B.Tech Software Engineering
Course Overview
The B.Tech in Software Engineering focuses on the complete lifecycle of software development—from analysis and design to deployment and maintenance. The program trains students to build robust, scalable, and secure software systems while adopting modern engineering practices, agile methodologies, and cloud computing technologies.
Course Objectives
- To provide strong foundations in programming, data structures, and software development methodologies.
- To develop expertise in designing high-quality, user-centered software systems.
- To familiarize students with industry tools, frameworks, DevOps practices, and cloud platforms.
- To prepare graduates for dynamic roles across IT, product development, and tech consulting sectors.
Learning Outcomes
- Ability to design and develop efficient software applications.
- Strong understanding of software testing, debugging, version control, and project management.
- Hands-on experience using modern tools (Git, Docker, Kubernetes, AWS, Agile tools).
- Capability to apply engineering concepts to build secure and scalable systems.
- Preparedness for careers such as software developer, system analyst, QA engineer, or cloud engineer.
Curriculum
Semester 1
Practical
- Chemistry Lab
- Python Programming Lab
Theory
- Fundamentals of Technical Communication
- Matrices and Calculus
- Applied Chemistry
- Foundations of Engineering
- Python Programming
- Engineering Graphics
Semester 2
Practical
- Physics Lab
- Digital Electronics Lab
- Data Structures and Algorithms Lab
- Workshop Practices Lab
Theory
- Professional Communication Skills
- Analytical Mathematics
- Physics for Computer Science
- Digital Electronics
- Data Structures and Algorithms
- Foundation Course in Quantitative & Logical Aptitude
Semester 3
Practical
- Operating Systems Lab
- Advanced Programming Practice Lab
- Database Management Systems Lab
- Advanced Corporate Communication Lab
Theory
- Discrete Mathematics
- Object Oriented Design and Programming
- Operating Systems
- Advanced Programming Practice
- Database Management Systems
- Digital Logic and Computer Organization
- Intermediate Quantitative Aptitude and Verbal Skills
Semester 4
Practical
- Design and Analysis of Algorithms Lab
- Artificial Intelligence Lab
- Computer Networks Lab
- Technical Writing and Documentation Lab
Theory
- Probability and Statistics
- Design and Analysis of Algorithms
- Artificial Intelligence
- Computer Networks
- Formal Language and Automata
- Advanced Aptitude and Logical Reasoning
Semester 5
Practical
- Software Engineering and Project Management Lab
- Compiler Design Lab
- Machine Learning Lab
- Professional Communication Practice Lab
Theory
- Software Engineering and Project Management
- Compiler Design
- Machine Learning
- Open Elective I
- Professional Elective I
- Professional Elective II
- Company-Specific Aptitude and Coding Preparation
Semester 6
Practical
- Data Science Lab
- Deep Learning Techniques Lab
- Internship
Theory
- Data Science
- Deep Learning Techniques
- Open Elective II
- Professional Elective III
- Professional Elective IV
- Company-Specific Aptitude and Reasoning Practice
Semester 7
Practical
- Software Engineering in Artificial Intelligence Lab
- Business Intelligence and Analytics Lab
- Project Phase I
Theory
- Report Writing
- Software Engineering in Artificial Intelligence
- Business Intelligence and Analytics
- Open Elective III
- Open Elective IV
- Professional Elective V
Semester 8
Project
- Project Phase II
Open Electives
- Web Programming
- Cloud Computing Fundamentals
- Mobile Application Development
- Data Analytics
Professional Electives
- Programming in Java
- Digital Image Processing
- Genetic Algorithm and its Applications
- Robot Programming
- Service Oriented Architecture
- Accelerated Data Science
- Artificial Neural Networks
- Data Mining and Analytics
- Natural Language Processing
- Advanced Algorithms
- Marketing Analytics
- Computational Neuroscience
- Nature Inspired Computing Techniques
- Information Retrieval
- Design Principles of Smart Space Management
- Functional Programming
- Pattern Recognition Techniques
- Computer Vision
- Artificial Intelligence in Genomics and Disease Prediction
- Machine Learning in Drug Discovery
- IoT Concepts and Applications
- Fuzzy Logic and its Applications
- Robotics: Computational Motion Planning
- Reinforcement Learning Techniques
- Cyber Physical Systems
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