Resources
IIT (ISM) technical clubs, Local hackathons, Online tutorials (Coursera, edX) for foundational tools
Career Connection
Early practical skills can open doors for basic internships and project opportunities, providing a competitive edge in the initial stages of career building within India''''s tech ecosystem.
Intermediate Stage
Apply Theoretical Knowledge to Practical Projects- (Semester 3-5)
Actively seek opportunities for mini-projects or research work that apply learned concepts in Data Structures, Algorithms, and Probability. Contribute to open-source projects or build small applications to gain hands-on experience and showcase skills beyond academic assignments.
Tools & Resources
GitHub for version control, Kaggle for data science projects, Departmental research labs, Faculty mentors
Career Connection
Practical projects demonstrate application skills to potential employers, making resumes more attractive for internships and entry-level positions in technology and data science sectors across India.
Network with Industry Professionals and Alumni- (Semester 3-5)
Attend industry seminars, guest lectures, and alumni meets organized by the institution. Leverage platforms like LinkedIn to connect with professionals in Mathematics and Computing fields. Seek mentorship and insights into career paths and industry trends specific to India''''s job market.
Tools & Resources
LinkedIn, Alumni network portal of IIT (ISM), Industry conferences/webinars
Career Connection
Networking is crucial for discovering internship opportunities, gaining industry insights, and securing referrals, which are significant advantages in the competitive Indian job market and help in navigating career choices.
Participate in Coding Competitions and Olympiads- (Semester 3-5)
Engage in competitive programming challenges on platforms like CodeChef, LeetCode, or participate in mathematical olympiads. This enhances problem-solving speed, logical thinking, and coding proficiency, which are critical skills for tech recruitment in India.
Tools & Resources
CodeChef, LeetCode, Google Kick Start, ACM ICPC for competitive programming
Career Connection
Success in these competitions is a strong indicator of technical prowess and is highly regarded by top tech companies during placements, often leading to direct interview calls and fast-tracked career growth.
Advanced Stage
Specialize and Build a Strong Portfolio- (Semester 6-8)
Focus on chosen areas of specialization (e.g., AI/ML, Data Science, Financial Computing) through advanced electives and online certifications. Develop a strong portfolio of projects, including the major project, demonstrating expertise in these niche areas for higher roles.
Tools & Resources
Specialized departmental electives, Coursera/edX certifications (e.g., Google AI, IBM Data Science), GitHub portfolio for showcasing projects
Career Connection
A specialized portfolio and advanced skills are essential for securing roles in specific high-demand domains, attracting higher salary packages, and showcasing readiness for advanced technical challenges in Indian companies and startups.
Engage in Comprehensive Placement Preparation- (Semester 6-8)
Begin rigorous preparation for technical interviews, aptitude tests, and group discussions well in advance. Practice coding problems, review core computer science and mathematics concepts, and refine communication skills. Utilize career services for mock interviews and resume building.
Tools & Resources
IIT (ISM) placement cell resources, GeeksforGeeks for interview prep, Mock interview platforms, Quantitative aptitude books
Career Connection
Thorough preparation is paramount for converting placement opportunities into job offers, especially for competitive roles in top-tier companies within the Indian IT, analytics, and financial sectors.
Pursue Research Opportunities or Capstone Projects- (Semester 6-8)
Leverage the final year''''s Major Project as a significant research opportunity. Aim for publications in conferences or journals, or develop an innovative solution to a real-world problem. This deepens understanding and builds research acumen for future endeavors.
Tools & Resources
Faculty research labs, Departmental funding for projects, Research publication platforms (e.g., arXiv, IEEE Xplore)
Career Connection
High-quality research or capstone projects can lead to direct entry into R&D roles, opportunities for higher studies (MS/PhD), or positions in innovation-driven companies in India and globally, setting a strong career trajectory.
Program Structure and Curriculum
Eligibility:
- Admission through JEE Advanced examination (specific cutoffs not part of syllabus document)
Duration: 8 semesters / 4 years
Credits: 177 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ML10101 | Engineering Graphics | Core | 3 | Introduction to Engineering Drawing, Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, CAD Fundamentals |
| EV10101 | Environmental Science | Core | 3 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Environmental Management, Global Environmental Issues, Sustainable Development |
| PH10101 | Physics | Core | 3 | Relativistic Mechanics, Quantum Physics, Solid State Physics, Electrodynamics and Magnetism, Lasers and Fiber Optics, Statistical Mechanics |
| CY10101 | Chemistry | Core | 3 | Atomic Structure and Bonding, Chemical Thermodynamics, Electrochemistry, Reaction Kinetics, Stereochemistry, Instrumental Methods of Analysis |
| MA10101 | Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Functions of Several Variables, Ordinary Differential Equations, Vector Calculus |
| EE10101 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines, Electrical Measuring Instruments |
| CS10101 | Introduction to Computing | Core | 3 | Programming Fundamentals, Conditional Statements, Loops and Arrays, Functions and Pointers, Structures and Unions, Basic Algorithms and Complexity |
| PH10102 | Physics Laboratory | Lab | 1 | Experiments on Optics, Experiments on Electricity, Experiments on Mechanics, Experiments on Thermal Physics |
| CY10102 | Chemistry Laboratory | Lab | 1 | Volumetric Analysis, Gravimetric Analysis, pH-metry, Conductometry, Spectroscopy |
| EE10102 | Basic Electrical Engineering Laboratory | Lab | 1 | Verification of Circuit Laws, Measurement of Electrical Quantities, Characteristics of Electrical Machines |
| CS10102 | Introduction to Computing Laboratory | Lab | 1 | Programming Exercises in C/C++, Debugging Techniques, Problem Solving through Coding |
| ML10102 | Workshop Practice | Lab | 1 | Fitting Operations, Carpentry Joints, Welding Techniques, Basic Machining Operations, Sheet Metal Work |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CE10101 | Engineering Mechanics | Core | 3 | Statics of Particles, Equilibrium of Rigid Bodies, Dynamics of Particles, Kinematics of Rigid Bodies, Kinetics of Rigid Bodies, Work and Energy |
| MA10201 | Mathematics-II | Core | 4 | Multivariable Calculus, Partial Differential Equations, Laplace Transforms, Fourier Series, Complex Analysis, Introduction to Probability and Statistics |
| MN10101 | Fluid Mechanics | Core | 3 | Fluid Properties, Fluid Statics, Fluid Kinematics, Fluid Dynamics, Laminar and Turbulent Flow, Boundary Layer Theory |
| EC10101 | Basic Electronics Engineering | Core | 3 | Semiconductor Diodes, Transistors (BJT, FET), Operational Amplifiers, Digital Logic Gates, Basic Amplifiers, Power Supplies |
| HS10101 | English for Communication | Core | 3 | Grammar and Vocabulary, Technical Writing, Public Speaking, Group Discussions, Presentation Skills, Report Writing |
| ML10103 | Material Science | Core | 3 | Crystalline Solids, Defects in Materials, Mechanical Properties of Materials, Phase Diagrams, Corrosion and Degradation, Polymers and Composites |
| ME10101 | Engineering Drawing | Core | 3 | Conventional Representation, Machine Elements, Assembly Drawings, Production Drawings, Sectional Views, Dimensioning and Tolerancing |
| EC10102 | Basic Electronics Engineering Laboratory | Lab | 1 | Experiments on Diodes and Rectifiers, Transistor Amplifier Characteristics, Operational Amplifier Applications, Digital Logic Gates Implementation |
| HS10102 | Communication Skills Laboratory | Lab | 1 | Language Proficiency Development, Public Speaking Practice, Group Discussion Techniques, Interview Skills Training |
| ME10102 | Manufacturing Practices | Lab | 1 | Foundry Operations, Welding Processes, Machining Operations, Forging and Forming, Sheet Metal Operations, Metrology and Inspection |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA20101 | Mathematics-III | Core | 4 | Linear Algebra (Vector Spaces, Matrices), Abstract Algebra (Groups, Rings), Real Analysis, Introduction to Functional Analysis, Integral Equations, Calculus of Variations |
| MC20101 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees (BST, AVL, B-Trees), Heaps and Priority Queues, Graphs (Traversal, Shortest Path), Hashing, Sorting and Searching Algorithms |
| MC20102 | Digital Logic Design | Core | 4 | Number Systems and Codes, Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Registers and Counters, Memory Units and PLDs |
| MC20103 | Discrete Mathematics | Core | 4 | Set Theory and Relations, Logic and Proof Techniques, Functions and Sequences, Combinatorics and Counting Principles, Graph Theory and Trees, Algebraic Structures (Groups, Rings, Fields) |
| MC20104 | Numerical Methods and Scientific Computing | Core | 4 | Solution of Algebraic Equations, Interpolation and Approximation, Numerical Differentiation and Integration, Numerical Solution of Ordinary Differential Equations, Eigenvalue Problems, Finite Difference Methods |
| MC20105 | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linked Lists and Stacks, Implementation of Queues and Trees, Graph Traversal Algorithms, Sorting and Searching Algorithms |
| MC20106 | Digital Logic Design Lab | Lab | 1 | Design of Combinational Circuits, Design of Sequential Circuits, FPGA based System Design, Verilog HDL Programming |
| HS201XX | Humanities Elective | Elective | 3 | Electives from disciplines like Economics, Psychology, Sociology, Philosophy |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MC20201 | Optimization Techniques | Core | 4 | Linear Programming and Simplex Method, Duality Theory, Transportation and Assignment Problems, Non-Linear Programming, Dynamic Programming, Queuing Theory |
| MC20202 | Operating Systems | Core | 4 | Operating System Structures, Process Management and CPU Scheduling, Inter-process Communication and Synchronization, Deadlocks, Memory Management and Virtual Memory, File Systems and I/O Systems |
| MC20203 | Computer Organization and Architecture | Core | 4 | Digital Logic Circuits, Data Representation and Arithmetic, Central Processing Unit (CPU) Design, Memory Hierarchy, Input/Output Organization, Pipelining and Parallel Processing |
| MC20204 | Probability and Statistics | Core | 4 | Probability Axioms and Conditional Probability, Random Variables and Distributions, Joint Probability Distributions, Sampling Distributions and Estimation, Hypothesis Testing, Regression and Correlation Analysis |
| MC20205 | Theory of Computation | Core | 4 | Finite Automata (DFA, NFA), Regular Expressions and Languages, Context-Free Grammars and Languages, Pushdown Automata, Turing Machines and Computability, Undecidability and Complexity Classes |
| MC20206 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management (fork, exec), Thread Programming and Synchronization, Memory Allocation Techniques |
| MC20207 | Computer Organization and Architecture Lab | Lab | 1 | Assembly Language Programming, CPU Simulation and Design, Memory Interfacing Experiments |
| OE202XX | Open Elective I | Elective | 3 | Electives from various engineering and science departments |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MC30101 | Applied Linear Algebra | Core | 4 | Vector Spaces and Subspaces, Linear Transformations and Matrices, Eigenvalues and Eigenvectors, Inner Product Spaces and Orthogonality, Matrix Decompositions (QR, SVD), Applications in Data Science and Machine Learning |
| MC30102 | Database Management Systems | Core | 4 | Introduction to DBMS and Data Models, Relational Model and Algebra, Structured Query Language (SQL), Database Design (ER Model, Normalization), Transaction Management and Concurrency Control, Database Recovery Systems |
| MC30103 | Design and Analysis of Algorithms | Core | 4 | Asymptotic Notations and Algorithm Analysis, Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, Graph Algorithms (Flows, Matching), NP-Completeness and Approximation Algorithms |
| MC30104 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical and Data Link Layer Protocols, Network Layer (IP Addressing, Routing Protocols), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS), Network Security Fundamentals |
| MC30105 | Database Management Systems Lab | Lab | 1 | SQL Querying and Database Manipulation, Database Design and Normalization, PL/SQL Programming, Database Administration Tasks |
| MC30106 | Computer Networks Lab | Lab | 1 | Socket Programming, Network Packet Analysis (Wireshark), Router and Switch Configuration, Network Security Tools and Techniques |
| MC301XX | Departmental Elective-I | Elective | 3 | Electives focused on advanced computing or mathematics topics |
| MC301XX | Departmental Elective-II | Elective | 3 | Electives focused on advanced computing or mathematics topics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MC30201 | Machine Learning | Core | 4 | Introduction to Machine Learning Paradigms, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, Dimensionality Reduction), Ensemble Methods and Boosting, Neural Networks and Deep Learning Fundamentals, Model Evaluation and Hyperparameter Tuning |
| MC30202 | Software Engineering | Core | 4 | Software Development Life Cycle Models, Requirements Engineering and Analysis, Software Design Principles and Patterns, Software Testing Techniques and Strategies, Software Project Management, Software Quality Assurance and Agile Methodologies |
| MC30203 | Computational Mathematics Laboratory | Lab | 1 | Numerical Methods Implementation in Python/MATLAB, Optimization Techniques Application, Statistical Data Analysis, Mathematical Modeling Exercises |
| MC30204 | Machine Learning Lab | Lab | 1 | Implementation of ML Algorithms using Scikit-learn, Data Preprocessing and Feature Engineering, Training and Evaluation of Classification Models, Introduction to Deep Learning Frameworks |
| MC302XX | Departmental Elective-III | Elective | 3 | Electives focused on advanced computing or mathematics topics |
| MC302XX | Departmental Elective-IV | Elective | 3 | Electives focused on advanced computing or mathematics topics |
| OE302XX | Open Elective II | Elective | 3 | Electives from various engineering and science departments |
| MC30205 | Summer Internship | Project | 2 | Industrial Project Work, Research Project Work, Report Writing, Presentation Skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MC40101 | Major Project Part-I | Project | 4 | Problem Identification and Formulation, Extensive Literature Review, System Design and Architecture, Initial Prototype Development, Project Planning and Management, Mid-term Project Report |
| MC401XX | Departmental Elective-V | Elective | 3 | Advanced topics in Mathematics and Computing, Specialized areas in Data Science, Cyber Security, AI, etc. |
| MC401XX | Departmental Elective-VI | Elective | 3 | Advanced topics in Mathematics and Computing, Specialized areas in Data Science, Cyber Security, AI, etc. |
| MC401XX | Departmental Elective-VII | Elective | 3 | Advanced topics in Mathematics and Computing, Specialized areas in Data Science, Cyber Security, AI, etc. |
| MC401XX | Departmental Elective-VIII | Elective | 3 | Advanced topics in Mathematics and Computing, Specialized areas in Data Science, Cyber Security, AI, etc. |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MC40201 | Major Project Part-II | Project | 8 | System Implementation and Development, Extensive Testing and Debugging, Performance Evaluation and Optimization, Thesis Writing and Documentation, Final Project Presentation and Defense, Deployment Strategies |
| MC402XX | Departmental Elective-IX | Elective | 3 | Advanced topics in Mathematics and Computing, Specialized areas in Data Science, Cyber Security, AI, etc. |
| MC402XX | Departmental Elective-X | Elective | 3 | Advanced topics in Mathematics and Computing, Specialized areas in Data Science, Cyber Security, AI, etc. |
