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B-TECH-B-E in Computer Science Engineering at Saveetha Institute of Medical and Technical Sciences
Chennai, Tamil Nadu
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About the Specialization
What is Computer Science Engineering at Saveetha Institute of Medical and Technical Sciences Chennai?
This Computer Science and Engineering program at Saveetha Institute of Medical and Technical Sciences focuses on providing a comprehensive foundation in computing principles and applications. It emphasizes core areas such as data structures, algorithms, operating systems, and database management, alongside emerging technologies like AI, Machine Learning, Cloud Computing, and IoT. The curriculum is designed to meet the evolving demands of the Indian IT industry, preparing students for innovative roles in technology.
Who Should Apply?
This program is ideal for fresh graduates from high school with a strong aptitude in mathematics and science, aspiring to build a career in software development, data science, cybersecurity, or cloud engineering. It also caters to students keen on research and innovation in computing. Specific prerequisite backgrounds typically include strong performance in 10+2 examinations with Physics, Chemistry, and Mathematics.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as Software Engineers, Data Scientists, AI/ML Engineers, Cloud Developers, and Cybersecurity Analysts. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The program aligns with industry certifications like AWS, Azure, and Google Cloud, fostering robust growth trajectories in top Indian and multinational companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to mastering Python and C++ programming, focusing on core concepts like data structures and object-oriented principles. Regularly practice coding challenges to build logic and problem-solving skills.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong programming fundamentals are critical for cracking technical interviews and excelling in entry-level software development roles across all tech companies.
Build a Solid Mathematical Base- (Semester 1-3)
Pay close attention to Engineering Mathematics and Probability courses. These subjects form the analytical backbone for advanced CSE topics like algorithms, machine learning, and data science.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Calculus, Linear Algebra), Textbooks
Career Connection
A robust mathematical understanding is essential for roles in AI/ML, data analytics, and research, enabling complex problem-solving and model development.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups, participate in college coding clubs, and work on small team projects. Collaborating with peers enhances understanding, develops teamwork skills, and provides exposure to different problem-solving approaches.
Tools & Resources
GitHub, Discord/WhatsApp groups, College Coding Clubs
Career Connection
Teamwork and communication skills are highly valued in the industry, making graduates better candidates for collaborative development environments and project management roles.
Intermediate Stage
Specialize and Apply Core Concepts- (Semester 3-5)
Deep dive into subjects like Data Structures, DBMS, and Operating Systems. Implement complex algorithms, build database applications, and understand OS internals through practical projects and labs.
Tools & Resources
SQL Practice platforms, Operating System simulators, Advanced Data Structure books
Career Connection
Mastering these core computer science areas is non-negotiable for roles as backend developers, system engineers, and database administrators in reputable companies.
Seek Industry Exposure through Internships/Mini-Projects- (Semester 4-6)
Actively look for summer internships or engage in challenging mini-projects, even if unpaid. This provides practical experience, helps in applying theoretical knowledge, and builds a professional network.
Tools & Resources
LinkedIn, Internshala, College career services, Industry hackathons
Career Connection
Internships are often a direct gateway to full-time employment, offering real-world skills and demonstrable experience that significantly boosts placement chances.
Participate in Coding Competitions & Hackathons- (Semester 3-5)
Regularly participate in competitive programming challenges and hackathons. This sharpens problem-solving abilities, introduces new technologies, and helps identify areas for improvement.
Tools & Resources
CodeChef, TopCoder, Kaggle, Major hackathon events
Career Connection
Performance in these events highlights exceptional technical skills and resilience to potential employers, often leading to direct interview opportunities with top tech firms.
Advanced Stage
Focus on Emerging Technologies and Specialization- (Semester 6-7)
Leverage electives and self-study to specialize in areas like AI/ML, Cloud Computing, Cybersecurity, or Full Stack Development. Work on capstone projects that integrate multiple advanced concepts.
Tools & Resources
Coursera/Udemy (specialization courses), Official cloud provider documentations (AWS, Azure), Deep Learning frameworks (TensorFlow, PyTorch)
Career Connection
Specialized skills are highly sought after in the current job market, enabling graduates to secure roles in cutting-edge domains with higher growth potential and remuneration.
Intensive Placement Preparation and Mock Interviews- (Semester 7-8)
Begin rigorous preparation for placements including aptitude tests, technical rounds, and HR interviews. Participate in mock interviews and group discussions organized by the career services department or external platforms.
Tools & Resources
Placement cell resources, InterviewBit, Glassdoor, Mock interview platforms
Career Connection
Thorough preparation directly correlates with higher success rates in securing placements, leading to job offers from desired companies.
Develop a Professional Portfolio and Network- (Semester 6-8)
Curate a strong online portfolio showcasing projects, contributions, and skills. Actively network with alumni, industry professionals, and faculty mentors through events, LinkedIn, and conferences.
Tools & Resources
GitHub portfolio, LinkedIn profile, Professional networking events
Career Connection
A compelling portfolio acts as a tangible resume, while professional networking opens doors to referrals, mentorship, and unadvertised job opportunities in the Indian tech ecosystem.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 10+2 with a minimum of 45% marks (40% for reserved category) in Mathematics, Physics, Chemistry (or relevant vocational subjects).
Duration: 8 semesters / 4 years
Credits: 159 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS23S101 | Professional English – I | Core | 3 | Listening and Speaking, Reading and Writing, Grammar and Vocabulary, Paragraph Writing, Letter Writing |
| MA23S101 | Engineering Mathematics – I | Core | 4 | Matrices, Differential Calculus, Functions of Several Variables, Multiple Integrals, Vector Calculus |
| PH23S101 | Engineering Physics | Core | 3 | Properties of Matter, Applied Optics, Quantum Physics, Material Science, Acoustics and Ultrasonics |
| CY23S101 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemistry, Corrosion, Fuels and Combustion, Polymer Chemistry |
| GE23S101 | Problem Solving and Python Programming | Core | 3 | Python Basics, Data Types and Control Flow, Functions and Modules, Data Structures (Lists, Tuples, Dictionaries), File Handling |
| GE23S111 | Problem Solving and Python Programming Laboratory | Lab | 1 | Basic Python Programs, Control Structures, Functions, List and Tuple Operations, Dictionary Operations |
| HS23S111 | Professional English – I Laboratory | Lab | 1 | Listening Practice, Conversational Skills, Presentation Skills, Group Discussion, Interview Skills |
| CY23S111 | Engineering Chemistry Laboratory | Lab | 1 | Water quality analysis, pH and conductivity measurements, Titrations, Estimation of chemical parameters |
| MC23S101 | Environmental Science and Engineering | Mandatory Non-Credit | 0 | Ecosystems, Biodiversity, Pollution, Waste Management, Sustainable Development, Environmental Legislation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS23S201 | Professional English – II | Core | 3 | Reading Comprehension, Advanced Grammar, Report Writing, Resume Preparation, Oral Presentation |
| MA23S201 | Engineering Mathematics – II | Core | 4 | Ordinary Differential Equations, Laplace Transforms, Vector Spaces, Complex Numbers, Fourier Series |
| GE23S201 | Engineering Graphics and Design | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, CAD tools introduction, Assembly Drawings |
| CS23S201 | Digital Principles and Computer Organization | Core | 3 | Digital Logic Gates, Boolean Algebra, Combinational Circuits, Sequential Circuits, Computer Architecture Basics, Memory Hierarchy |
| CS23S202 | Object Oriented Programming using C++ | Core | 3 | C++ Fundamentals, Classes and Objects, Constructors and Destructors, Inheritance, Polymorphism, Exception Handling |
| CS23S211 | Digital Principles and Computer Organization Lab | Lab | 1 | Logic Gates Implementation, Boolean Expression Realization, Combinational Circuit Design, Sequential Circuit Design |
| CS23S212 | Object Oriented Programming using C++ Lab | Lab | 1 | Class and Object Programs, Operator Overloading, Inheritance Implementation, Polymorphism, File I/O |
| GE23S211 | Manufacturing Practices Laboratory | Lab | 1 | Fitting, Carpentry, Welding, Sheet Metal Work, Foundry Practices |
| MC23S201 | Value Education | Mandatory Non-Credit | 0 | Human Values, Ethics, Moral Development, Peace, Harmony, Social Responsibility |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA23S301 | Probability and Queueing Theory | Core | 4 | Probability Axioms, Random Variables, Probability Distributions, Joint Distributions, Queueing Models (M/M/1, M/M/C) |
| CS23S301 | Data Structures | Core | 3 | Arrays, Stacks, Queues, Linked Lists, Trees, Graphs, Searching and Sorting |
| CS23S302 | Database Management Systems | Core | 3 | Relational Model, SQL Queries, Schema Design, Normalization, Transaction Management, Concurrency Control |
| CS23S303 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Asymptotic Notations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Graph Algorithms |
| CS23S304 | Principles of Operating Systems | Core | 3 | OS Structure, Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, Deadlocks |
| EI23S301 | Electrical and Electronics Engineering | Core | 3 | DC and AC Circuits, Semiconductor Devices, Diodes and Transistors, Operational Amplifiers, Digital Electronics Basics |
| CS23S311 | Data Structures Laboratory | Lab | 1 | Array implementations, Linked List operations, Stack and Queue applications, Tree traversals, Graph algorithms |
| CS23S312 | Database Management Systems Laboratory | Lab | 1 | SQL DDL/DML, Joins, Views, Stored Procedures, Triggers, ER Diagram mapping |
| CS23S313 | Principles of Operating Systems Laboratory | Lab | 1 | Shell scripting, Process creation, Inter-process communication, CPU scheduling algorithms, Memory management simulation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS23S401 | Artificial Intelligence | Core | 3 | AI Agents, Search Algorithms (DFS, BFS, A*), Knowledge Representation, Logic, Machine Learning Basics, Natural Language Processing |
| CS23S402 | Microprocessors and Microcontrollers | Core | 3 | 8085/8086 Architecture, Instruction Set, Assembly Language Programming, Interfacing Peripherals, Microcontroller Basics |
| CS23S403 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| CS23S404 | Computer Networks | Core | 3 | OSI Model, TCP/IP Protocol Suite, Data Link Layer, Network Layer (IP, Routing), Transport Layer (TCP, UDP), Application Layer |
| CS23S405 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Design Principles, Testing Methodologies, Project Management |
| HS23S401 | Universal Human Values and Ethics | Core | 3 | Understanding Harmony, Human-Human Relationship, Society and Nature, Professional Ethics, Ethical Dilemmas |
| CS23S411 | Artificial Intelligence Laboratory | Lab | 1 | Python for AI, Search algorithm implementation, Knowledge representation, Logic programming (Prolog/Lisp), Machine Learning libraries |
| CS23S412 | Microprocessors and Microcontrollers Laboratory | Lab | 1 | 8085/8086 assembly programming, Interfacing with I/O devices, Microcontroller programming |
| CS23S413 | Computer Networks Laboratory | Lab | 1 | Socket Programming, Network configuration, Protocol analysis (Wireshark), Routing protocols, Network security tools |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS23S501 | Professional Communication and Career Skills | Core | 2 | Advanced Communication, Interpersonal Skills, Presentation Techniques, Interview Skills, Group Discussion |
| CS23S501 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization, Code Generation |
| CS23S502 | Data Warehousing and Data Mining | Core | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Concepts, Association Rule Mining, Classification, Clustering |
| CS23S503 | Full Stack Development | Core | 3 | Front-end Technologies (HTML, CSS, JavaScript), Back-end Frameworks (Node.js/Python/Java), Databases (SQL/NoSQL), API Development, Deployment |
| CS23S504 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Regression, Classification, Deep Learning Basics, Model Evaluation, Reinforcement Learning |
| CS23SE01 | Object Oriented Analysis and Design (Program Elective - I) | Elective | 3 | UML Diagrams, Use Cases, Class Diagrams, Sequence Diagrams, Design Patterns, Software Architecture |
| GE23OE01 | Disaster Management (Open Elective - I) | Elective | 3 | Types of Disasters, Disaster Mitigation, Preparedness, Response, Recovery, Rehabilitation |
| CS23S511 | Data Mining and Data Warehousing Laboratory | Lab | 1 | ETL tools, OLAP cube creation, Data preprocessing, Association rule mining, Classification algorithms, Clustering algorithms |
| CS23S512 | Full Stack Development Laboratory | Lab | 1 | Front-end development (React/Angular), Back-end API implementation, Database integration, User authentication, Deployment |
| CS23S513 | Machine Learning Laboratory | Lab | 1 | Python for ML (Scikit-learn, Pandas), Data preprocessing, Regression models, Classification models, Clustering, Model evaluation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HS23S601 | Indian Constitution and Professional Ethics | Core | 2 | Constitution of India, Fundamental Rights and Duties, Directive Principles, Legislative System, Professional Ethics in Engineering |
| CS23S601 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, AWS/Azure/GCP Basics, Cloud Storage |
| CS23S602 | Cyber Security | Core | 3 | Network Security, Cryptography, Malwares, Vulnerabilities, Ethical Hacking Basics, Cyber Forensics, Security Policies |
| CS23S603 | Software Project Management | Core | 3 | Project Planning, Project Scheduling, Risk Management, Resource Management, Quality Management, Agile Methodologies |
| CS23SE05 | Big Data Analytics (Program Elective - II) | Elective | 3 | Big Data Characteristics, Hadoop Ecosystem (HDFS, MapReduce), Spark, NoSQL Databases, Data Streaming, Big Data Tools |
| OE23S601 | Entrepreneurship Development (Open Elective - II) | Elective | 3 | Entrepreneurial Mindset, Business Plan, Funding, Marketing, Legal Aspects, Innovation |
| CS23S611 | Cloud Computing Laboratory | Lab | 1 | AWS/Azure/GCP account setup, Virtual machine deployment, Storage services, Networking in cloud, Serverless functions |
| CS23S612 | Cyber Security Laboratory | Lab | 1 | Network scanning tools, Vulnerability assessment, Cryptography implementation, Web application security testing, Firewall configuration |
| CS23S613 | Mini Project II | Project | 2 | Project Planning, Design, Implementation, Testing, Documentation, Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS23S701 | Internet of Things | Core | 3 | IoT Architecture, Sensors and Actuators, Communication Protocols (MQTT, CoAP), IoT Platforms, Edge Computing, Security in IoT |
| CS23S702 | Deep Learning | Core | 3 | Neural Networks, Activation Functions, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers |
| CS23SE09 | Blockchain Technologies (Program Elective - III) | Elective | 3 | Cryptocurrencies, Distributed Ledgers, Smart Contracts, Consensus Mechanisms, Blockchain Platforms (Ethereum, Hyperledger) |
| CS23SE13 | Augmented Reality / Virtual Reality (Program Elective - IV) | Elective | 3 | AR/VR Devices, 3D Graphics, Interaction Techniques, Tracking, Immersion, Application Development |
| GE23OE03 | Total Quality Management (Open Elective - III) | Elective | 3 | Quality Principles, Quality Management Systems, TQM Tools, Continuous Improvement, Six Sigma |
| CS23S711 | Deep Learning Laboratory | Lab | 1 | TensorFlow/Keras/PyTorch, CNN implementation, RNN implementation, Transfer learning, Image recognition, Natural language processing |
| CS23S712 | Internship / Industrial Training | Core | 3 | Practical Industry Exposure, Real-world Project Experience, Professional Skill Development, Report Writing, Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS23SE17 | Quantum Computing (Program Elective - V) | Elective | 3 | Quantum Bits, Superposition, Entanglement, Quantum Gates, Quantum Algorithms (Shor''''s, Grover''''s), Quantum Cryptography |
| CS23S811 | Project Work / Thesis | Project | 9 | Problem Identification, Literature Survey, Design, Implementation, Testing, Evaluation, Technical Report Writing, Presentation |
