Curriculum Overview
The curriculum at Al Falah University Faridabad's Computer Science program is meticulously designed to provide a comprehensive yet flexible educational experience. The eight-semester structure ensures that students acquire foundational knowledge in mathematics, science, and engineering principles before specializing in advanced topics.
The program integrates core theoretical concepts with practical applications through laboratory sessions, group projects, and industry exposure. Students are encouraged to engage in research activities early on, preparing them for both immediate career entry and further academic pursuits.
Semester-wise Course Structure
| SEMESTER | COURSE CODE | COURSE TITLE | CR. HOURS (L-T-P-C) | PRE-REQUISITES |
|---|---|---|---|---|
| 1 | MATH101 | Calculus I | 3-0-0-3 | - |
| 1 | PHYS101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CHEM101 | Chemistry | 3-0-0-3 | - |
| 1 | ENG101 | English Communication Skills | 2-0-0-2 | - |
| 1 | CSE101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CSE102 | Programming Lab | 0-0-3-1 | - |
| 1 | MATH102 | Calculus II | 3-0-0-3 | MATH101 |
| 1 | PHYS102 | Basic Electronics | 3-0-0-3 | - |
| 2 | MATH201 | Linear Algebra and Differential Equations | 3-0-0-3 | MATH102 |
| 2 | CSE201 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| 2 | CSE202 | Data Structures Lab | 0-0-3-1 | CSE102 |
| 2 | CSE203 | Digital Logic Design | 3-0-0-3 | - |
| 2 | CSE204 | Digital Logic Lab | 0-0-3-1 | - |
| 2 | CSE205 | Computer Organization | 3-0-0-3 | - |
| 2 | CSE206 | Computer Organization Lab | 0-0-3-1 | - |
| 3 | CSE301 | Database Management Systems | 3-0-0-3 | CSE201 |
| 3 | CSE302 | Database Lab | 0-0-3-1 | - |
| 3 | CSE303 | Operating Systems | 3-0-0-3 | CSE205 |
| 3 | CSE304 | Operating Systems Lab | 0-0-3-1 | - |
| 3 | CSE305 | Object-Oriented Programming | 3-0-0-3 | CSE201 |
| 3 | CSE306 | OOP Lab | 0-0-3-1 | - |
| 4 | CSE401 | Software Engineering | 3-0-0-3 | CSE305 |
| 4 | CSE402 | Software Engineering Lab | 0-0-3-1 | - |
| 4 | CSE403 | Computer Networks | 3-0-0-3 | CSE205 |
| 4 | CSE404 | Computer Networks Lab | 0-0-3-1 | - |
| 4 | CSE405 | Web Technologies | 3-0-0-3 | CSE305 |
| 4 | CSE406 | Web Technologies Lab | 0-0-3-1 | - |
| 5 | CSE501 | Machine Learning | 3-0-0-3 | MATH201 |
| 5 | CSE502 | ML Lab | 0-0-3-1 | - |
| 5 | CSE503 | Cybersecurity Fundamentals | 3-0-0-3 | CSE403 |
| 5 | CSE504 | Cybersecurity Lab | 0-0-3-1 | - |
| 5 | CSE505 | Data Mining and Analytics | 3-0-0-3 | CSE301 |
| 5 | CSE506 | Data Mining Lab | 0-0-3-1 | - |
| 6 | CSE601 | Advanced Algorithms | 3-0-0-3 | CSE201 |
| 6 | CSE602 | Algorithm Design Lab | 0-0-3-1 | - |
| 6 | CSE603 | Human Computer Interaction | 3-0-0-3 | CSE501 |
| 6 | CSE604 | Human Computer Interaction Lab | 0-0-3-1 | - |
| 6 | CSE605 | Cloud Computing | 3-0-0-3 | CSE403 |
| 6 | CSE606 | Cloud Computing Lab | 0-0-3-1 | - |
| 7 | CSE701 | Research Methodology | 3-0-0-3 | - |
| 7 | CSE702 | Capstone Project I | 0-0-6-2 | - |
| 8 | CSE801 | Capstone Project II | 0-0-6-2 | - |
Advanced Departmental Electives
Departmental electives in the Computer Science program allow students to explore specialized areas of interest and gain deeper insights into emerging technologies. These courses are designed to complement core subjects and prepare students for advanced roles in their chosen fields.
1. Machine Learning
This course introduces students to the principles and practices of machine learning, covering supervised and unsupervised learning techniques. Students learn how to apply algorithms such as decision trees, neural networks, clustering, and reinforcement learning to solve real-world problems. The course includes hands-on labs where students implement models using Python and libraries like TensorFlow and Scikit-learn.
2. Cybersecurity Fundamentals
This elective explores the foundational concepts of cybersecurity, including network security protocols, cryptography, ethical hacking, and risk management. Students gain practical experience through simulated attacks and defensive strategies, preparing them for roles in IT security and compliance.
3. Data Mining and Analytics
Focused on extracting patterns from large datasets, this course covers data preprocessing, clustering, classification, regression, and association rule mining. Students learn to use tools like R and Python to analyze business intelligence and make data-driven decisions.
4. Software Engineering
This course emphasizes the lifecycle of software development, including requirements analysis, design, implementation, testing, and maintenance. Students work on group projects that simulate real-world scenarios, applying agile methodologies and version control systems like Git.
5. Human-Computer Interaction
Designed to understand how people interact with technology, this course covers usability principles, user experience design, and interface prototyping. Students learn to conduct usability studies and create accessible digital products using modern tools and frameworks.
6. Cloud Computing
This elective explores the architecture, deployment models, and services offered by cloud platforms like AWS, Azure, and GCP. Students gain hands-on experience in designing scalable applications and managing infrastructure in virtual environments.
7. Embedded Systems
Focused on developing software for embedded devices, this course covers microcontroller architectures, real-time operating systems, and sensor integration. Students build projects using Arduino and Raspberry Pi to understand low-level programming and hardware-software interaction.
8. Game Development
This course introduces students to game design principles and development tools such as Unity and Unreal Engine. Students learn about 3D modeling, animation, scripting, and game physics to create interactive experiences.
9. Internet of Things (IoT)
Exploring the integration of physical devices with the internet, this course covers IoT architecture, wireless communication protocols, data processing, and security considerations. Students develop projects involving smart home systems and wearable technologies.
10. Quantum Computing
Introducing the fundamentals of quantum mechanics and its application in computing, this course covers qubit manipulation, quantum algorithms, and error correction methods. Students gain exposure to quantum simulation tools and explore potential applications in cryptography and optimization.
Project-Based Learning Philosophy
Our program places a strong emphasis on project-based learning as a means of reinforcing theoretical concepts and developing practical skills. Projects are integrated throughout the curriculum, beginning with mini-projects in early semesters and culminating in a comprehensive capstone project in the final year.
The mini-projects, typically undertaken in groups, are designed to address specific problems or challenges within computer science domains. These projects encourage collaboration, critical thinking, and technical communication among students. Faculty mentors guide students through each stage of the project lifecycle, from ideation to implementation and presentation.
The capstone project is a significant component of the program that requires students to work independently or in small teams to develop a substantial solution to a real-world problem. The project spans two semesters and involves extensive research, design, development, testing, and documentation. Students are paired with faculty mentors who provide expert guidance throughout the process.
Students select their projects based on personal interests, faculty expertise, and industry relevance. The selection process includes a proposal phase where students present their ideas to faculty members and receive feedback. Projects may be sourced from industry partners, government initiatives, or original research concepts proposed by students themselves.