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₹2,50,000
Placement
98.0%
Avg Package
₹6,20,000
Highest Package
₹11,50,000
Fees
₹2,50,000
Placement
98.0%
Avg Package
₹6,20,000
Highest Package
₹11,50,000
Seats
120
Students
800
Seats
120
Students
800
The Computer Science curriculum at Roorkee College Of Engineering is meticulously designed to provide a robust foundation in theoretical principles while emphasizing practical applications and industry relevance. The program spans eight semesters, with each semester carefully structured to build upon prior knowledge and introduce new concepts gradually.
The curriculum includes core courses, departmental electives, science electives, and laboratory components that collectively form a comprehensive educational experience. Core courses lay the groundwork for understanding fundamental concepts in computing, while electives allow students to specialize based on their interests and career goals.
Core courses are mandatory for all Computer Science students and include foundational subjects such as Introduction to Programming, Data Structures and Algorithms, Object-Oriented Programming, Database Management Systems, Operating Systems, Computer Networks, Compiler Design, Software Engineering, and Web Technologies.
These courses allow students to explore specialized areas within computer science. Students select electives based on their interests and career aspirations, choosing from options such as Artificial Intelligence, Cybersecurity, Data Science, Human-Computer Interaction, Mobile Computing, Internet of Things, Game Development, Computational Biology, and Quantitative Finance.
To broaden the educational experience, students also take science electives that complement their technical training. These include courses in Mathematics, Physics, Chemistry, and Biology, offering insights into how scientific principles apply to computing applications.
Each course is supported by laboratory components where students engage in hands-on experimentation and application of theoretical concepts. Labs provide opportunities for troubleshooting, debugging, and developing practical skills essential for professional success.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computing | 3-0-0-3 | - |
| 1 | CS103 | Digital Logic Design | 3-0-0-3 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS203 | Database Management Systems | 3-0-0-3 | CS101 |
| 3 | CS301 | Operating Systems | 3-0-0-3 | CS201, CS202 |
| 3 | CS302 | Computer Networks | 3-0-0-3 | CS201, CS202 |
| 3 | CS303 | Compiler Design | 3-0-0-3 | CS201, CS202 |
| 4 | CS401 | Software Engineering | 3-0-0-3 | CS201, CS202 |
| 4 | CS402 | Web Technologies | 3-0-0-3 | CS201, CS202 |
| 4 | CS403 | Mobile Application Development | 3-0-0-3 | CS201, CS202 |
| 5 | CS501 | Artificial Intelligence | 3-0-0-3 | CS201, CS202 |
| 5 | CS502 | Cybersecurity Fundamentals | 3-0-0-3 | CS201, CS202 |
| 5 | CS503 | Data Science and Analytics | 3-0-0-3 | CS201, CS202 |
| 6 | CS601 | Advanced Algorithms | 3-0-0-3 | CS201, CS202 |
| 6 | CS602 | Distributed Systems | 3-0-0-3 | CS301, CS302 |
| 6 | CS603 | Cloud Computing | 3-0-0-3 | CS301, CS302 |
| 7 | CS701 | Capstone Project I | 0-0-6-6 | All previous semesters |
| 8 | CS801 | Capstone Project II | 0-0-6-6 | All previous semesters |
The department offers a wide range of advanced elective courses that allow students to delve deeper into specific areas of interest within computer science. These courses are designed to be both rigorous and relevant, preparing students for advanced roles in industry or further academic pursuits.
This course explores the architecture and training of deep learning models, covering topics such as backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students engage in practical exercises using TensorFlow and PyTorch to build and train their own models.
Focused on image processing techniques and object recognition systems, this course introduces students to fundamental concepts like edge detection, feature extraction, and machine learning algorithms for visual data interpretation. Practical components include building a face recognition system or autonomous robot navigation.
Students learn cryptographic algorithms, secure communication protocols, and methods for protecting digital information. The course covers both symmetric and asymmetric encryption techniques, hash functions, digital signatures, and blockchain technology applications.
Utilizing tools like Hadoop and Spark, students gain experience in processing large datasets and extracting meaningful insights through statistical modeling and data mining techniques. The course includes hands-on labs where students work with real-world datasets from various industries.
This course examines the design and evaluation of interactive systems for users. Topics include usability testing, interface design principles, accessibility standards, and user experience research methods. Students often create prototypes and conduct user studies to refine their designs.
Through this elective, students learn to develop interactive entertainment software using modern game engines like Unity or Unreal. The curriculum covers game mechanics, scripting, visual design, sound integration, and optimization techniques for performance.
Students gain proficiency in developing cross-platform mobile applications using frameworks such as React Native or Flutter. The course includes designing user interfaces, integrating APIs, and deploying apps to app stores.
This course focuses on programming microcontrollers and embedded devices for real-time applications. Students learn about hardware-software co-design, real-time operating systems, and interfacing sensors and actuators in smart devices.
Combining mathematics and computer science with finance, this elective introduces students to financial modeling, algorithmic trading strategies, and risk management tools. Students implement pricing models for derivatives and perform portfolio optimization exercises.
This interdisciplinary course bridges biology and computational methods, focusing on bioinformatics applications such as genome assembly, protein structure prediction, and phylogenetic analysis. Students use programming languages like Python to analyze biological data sets.
Project-based learning is central to the department's philosophy, integrating theory with hands-on experience throughout the curriculum. This approach ensures that students not only understand academic concepts but also apply them in real-world scenarios.
Starting from the second year, students participate in mini-projects that simulate real-world challenges. These projects typically involve working in teams and require students to research, design, implement, and present solutions to specific problems. Mini-projects are assessed based on technical execution, teamwork, and presentation quality.
The final-year thesis or capstone project represents the culmination of a student's academic journey. Students select projects aligned with their interests and career goals, often in collaboration with faculty mentors or industry partners. The project involves extensive research, experimentation, and documentation.
Students are guided through a structured process to select appropriate projects based on their interests, available resources, and faculty expertise. Faculty mentors provide guidance throughout the project lifecycle, helping students navigate challenges and refine their approaches.
Projects are evaluated using multiple criteria including technical depth, innovation, feasibility, documentation quality, and presentation effectiveness. Regular progress reviews ensure that projects stay on track and meet academic standards.
