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B-E in Name Computer Science Engineering Data Science Seats 60 at Shri Madhwa Vadiraja Institute of Technology & Management
Udupi, Karnataka
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About the Specialization
What is {"name": "Computer Science & Engineering (Data Science)", "seats": 60} at Shri Madhwa Vadiraja Institute of Technology & Management Udupi?
This Computer Science & Engineering (Data Science) program at Shri Madhwa Vadiraja Institute of Technology and Management focuses on equipping students with a robust foundation in core computer science alongside specialized knowledge in data analysis, machine learning, and artificial intelligence. Recognizing India''''s burgeoning digital economy and the massive data generation across sectors, this program trains students to transform raw data into actionable insights. It emphasizes a blend of theoretical concepts and practical applications, preparing graduates for the high demand in data-driven roles within the Indian industry.
Who Should Apply?
This program is ideal for fresh graduates from science or engineering backgrounds with a strong aptitude for mathematics, statistics, and programming. It also appeals to individuals keen on solving complex problems using data, those aspiring for roles in analytics, AI, and machine learning, and students eager to contribute to India''''s data revolution. A prerequisite might include a strong foundation in 10+2 level mathematics and basic computer literacy.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths as Data Scientists, Machine Learning Engineers, Data Analysts, AI Developers, or Business Intelligence Specialists within India. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA in top-tier Indian companies and MNCs. The program fosters critical thinking, problem-solving, and analytical skills, aligning graduates for advanced studies or professional certifications in AI/ML from platforms like NPTEL or Coursera.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Data Structures- (Semester 1-2)
Dedicate significant time in Semesters 1-2 to solidify your understanding of C, Python, and Java programming, along with fundamental data structures like arrays, linked lists, stacks, and queues. Participate actively in programming competitions and online coding challenges to sharpen problem-solving skills.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses on Programming and Data Structures
Career Connection
Strong programming and data structures knowledge are non-negotiable for cracking technical interviews for entry-level developer or data science roles at companies like TCS, Infosys, and startups.
Build a Strong Mathematical and Statistical Base- (Semester 1-3)
Focus on excelling in Engineering Mathematics courses. These subjects form the backbone of Data Science and Machine Learning. Engage in extra practice problems and seek conceptual clarity from faculty. Understand linear algebra, calculus, probability, and statistics thoroughly.
Tools & Resources
Khan Academy, MIT OpenCourseware, NPTEL for Mathematics, Textbooks prescribed by VTU
Career Connection
A robust mathematical foundation is crucial for understanding complex algorithms in AI/ML, which directly impacts your ability to innovate and solve advanced data problems in the industry.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups with peers to discuss difficult concepts and work on mini-projects together. Collaborative learning enhances understanding and develops teamwork skills, which are highly valued in corporate environments. Participate in college-level hackathons.
Tools & Resources
GitHub for version control, Google Meet for online collaboration, College labs for group work
Career Connection
Developing strong teamwork and communication skills through collaborative projects helps you integrate smoothly into development teams and contribute effectively to larger industry projects.
Intermediate Stage
Undertake Mini-Projects & Explore Data Science Tools- (Semester 3-5)
Beyond lab work, initiate independent mini-projects using real-world datasets (e.g., from Kaggle). Experiment with Python libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. Document your projects on platforms like GitHub to build a portfolio.
Tools & Resources
Kaggle for datasets, Jupyter Notebooks, Google Colab, GitHub
Career Connection
A strong project portfolio showcasing practical application of data science tools is vital for attracting recruiters for internships and entry-level data scientist positions.
Seek Early Industry Exposure through Internships/Workshops- (Semester 4-6)
Actively look for short-term internships, workshops, or bootcamps in data science or related fields during semester breaks. Even unpaid internships offer valuable real-world experience and networking opportunities within the Indian tech landscape.
Tools & Resources
Internshala, LinkedIn, College placement cell notices, Industry specific workshops
Career Connection
Early industry exposure provides practical insights, helps you understand corporate work culture, and significantly boosts your resume for future placements by reputable Indian tech firms and MNCs.
Deep Dive into Core Data Science and ML Concepts- (Semester 5-6)
Focus on understanding the theoretical underpinnings of Artificial Intelligence, Machine Learning, and Database Management Systems. Supplement classroom learning with online courses from top universities (e.g., Coursera''''s ''''Machine Learning'''' by Andrew Ng).
Tools & Resources
Coursera, edX, Udemy, NPTEL''''s AI/ML courses
Career Connection
A solid grasp of core concepts is essential for acing specialized technical interviews for data science and AI roles. It also empowers you to critically evaluate and choose appropriate algorithms for real-world problems.
Advanced Stage
Execute a Capstone Project with Industry Relevance- (Semester 7-8)
For your final year project, choose a problem that addresses a real-world business need or a significant research gap in data science. Collaborate with faculty or industry mentors. Aim for a deployable solution or a research publication.
Tools & Resources
Advanced ML frameworks (TensorFlow, PyTorch), Cloud platforms (AWS, Azure, GCP), Research papers (arXiv, IEEE Xplore)
Career Connection
A well-executed, impactful capstone project serves as a strong testament to your skills, often leading to direct placement opportunities or sponsorships for higher studies and research roles.
Prepare Rigorously for Placements & Higher Studies- (Semester 7-8)
Engage in intensive interview preparation, focusing on data structures, algorithms, SQL, machine learning concepts, and soft skills. Attend mock interviews, revise aptitude, and refine your resume and LinkedIn profile. Research graduate programs if considering higher education.
Tools & Resources
LeetCode, GeeksforGeeks, Mock interview platforms, Resume builders, GRE/GATE preparation materials
Career Connection
Systematic preparation directly translates into securing coveted positions in top Indian tech companies, data analytics firms, or admission into prestigious postgraduate programs.
Network Actively and Stay Updated with Industry Trends- (Semester 6-8)
Attend industry conferences, tech meetups, and webinars (both online and offline) in cities like Bengaluru, Hyderabad, or Pune. Connect with professionals on LinkedIn, participate in discussions, and follow leading data science blogs and research. Understand the latest tools and breakthroughs.
Tools & Resources
LinkedIn, Medium blogs for Data Science, Kaggle forums, AI/ML conferences in India
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and helps you stay competitive by understanding the evolving demands of the Indian data science job market.
Program Structure and Curriculum
Eligibility:
- Admissions typically based on performance in Karnataka Common Entrance Test (KCET) or COMEDK UGET, or JEE Mains, followed by counselling. Candidates must have passed 10+2 or equivalent with Physics, Mathematics, and one of Chemistry/Biology/Biotechnology/Technical Vocational subject with an aggregate of 45% (40% for reserved categories) from a recognized board.
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 |
|---|---|---|---|---|
| BMSM101 | Engineering Mathematics – I | Core | 3 | Differential Calculus, Integral Calculus, Partial Differentiation, Vector Calculus, Multiple Integrals |
| BPHYE101 | Engineering Physics | Core | 3 | Wave Optics, Quantum Mechanics, Laser Physics, Optical Fibers, Material Science |
| BBEEE101 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Three-Phase Systems, Transformers, Electrical Machines |
| BCVE101 | Elements of Civil Engineering and Mechanics | Core | 3 | Civil Engineering Materials, Surveying, Mechanics of Materials, Fluid Mechanics, Engineering Mechanics |
| BGE101 | Computer Aided Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Section of Solids, Development of Surfaces, CAD Software |
| BHUM101 | Communicative English | Core | 2 | Grammar, Vocabulary, Reading Comprehension, Listening Skills, Writing Skills |
| BPHYL101 | Engineering Physics Lab | Lab | 1 | Experiments on Optics, Electricity, Material Properties, Semiconductor Devices, Magnetic Effects |
| BBEL101 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, KVL and KCL, Thevenin''''s Theorem, Norton''''s Theorem, Measurement of Power |
| BCS101 | Introduction to Programming | Core | 3 | C Programming Basics, Data Types and Operators, Control Flow Statements, Functions, Arrays and Pointers |
| BCSL101 | Introduction to Programming Lab | Lab | 1 | C Program Implementation, Debugging Techniques, File Operations, Dynamic Memory Allocation, Practical Exercises |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMSM201 | Engineering Mathematics – II | Core | 3 | Linear Algebra, Vector Spaces, Eigenvalues and Eigenvectors, Numerical Methods, Probability and Statistics |
| BCHYE201 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion, Fuel Cells, Polymers, Water Technology |
| BEELE201 | Basic Electronics and Engineering | Core | 3 | Diode Circuits, Transistors, Operational Amplifiers, Digital Electronics, Communication Systems |
| BME201 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Manufacturing Processes |
| BECE201 | Basic Computer Organization | Core | 3 | Number Systems, Logic Gates, Boolean Algebra, Combinational Circuits, Sequential Circuits |
| BHUM201 | Technical English | Core | 2 | Technical Writing, Report Generation, Presentation Skills, Group Discussion, Professional Communication |
| BCHL201 | Engineering Chemistry Lab | Lab | 1 | Titrations, pH Measurement, Spectrophotometry, Viscosity Determination, Water Analysis |
| BEEL201 | Basic Electronics Lab | Lab | 1 | Diode Characteristics, Rectifiers, Transistor Amplifiers, Logic Gates Implementation, Op-Amp Applications |
| BECL201 | Computer Organization Lab | Lab | 1 | Logic Gates Simulation, Adders and Subtractors, Flip-Flops, Registers, Memory Unit Simulation |
| BCSL201 | Problem Solving with Python Lab | Lab | 1 | Python Fundamentals, Data Structures in Python, Algorithmic Problem Solving, Using Python Libraries, Debugging and Testing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMSM301 | Engineering Mathematics – III | Core | 3 | Fourier Transforms, Z-Transforms, Partial Differential Equations, Complex Variables, Probability and Statistics |
| BCSC301 | Data Structures | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal, Hashing Techniques |
| BCSDC302 | Object Oriented Programming with Java | Core | 4 | OOP Concepts, Classes, Objects, Inheritance, Polymorphism and Abstraction, Exception Handling, Collections Framework |
| BCDC303 | Discrete Mathematics & Graph Theory | Core | 4 | Set Theory and Logic, Relations and Functions, Recurrence Relations, Graph Theory Fundamentals, Trees and Connectivity |
| BCDC304 | Digital Logic Design & Computer Organization | Core | 4 | Logic Gates and Boolean Algebra, Combinational Logic Circuits, Sequential Logic Circuits, CPU Organization, Memory Hierarchy |
| BCSC305 | Data Structures Lab | Lab | 1 | Array Operations, Linked List Implementations, Stack/Queue Applications, Tree Traversal Algorithms, Graph Algorithms |
| BCSDCL306 | Object Oriented Programming Lab with Java | Lab | 1 | Java Programs for OOP, GUI Applications, Database Connectivity, Multithreading Concepts, Practical Projects |
| BCSDC307 | Mini Project – I | Project | 2 | Problem Identification, Project Design, Implementation and Testing, Documentation, Presentation Skills |
| BCSDC308 | Vyavaharika Kannada / Aadunika Kannada / Constitution of India and Professional Ethics | Compulsory | 1 | Kannada Language Skills, Indian Constitution Principles, Fundamental Rights and Duties, Professional Ethics, Human Values |
| BCSDC309 | Samvedana / Balake Kannada | Compulsory | 1 | Basic Kannada Communication, Everyday Vocabulary, Cultural Understanding, Reading and Writing, Conversational Skills |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMSM401 | Engineering Mathematics – IV | Core | 3 | Statistical Methods, Random Variables, Probability Distributions, Sampling Theory, Stochastic Processes |
| BCSC401 | Design and Analysis of Algorithms | Core | 4 | Algorithmic Paradigms, Sorting and Searching Algorithms, Graph Algorithms, Dynamic Programming, Greedy Algorithms |
| BCSDC402 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems and I/O |
| BCSDC403 | Database Management Systems | Core | 4 | DBMS Concepts, ER Modeling, Relational Algebra, SQL Queries, Normalization and Transactions |
| BCDC404 | Microcontroller & Embedded Systems | Core | 4 | Microcontroller Architecture, Assembly Language Programming, Interfacing Techniques, Embedded System Design, Real-Time Operating Systems |
| BCSC405 | Design and Analysis of Algorithms Lab | Lab | 1 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Solutions, Greedy Algorithm Applications, Divide and Conquer Strategies |
| BCSDCL406 | Database Management Systems Lab | Lab | 1 | SQL Queries and Commands, Database Design, PL/SQL Programming, Transaction Management, Report Generation |
| BCSDC407 | Mini Project – II | Project | 2 | Advanced Project Planning, System Development, Testing and Debugging, Technical Documentation, Presentation Skills |
| BCSDC408 | Environmental Studies | Compulsory | 1 | Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Sustainable Development, Climate Change |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCSDC501 | Artificial Intelligence | Core | 4 | AI Agents and Problem Solving, Search Algorithms (Heuristic, Adversarial), Knowledge Representation, Introduction to Machine Learning, Expert Systems |
| BCSDC502 | Computer Networks | Core | 4 | OSI and TCP/IP Models, Network Topologies, Routing Protocols, Congestion Control, Network Security Basics |
| BCSDC503 | Software Engineering | Core | 3 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Techniques, Project Management |
| BCSDC504 | Professional Elective – I | Elective | 3 | Key topics may vary based on chosen elective (e.g., Big Data Analytics, Deep Learning, Cloud Computing, Advanced Java, Computer Graphics & Visualization). Sample topics: Big Data Concepts and Ecosystems, Neural Network Architectures, Cloud Service Models (IaaS, PaaS, SaaS), JavaFX and GUI Development, 3D Transformations in Computer Graphics. |
| BCSDC505 | Open Elective – I | Elective | 3 | Key topics may vary based on chosen elective from other departments (e.g., Entrepreneurship, Research Methodology, Intellectual Property, Cyber Security Basics). Sample topics: Entrepreneurship Fundamentals, Research Design and Data Analysis, Intellectual Property Rights, Introduction to Cyber Security. |
| BCSDC506 | Data Science Lab with Python | Lab | 1 | Python for Data Manipulation (Pandas), Data Visualization (Matplotlib, Seaborn), Machine Learning Libraries (Scikit-learn), Data Preprocessing, Exploratory Data Analysis |
| BCSDC507 | AI and Machine Learning Lab | Lab | 1 | Implementing AI Search Algorithms, Machine Learning Algorithms (Regression, Classification), Clustering Techniques, Neural Network Implementation, Model Evaluation Metrics |
| BCSDC508 | Internship-I / Mini Project-III | Project/Internship | 2 | Industry Exposure, Project Planning and Execution, Problem Solving, Report Writing, Presentation Skills |
| BCSDC509 | Social Connect & Responsibility | Compulsory | 1 | Community Engagement, Social Awareness, Ethical Practices, Professional Responsibility, Teamwork |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCSDC601 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation and Selection, Ensemble Methods, Feature Engineering |
| BCSDC602 | Web Technologies | Core | 4 | HTML, CSS, JavaScript, Server-side Scripting (Node.js/Django), Database Integration for Web, Web Security Fundamentals, API Design and Development |
| BCSDC603 | Professional Elective – II | Elective | 3 | Key topics may vary based on chosen elective (e.g., Data Warehousing & Data Mining, Natural Language Processing, Computer Vision, Digital Image Processing, Pattern Recognition). Sample topics: Data Warehouse Design, NLP Text Preprocessing, Image Feature Extraction, Image Enhancement Techniques, Statistical Pattern Recognition. |
| BCSDC604 | Professional Elective – III | Elective | 3 | Key topics may vary based on chosen elective (e.g., Internet of Things, Blockchain Technology, Cyber Security, Distributed Computing, Robotics and Automation). Sample topics: IoT Architecture and Protocols, Cryptographic Principles, Network Security Threats, Distributed System Concepts, Robotic Process Automation. |
| BCSDC605 | Open Elective – II | Elective | 3 | Key topics may vary based on chosen elective from other departments (e.g., Project Management, Supply Chain Management, Financial Accounting, Indian Constitution). Sample topics: Project Life Cycle, Logistics and Inventory Management, Accounting Principles, Indian Polity and Governance. |
| BCSDC606 | Machine Learning Lab | Lab | 1 | Implementing ML Algorithms, Data Preprocessing and Feature Selection, Model Training and Hyperparameter Tuning, Evaluation Metrics for ML Models, Introduction to Deep Learning Frameworks |
| BCSDC607 | Web Technologies Lab | Lab | 1 | Front-end Development (HTML, CSS, JS), Back-end Development (Server-side Frameworks), Database Integration, API Consumption and Creation, Full-Stack Application Development |
| BCSDC608 | Internship-II / Mini Project-IV | Project/Internship | 2 | Advanced Internship Experience, Project Management Skills, Software Development Life Cycle, Technical Report Writing, Problem-solving in Industry |
| BCSDC609 | Universal Human Values | Compulsory | 1 | Ethics and Morality, Professional Values, Human Conduct, Harmony in Society, Sustainable Living |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCSDC701 | Project Work Phase – I | Project | 6 | Problem Definition, Literature Review, System Design and Architecture, Prototype Development, Progress Report and Presentation |
| BCSDC702 | Professional Elective – IV | Elective | 3 | Key topics may vary based on chosen elective (e.g., Ethical Hacking, Software Testing, Mobile Application Development, Game Programming, Augmented Reality & Virtual Reality). Sample topics: Penetration Testing, Software Quality Assurance, Android/iOS Development, Game Engine Fundamentals, AR/VR Principles and Applications. |
| BCSDC703 | Professional Elective – V | Elective | 3 | Key topics may vary based on chosen elective (e.g., Data Governance, Business Intelligence, Data Stream Processing, Reinforcement Learning, Financial Data Analytics). Sample topics: Data Quality Management, BI Tools and Techniques, Real-time Data Processing, Markov Decision Processes, Algorithmic Trading. |
| BCSDC704 | Open Elective – III | Elective | 3 | Key topics may vary based on chosen elective from other departments (e.g., Entrepreneurship, E-commerce, Marketing Management, Organizational Behavior, Renewable Energy Sources). Sample topics: Startup Ecosystem, Digital Marketing Strategies, Consumer Behavior, Team Dynamics, Solar and Wind Energy. |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCSDC801 | Project Work Phase – II | Project | 10 | Full System Implementation, Testing and Validation, Optimization and Deployment, Final Project Report, Demonstration and Viva Voce |
| BCSDC802 | Professional Elective – VI | Elective | 3 | Key topics may vary based on chosen elective (e.g., Enterprise Resource Planning, Supply Chain Analytics, Agile Methodologies, Human Computer Interaction, System Simulation & Modeling). Sample topics: ERP Modules, Supply Chain Optimization, Scrum and Kanban, Usability Engineering, Simulation Tools and Techniques. |
