B.Tech. (Computer Science Engineering)

 B.Tech CSE

School of Engineering & Technology

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  • Duration

    4 Years

  • Eligibility

    Min. 50% Aggregate in 10+2

    with Physics and Mathematics as Compulsory Subjects.

  • Selection Procedure

    Entrance Test + PI

Pedagogy

  • padagogy

    Student-Centric Approach

    Focus on individualized learning and fostering critical thinking and encouraging creativity and problem-solving through interactive sessions.

  • padagogy

    Hands-On Learning

    Project-based learning with real-world applications, Industry-driven projects.

  • padagogy

    Problem-Based Learning (PBL)

    Engaging students with real-world problems to solve, and execute application of theoretical knowledge.

  • padagogy

    Simulation Based Learning

    Practical training through virtual labs and interactive simulations.

  • padagogy

    Continuous Assessment

    Through assignments, quizzes, and practicals to ensure learning outcomes are met.

  • padagogy

    Case Studies

    Analysing real-world engineering failures and successes with a set of examples.

  • padagogy

    Interactive Learning Tools

    Using online platforms, interactive simulations, and educational software to enhance learning.

  • padagogy

    Guest Lectures and Industry Interaction

    Inviting industry experts to share their experiences and insights.

  • padagogy

    Project-Based Learning

    Assigning projects that require students to design, analyse, and test theoretical knowledge.

  • padagogy

    Flipped Classrooms

    Aim to increase student engagement and learning. Students are introduced to the learning material before class.

Program Outcomes

Cognitive Knowledge

Provide education that leads to comprehensive understanding of the principles and practices of Computer Science Engineering.

Information and Computer Literacy

Educate and make students up to date with the current scientific literature, computer programs and web information.

Employability Skills

Enhancing the employability skills by making the students find innovative solutions for challenges and problems in various domains of computer science & engineering.

Experimental Skills

Provide broad based training in technical skills in methods of Computer Science.

Research Skills

Use research based knowledge and research methods including design of experiments, analysis and interpretation of data and synthesis of the information for computer science & engineering.

Critical Thinking

Empower students with the ability to think and solve problems.

Professional Ethics

Make students learn ethical principles and standards that govern the decisions and behaviour in their specific field.

Life-long Learning

Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological changes.

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School of Engineering & Technology

Career Paths

After Completing the Program, students will acquire a diverse skill set that equips them for various high-demand career paths. The curriculum is designed to ensure students not only gain technical expertise but also develop essential competencies for thriving in the technology industry.

Block Chain Developer

Cloud Solutions Architect

Software Developer

Entrepreneurs

Full Stack Developer

Data Scientist/Analyst

Cyber Security Specialist

AIML Engineer

Curriculum

Course Category Paper Title Credits
Core Engineering Graphics and Visualization 3
Core Engineering Chemistry 3
Core Engineering Mathematics I 4
Core Engineering Graphics and Visualization Lab 1
Core Engineering Chemistry Lab 1
DSE Fundamental of Electrical and Electronics 2
Fundamental of Electrical and Electronics Lab 1
OR
Introduction to Computer Science 2
Introduction to Computer Application Lab 1
AECC English 3
Language Lab 1
AUC# Sports/Yoga/NSS/NCC 1#
Total 19

# Audit but Mandatory Course

Course Category Paper Title Credits
Core Programming for Problem Solving using C 3
Core Engineering Physics 3
Core Engineering Mathematics II 4
Core Programming for Problem Solving Lab 2
Core Engineering Physics Lab 1
SEC Design Thinking 1
Design Thinking & Idea Lab 1
DSE Engineering Mechanics 3
OR
Smart and Sustainable Energy 3
AECC Communication and Soft Skills Lab 2
VAC# Environmental Science 2#
Total 20

# Audit but Mandatory Course

Course Category Paper Title Credits
CORE Mathematics III 3
CORE Data Structure and Algorithms through ‘C’ 3
CORE Digital Electronics 3
CORE Object Oriented Programming using C++ 3
CORE Web Designing Lab 1
CORE Data Structure and Algorithms through ‘C’ Lab 1
CORE Digital Electronics Lab 1
CORE Object Oriented Programming using C++ Lab 1
DSE Discrete Mathematical Structure 3
Microprocessor
OE* Generic Elective* 2
Extra-Curricular Activity** (Mandatory Qualifying Non-credited but graded Course)
Total 23

*Students can choose any one subject from the pool of Open Elective Courses.

**Mandatory Qualifying Non-credited but graded Course.

Course Category Course Names Credits
CORE Python Programming 3
CORE Operating Systems 3
CORE Design & Analysis of Algorithms 3
CORE Optimization Techniques 4
CORE Python Programming Lab 1
CORE UNIX Shell Scripting and TCP/IP Lab 1
CORE Design & Analysis of Algorithms Lab 1
DSE Optimization Techniques Lab 3
Computer Organization and Architecture
OE* Communication Fundamentals 2
Co-Curricular Activity** (Mandatory Qualifying Non-credited but graded Course)
Total 21

*Students can choose any one subject from the pool of Open Elective Courses.

**Mandatory Qualifying Non-credited but graded Course.

Course Category Course Names Credits
CORE Computer Graphics 3
CORE Advanced PYTHON 3
CORE Database Management System 4
CORE Programming in JAVA 1
CORE Computer Graphics Lab 1
CORE Advanced PYTHON Lab 1
CORE Database Management System Lab 2
DSE Programming in JAVA Lab 3
Software Engineering
GE Information Theory and Coding 2
GE System Analysis and Design 1
SEC Introduction to R Programming 1
SEC Introduction to R Programming Lab 1
GE Generic Elective* 3
Total 23
List of Generic Electives
Sensors Transducers and Actuators
Distributed Data Bases
Organizational Behavior

Course Category Course Names Credits
CORE Computer Networks 3
CORE Application and Development using JAVA 3
CORE Formal Language and Automata Theory 3
CORE Node Js - Backend 3
CORE Computer Networks Lab 1
CORE Application and Development using JAVA Lab 1
CORE UML Lab 1
CORE Node Js - Backend Lab 1
DSE Real Time Systems 3
Artificial Intelligence
Logical and Functional Programming
SEC Ethical Hacking 1
SEC Ethical Hacking Lab 1
GE Generic Elective* 3
Total 24
List of Generic Electives
Introduction to Signal Processing
Network Security
Entrepreneurship Development Program

Course Category Course Titles Credits
CORE Compiler Construction 3
CORE Scala 3
CORE Android Programming 3
CORE Cryptography 3
CORE Compiler Construction Lab 1
CORE Scala Lab 1
CORE Android Programming Lab 1
CORE Minor Project 1
DSE Cloud Computing 3
Deep Learning & Neural Networks
Data Mining and Warehousing
GE Generic Elective 3
Total 24
List of Generic Electives
Wireless Sensor Networks
Blockchain Technology
Digital Marketing

Duration Category Credits
Training for 4-6 Months Training for Dissertation (Valuation by Internal Examiner) 15
Dissertation (Valuation by External Examiner)
Presentation
Defense
Total 15

Tuition Fee

₹124,300

Note *Lab & Library fee will be charged separately
#Program Proposed from Session 2024-25

Scholarship

As per University norms

Write to us to check for your eligibility

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JEST 2025

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Frequently Asked Questions

The Department has the following major Labs. These Labs have Computer Systems with the latest Configurations and printing facility. Main labs are:
  • Cloud Computing and Virtualization Lab
  • Cyber Security Lab
  • Software Engineering and CASE Tools Lab
  • Applied Statistical Analysis Lab
  • IOT Lab
  • SAP Lab
  • Advanced DBMS Lab
  • Advanced JAVA Lab
  • Artificial Intelligence & Machine Learning Lab
  • Computer programming Lab
  • Research Lab/Project Lab

Students can do specialized courses in Cloud Computing, Cyber Security, Graphics and Gaming, Virtualization, Big Data, Internet of Things (IoT), Robotics, Wireless communication, Speech Processing, Machine Learning, Computer Vision, Pattern Recognition, VLSI, Embedded Systems, Soft Computing, Algorithms, DBMS, Computer Architecture, Artificial Intelligence, Software Engineering, Simulation &Modeling, entrepreneurship etc.

  • Student-oriented approaches to curriculum design and teaching.
  • Problem/inquiry/research-based education.
  • Well-planned and well-designed Rubrics (Formative assessment).
  • NPTEL Lectures: To promote self-learning and to share knowledge among students.
  • Virtual Lab: Exposure of Programming & Lab practices.
  • Take up Projects: To prepare student to develop live projects with their own.
  • Content beyond Syllabus: To meet-out current Industry demand and fill the gap.
  • Course of actions and implementation on Stakeholders Feedback
  • Major focus is to prepare the students for Product based Companies.
  • Profile based training for Final Year Students
  • Special Technical training for eligible students in placement.
  • Coding Culture: Coding Competitions for CSE Students (Coding Culture): In today’s rapidly changing environment, programming skills are essential tools that can be utilized and incorporated into various fields and domains. Hence, it becomes absolutely essential to equip young minds with such skills.
  • Roadmap to becoming a Successful Developer/Software Engg/ Front -End Developer/ Back-End Developer/ Data Scientist/ Business Analyst/ Cloud Architect.

  • Curriculum is based on the Industry needs and completely practical oriented.
  • Curriculum is Dynamic, Fully Articulated, Realistic and Student oriented.
  • Coding and passion for learning new algorithms and developing new ones.
  • Course contents are multidisciplinary in nature and future oriented.

Cyber security is “a computing-based discipline involving technology, people, information, and processes to enable assured operations in the context of adversaries.” 

Data Science is “a set of fundamental principles that guide the extraction of knowledge from data.”

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