B-TECH in Mathematics And Computing at Manipal Academy of Higher Education
Udupi, Karnataka
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About the Specialization
What is Mathematics And Computing at Manipal Academy of Higher Education Udupi?
This Mathematics and Computing program at Manipal Academy of Higher Education focuses on integrating advanced mathematical theories with computational techniques. It addresses the growing need in the Indian industry for professionals who can leverage mathematical rigor to solve complex problems in fields like data science, artificial intelligence, and financial modeling. The program differentiates itself by providing a strong theoretical foundation coupled with extensive practical application. This interdisciplinary approach is highly relevant in India''''s booming digital economy, which demands a blend of analytical and technological expertise.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and an interest in computational problem-solving, seeking entry into high-tech analytical roles. It also suits working professionals looking to upskill in quantitative finance, machine learning, or data analytics, and career changers transitioning into data-intensive industries. Specific prerequisite backgrounds include a strong foundation in 10+2 level Physics and Mathematics. Students aspiring for research or advanced studies in computational mathematics will also find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Machine Learning Engineer, Quantitative Analyst, Research Scientist, and Software Developer in analytics roles. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly higher. Growth trajectories often lead to lead data scientist, AI architect, or research head positions in Indian and global firms. The curriculum also aligns with skills required for certifications in areas like data science and financial modeling, enhancing professional standing.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus on building a strong foundation in C/C++/Java/Python through consistent coding practice and understanding core data structures and algorithms. Participate in coding competitions to hone problem-solving skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, NPTEL courses on Data Structures
Career Connection
Essential for cracking technical interviews and building efficient software solutions, directly impacting placement readiness for tech roles.
Excel in Engineering Mathematics- (Semester 1-2)
Develop a deep understanding of Engineering Mathematics I & II, Linear Algebra, and Discrete Mathematics. Actively solve problems, attend doubt-clearing sessions, and form study groups to grasp complex concepts thoroughly.
Tools & Resources
Khan Academy, NPTEL lectures, standard textbooks (e.g., Erwin Kreyszig, B.S. Grewal), university-provided study materials
Career Connection
Provides the analytical backbone for advanced subjects like Machine Learning and Quantitative Finance, crucial for research and data science roles.
Enhance Communication and Soft Skills- (Semester 1-2)
Actively participate in communication skills labs, debate clubs, and public speaking events. Practice effective written and oral communication, essential for academic presentations and future professional interactions.
Tools & Resources
Toastmasters International clubs, online communication courses, university language labs, TED Talks
Career Connection
Improves interview performance, team collaboration, and client communication, making graduates more well-rounded and employable in diverse roles.
Intermediate Stage
Deep Dive into Core Computing & Math- (Semester 3-5)
Focus on specialized subjects like Database Management Systems, Operating Systems, Numerical Methods, and Probability & Statistics. Apply theoretical knowledge to practical projects and simulations. Explore open-source projects.
Tools & Resources
GitHub, Kaggle datasets, SQL platforms (e.g., MySQL, PostgreSQL), Linux command line, MATLAB/R/Python for numerical methods
Career Connection
Builds a robust profile for roles requiring strong technical foundations in software development, data management, and statistical analysis.
Engage in Mini-Projects and Internships- (Semester 4-5)
Undertake small-scale projects applying concepts from OOP, DBMS, or ML. Actively seek out summer internships in relevant fields (e.g., data analytics, software development) to gain industry exposure.
Tools & Resources
LinkedIn, Internshala, university career services, project-based online courses (e.g., Coursera, Udemy)
Career Connection
Practical experience is invaluable for placements, providing real-world context and demonstrating applied skills to potential employers.
Participate in Hackathons & Competitions- (Semester 4-5)
Join hackathons, data science challenges, and mathematical modeling competitions. This fosters innovative problem-solving, teamwork, and provides a platform to showcase skills beyond academics.
Tools & Resources
HackerEarth, Kaggle, university-organized tech fests and competitions
Career Connection
Builds a strong portfolio, demonstrates initiative, and offers networking opportunities with industry professionals and peers, enhancing job prospects.
Advanced Stage
Specialize with Advanced Electives & Research- (Semester 6-7)
Choose program and open electives strategically to specialize in areas like Deep Learning, Financial Mathematics, or Advanced Data Structures. Engage in research projects with faculty to explore complex topics.
Tools & Resources
Research papers (IEEE Xplore, ACM Digital Library), specialized software (TensorFlow, PyTorch, R, SAS), university research labs
Career Connection
Develops deep expertise in niche areas, making graduates highly sought after for specialized roles in R&D, quantitative finance, and advanced AI.
Focus on Industry-Ready Capstone Project- (Semester 7-8)
Dedicate significant effort to the Project Work (Phase I & II) and Internship. Aim for a challenging, industry-relevant project that showcases comprehensive skills acquired throughout the program.
Tools & Resources
Version control (Git), project management tools (Jira, Trello), collaboration platforms, industry mentors
Career Connection
The capstone project and internship are often the strongest talking points in interviews, demonstrating practical problem-solving and readiness for professional roles.
Intensive Placement Preparation & Networking- (Semester 7-8)
Begin focused preparation for placements well in advance. Practice aptitude, technical, and HR interview questions. Network with alumni and industry professionals through workshops, seminars, and LinkedIn.
Tools & Resources
InterviewBit, Glassdoor, company-specific preparation guides, MAHE alumni network, career development cell
Career Connection
Maximizes chances of securing desirable job offers by being fully prepared for the recruitment process and leveraging professional connections.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 10+2 or A Level or IB or an equivalent examination with Physics, Mathematics and English as compulsory subjects, along with Chemistry or Biotechnology or Biology or Technical Vocational subject as an optional subject from a recognized Board, with a minimum of 50 % marks in Physics, Mathematics and any one of the optional subjects taken together.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT 101 | Engineering Mathematics – I | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Algebra, Sequences and Series |
| PHY 101 | Engineering Physics | Core | 3 | Oscillations and Waves, Quantum Mechanics, Solid State Physics, Optics, Electromagnetism |
| PHY 101L | Engineering Physics Lab | Lab | 1 | Basic Physics Experiments, Optical Measurements, Electrical Measurements, Wave Phenomena, Material Properties |
| CHM 101 | Engineering Chemistry | Core | 3 | Thermodynamics, Electrochemistry, Polymer Chemistry, Material Science, Water Technology |
| CHM 101L | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Instrumental Methods, Synthesis of Compounds, Water Quality Analysis, pH Measurements |
| CSE 101 | Introduction to Computing | Core | 3 | Programming Paradigms, Data Structures, Algorithms, Problem Solving, Computational Thinking |
| CSE 101L | Introduction to Computing Lab | Lab | 1 | Programming Basics, Data Types, Control Structures, Functions, Debugging |
| HSS 101 | Communication Skills in English | Core | 2 | Grammar and Usage, Oral Communication, Written Communication, Presentation Skills, Report Writing |
| HSS 101L | Communication Skills in English Lab | Lab | 1 | Group Discussions, Public Speaking, Listening Comprehension, Interview Skills, Role Playing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT 102 | Engineering Mathematics – II | Core | 4 | Differential Equations, Laplace Transforms, Fourier Series, Vector Calculus, Complex Numbers |
| BME 101 | Basic Electrical and Electronics Engineering | Core | 3 | DC and AC Circuits, Semiconductor Devices, Digital Electronics, Transistors, Operational Amplifiers |
| BME 101L | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Circuit Laws, Diode Characteristics, Transistor Amplifiers, Digital Gates, Op-Amp Applications |
| CEN 101 | Engineering Graphics | Core | 1 | Orthographic Projections, Isometric Views, Sectional Views, AutoCAD Basics, Dimensioning |
| WKS 101 | Engineering Workshop | Core | 1 | Carpentry, Welding, Machining, Sheet Metal Work, Fitting |
| CSE 102 | Data Structures and Algorithms | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting Algorithms, Searching Algorithms |
| CSE 102L | Data Structures and Algorithms Lab | Lab | 1 | Implementing Data Structures, Algorithm Analysis, Practical Sorting Algorithms, Graph Traversal, Recursion |
| HSS 102 | Environmental Studies | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Climate Change, Environmental Management |
| HSS 102L | Environmental Studies Lab | Lab | 1 | Water Analysis, Air Quality Monitoring, Waste Management, Environmental Impact Assessment, Field Study |
| NSS 101 / NCS 101 | NSS / NCC / Sport & Yoga | Core | 1 | Community Service, Leadership Development, Social Awareness, Teamwork, Physical Fitness |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT 201 | Discrete Mathematics | Core | 4 | Logic and Proofs, Set Theory, Relations and Functions, Graph Theory, Combinatorics |
| CST 201 | Object Oriented Programming | Core | 3 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Exception Handling |
| CST 201L | Object Oriented Programming Lab | Lab | 1 | Java/Python Programming, Class Design, Object Interaction, Polymorphic Behavior, Debugging OOP Code |
| CST 202 | Database Management Systems | Core | 3 | Relational Model, SQL Query Language, Database Design (ER Model), Normalization, Transaction Management |
| CST 202L | Database Management Systems Lab | Lab | 1 | SQL Queries Practice, Database Creation, ER Diagrams Implementation, Stored Procedures, JDBC/ODBC Connectivity |
| MAT 202 | Linear Algebra | Core | 4 | Vector Spaces, Matrices and Determinants, Eigenvalues and Eigenvectors, Linear Transformations, Orthogonality and Inner Product Spaces |
| MNC 201 | Numerical Methods | Core | 4 | Error Analysis, Root Finding Methods, Interpolation and Approximation, Numerical Differentiation and Integration, Numerical Solutions of Differential Equations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MAT 203 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Regression and Correlation |
| CST 203 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Concurrency and Deadlocks |
| CST 203L | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Creation and Management, Memory Allocation Strategies, Synchronization Problems, Deadlock Avoidance and Detection |
| CST 204 | Computer Networks | Core | 3 | Network Topologies and Protocols, OSI and TCP/IP Models, Routing and Addressing, Transport Layer Services, Network Security Basics |
| CST 204L | Computer Networks Lab | Lab | 1 | Socket Programming, Network Configuration, Packet Analysis with Wireshark, Client-Server Applications, Network Troubleshooting Tools |
| MAT 204 | Optimization Techniques | Core | 4 | Linear Programming, Simplex Method, Duality Theory, Non-Linear Programming, Dynamic Programming |
| MNC 202 | Computational Statistics | Core | 4 | Statistical Computing with R/Python, Data Simulation Methods, Resampling Techniques (Bootstrap, Jackknife), Bayesian Inference Basics, Introduction to Statistical Machine Learning |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MNC 301 | Mathematical Modeling | Core | 4 | Model Formulation and Validation, Differential Equation Models, Optimization Models, Stochastic Models, Simulation Techniques |
| MNC 302 | Introduction to Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Linear and Logistic Regression, Decision Trees and Random Forests, Model Evaluation and Validation |
| CST 301 | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity Analysis, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| CST 301L | Design and Analysis of Algorithms Lab | Lab | 1 | Implementing Advanced Algorithms, Time and Space Complexity Analysis, Algorithm Comparison, Problem Solving Strategies, Data Structure Optimization |
| MNC 303 | Mathematical Foundations of Cryptography | Core | 4 | Number Theory Concepts, Finite Fields, Symmetric Key Cryptography (AES), Public Key Cryptography (RSA, ECC), Hash Functions and Digital Signatures |
| PE-I | Program Elective – I | Program Elective | 3 | Varies based on elective choice (e.g., Advanced ML, Optimization, Scientific Computing) |
| OE-I | Open Elective – I | Open Elective | 3 | Varies based on elective choice (e.g., Humanities, Management, Interdisciplinary) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MNC 304 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Reinforcement Learning Basics |
| MNC 305 | Financial Mathematics | Core | 4 | Interest Rates and Compounding, Derivatives Pricing, Black-Scholes Model, Portfolio Optimization, Risk Management in Finance |
| CST 302 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Testing and Quality Assurance, Project Management |
| MNC 306 | Scientific Computing | Core | 4 | Numerical Linear Algebra, Iterative Methods for Linear Systems, Numerical Solution of PDEs, Scientific Visualization, High Performance Computing Concepts |
| PE-II | Program Elective – II | Program Elective | 3 | Varies based on elective choice (e.g., Natural Language Processing, Data Mining, Quantum Computing) |
| OE-II | Open Elective – II | Open Elective | 3 | Varies based on elective choice (e.g., Entrepreneurship, Cyber Law, Foreign Language) |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MNC 401 | Advanced Data Structures and Algorithms | Core | 4 | Advanced Tree Structures (Red-Black, B-Trees), Hashing Techniques, String Algorithms, Amortized Analysis, Network Flow Algorithms |
| MNC 402 | Research Methodology and Project Management | Core | 3 | Research Design and Ethics, Data Collection and Analysis Methods, Technical Report Writing, Project Planning and Scheduling, Risk Management in Projects |
| PE-III | Program Elective – III | Program Elective | 3 | Varies based on elective choice (e.g., Computer Vision, Algorithmic Trading, Bio-inspired Computing) |
| PE-IV | Program Elective – IV | Program Elective | 3 | Varies based on elective choice |
| PE-V | Program Elective – V | Program Elective | 3 | Varies based on elective choice |
| OE-III | Open Elective – III | Open Elective | 3 | Varies based on elective choice |
| MNC 499 | Project Work Phase – I | Project | 3 | Problem Definition and Scoping, Literature Survey and Gap Analysis, Methodology Design, Initial Implementation and Prototyping, Report Writing and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MNC 498 | Internship | Internship | 12 | Industry Exposure and Application, Real-world Problem Solving, Professional Skill Development, Team Collaboration, Technical Documentation and Presentation |
| MNC 499A | Project Work Phase – II | Project | 8 | Advanced Implementation and Development, Testing and Validation, Optimization and Refinement, Result Analysis and Discussion, Thesis Writing and Project Defense |