Curriculum Overview
The Computer Science And Engineering program at Dr. A P J Abdul Kalam Technical University Lucknow is structured to provide a balanced and comprehensive education that combines foundational knowledge with advanced technical skills and practical experience. The curriculum spans eight semesters, each building upon the previous one to ensure progressive learning outcomes.
First Year Courses
In the first year, students are introduced to essential concepts in mathematics, physics, and basic programming. These foundational courses lay the groundwork for more complex topics in subsequent years.
- Engineering Mathematics I: This course covers differential equations, integral calculus, vector analysis, and matrix algebra, providing students with the mathematical tools needed for advanced engineering concepts.
- Introduction to Programming using C: Students learn fundamental programming constructs such as variables, loops, functions, arrays, and pointers through hands-on exercises in C language.
- Basic Electrical and Electronics Engineering: Covers electrical circuits, magnetism, electromagnetism, semiconductor devices, and basic electronics components.
- Engineering Physics: Explores wave mechanics, optics, thermodynamics, quantum physics, and modern physics principles relevant to engineering applications.
- Communication Skills: Enhances oral and written communication abilities through presentations, debates, and writing assignments.
- Computer Programming Laboratory: Provides practical exposure to programming concepts learned in theory through laboratory sessions using C language.
Second Year Courses
The second year builds upon the first-year foundation by introducing core engineering subjects and expanding programming knowledge with object-oriented programming concepts.
- Engineering Mathematics II: Continues from the first year, covering ordinary differential equations, partial differential equations, Laplace transforms, and Fourier series.
- Data Structures and Algorithms: Introduces linear data structures like arrays, linked lists, stacks, queues, trees, and graphs, along with algorithmic analysis and sorting techniques.
- Digital Logic Design: Covers Boolean algebra, logic gates, combinational and sequential circuits, flip-flops, registers, counters, and memory units.
- Object Oriented Programming using Java: Teaches object-oriented concepts such as classes, objects, inheritance, polymorphism, encapsulation, and abstraction using Java language.
- Database Management Systems: Covers relational models, SQL queries, normalization, transaction management, indexing, and database design principles.
- Data Structures and Algorithms Laboratory: Offers practical implementation of data structures and algorithms learned in the theory course through lab projects.
Third Year Courses
The third year delves deeper into specialized areas such as operating systems, computer networks, software engineering, and microprocessor architecture.
- Operating Systems: Explores process management, memory management, file systems, scheduling algorithms, and deadlock handling in OS design.
- Computer Networks: Covers network topologies, protocols, TCP/IP model, routing, switching, network security, and wireless communication.
- Software Engineering: Introduces software development lifecycle, requirements analysis, design patterns, testing strategies, and project management tools.
- Design and Analysis of Algorithms: Focuses on algorithmic complexity, greedy methods, divide-and-conquer techniques, dynamic programming, and graph algorithms.
- Microprocessor and Microcontroller: Covers architecture, instruction set, assembly language programming, interfacing concepts, and embedded system design principles.
- Operating Systems Laboratory: Provides hands-on experience with operating system concepts through lab simulations and practical assignments.
Fourth Year Courses
The fourth year focuses on advanced topics in artificial intelligence, cybersecurity, web technologies, embedded systems, and cloud computing to prepare students for specialized roles in industry or further studies.
- Artificial Intelligence and Machine Learning: Introduces AI concepts, neural networks, machine learning algorithms, natural language processing, and deep learning techniques.
- Cybersecurity Fundamentals: Covers cryptography, network security protocols, ethical hacking, risk assessment, and incident response strategies.
- Web Technologies: Explores HTML/CSS, JavaScript frameworks, backend development using Node.js or Python Flask, RESTful APIs, and web security measures.
- Embedded Systems: Focuses on microcontroller architectures, real-time operating systems, sensor integration, and hardware-software co-design principles.
- Cloud Computing: Covers cloud architecture models, virtualization technologies, cloud deployment strategies, and major cloud platforms like AWS, Azure, and GCP.
- Cybersecurity Laboratory: Provides practical exposure to cybersecurity tools, vulnerability assessments, penetration testing, and security monitoring techniques.
Fifth Year Courses
The fifth year offers specialized elective courses that allow students to tailor their education according to their career goals and interests.
- Advanced Data Structures and Algorithms: Delves into complex data structures, algorithm optimization techniques, and advanced problem-solving strategies.
- Computer Vision and Image Processing: Covers image enhancement, segmentation, feature extraction, object detection, and computer vision applications using TensorFlow and OpenCV.
- Natural Language Processing: Explores text processing, language modeling, sentiment analysis, machine translation, and dialogue systems using NLP libraries.
- Distributed Systems: Introduces distributed computing paradigms, consensus algorithms, fault tolerance, and scalability challenges in large-scale systems.
- Human-Computer Interaction: Focuses on user-centered design principles, usability testing, interaction design patterns, and accessibility standards.
- Internet of Things (IoT) Laboratory: Provides practical implementation of IoT concepts using Raspberry Pi, Arduino, sensor networks, and cloud integration platforms.
Sixth Year Courses
The sixth year continues with advanced electives that reflect emerging trends in computing and technology innovation.
- Big Data Analytics: Covers Hadoop ecosystem, Spark frameworks, data mining techniques, predictive analytics, and big data visualization tools.
- Reinforcement Learning: Explores reinforcement learning algorithms, Markov decision processes, Q-learning, policy gradients, and applications in robotics and gaming.
- DevOps and CI/CD Pipelines: Introduces DevOps principles, continuous integration, containerization using Docker, orchestration with Kubernetes, and automation tools.
- Advanced Networking: Covers advanced routing protocols, network security, network performance tuning, and emerging networking technologies like SDN and NFV.
- Quantum Computing: Explores quantum algorithms, quantum gates, quantum error correction, and quantum programming using Qiskit and Cirq libraries.
- Software Testing and Quality Assurance: Focuses on testing methodologies, automated testing tools, quality metrics, test coverage analysis, and software validation techniques.
Seventh Year Courses
The seventh year introduces students to research methodology and prepares them for capstone projects or thesis work.
- Research Methodology: Covers research design, literature review techniques, hypothesis formulation, experimental design, and academic writing skills.
- Capstone Project - Part I: Students begin working on their capstone project under faculty supervision, selecting a topic, conducting literature reviews, and designing methodology.
Eighth Year Courses
The final year completes the academic journey with the execution of the capstone project or thesis work, culminating in presentations and evaluations.
- Capstone Project - Part II: Students finalize their research, implement solutions, document findings, and present results to faculty panels and industry experts.
Departmental Elective Courses
Departmental electives are offered in the fifth, sixth, seventh, and eighth semesters to allow students to specialize further based on their interests and career aspirations.
- Deep Learning for Computer Vision: This course explores convolutional neural networks, recurrent neural networks, transformer models, and their applications in image recognition, object detection, segmentation, and generation. Students will implement architectures using TensorFlow and PyTorch frameworks.
- Blockchain Technology and Applications: An exploration of blockchain fundamentals, consensus mechanisms, smart contracts, decentralized applications (dApps), and cryptographic protocols. Students will develop blockchain-based solutions using Ethereum and Hyperledger Fabric platforms.
- Computational Biology and Bioinformatics: Combines computational methods with biological data analysis to study genetics, genomics, proteomics, and drug discovery. Focuses on algorithms for sequence alignment, phylogenetic tree construction, and protein structure prediction.
- Recommender Systems: Covers collaborative filtering, content-based filtering, hybrid approaches, matrix factorization, neural network models, and evaluation metrics for recommendation engines used in streaming services, e-commerce platforms, and social media networks.
- Edge Computing and Mobile Robotics: Examines edge computing architectures, mobile robot navigation, sensor fusion, autonomous systems, and real-time decision-making algorithms. Students will build robotic prototypes using Raspberry Pi and Arduino platforms.
- Speech Recognition and Synthesis: Delves into speech signal processing, phonetic analysis, automatic speech recognition (ASR), text-to-speech (TTS) systems, speaker identification, and voice cloning technologies using deep learning models.
- Augmented Reality and Virtual Reality: Introduces AR/VR development environments, 3D modeling, interactive interfaces, spatial computing, immersive experiences, and applications in gaming, education, healthcare, and architecture.
- Network Security and Penetration Testing: Covers network vulnerabilities, penetration testing methodologies, ethical hacking techniques, firewall configurations, intrusion detection systems (IDS), and secure network design principles.
- Quantum Algorithms and Cryptography: Studies quantum algorithms like Shor’s algorithm, Grover’s search, and quantum key distribution. Includes practical implementation of quantum circuits using Qiskit and Cirq libraries on IBM Quantum Experience platform.
- Computer Graphics and Animation: Explores 3D rendering techniques, shading models, animation principles, texture mapping, lighting effects, and real-time graphics programming using OpenGL, DirectX, and Unity engine.
- Software Architecture and Design Patterns: Focuses on architectural styles, design patterns, microservices, service-oriented architecture (SOA), domain-driven design (DDD), and scalability planning for large-scale software systems.
- Mobile Application Development: Covers mobile app development using cross-platform frameworks like React Native, Flutter, Xamarin, and native development environments for iOS and Android platforms. Includes UI/UX considerations and deployment strategies.
- Database Systems and NoSQL Technologies: Examines relational database design, normalization, transaction processing, indexing strategies, distributed databases, document stores, key-value stores, graph databases, and time-series data handling.
- Human-Computer Interaction and User Experience Design: Addresses user-centered design principles, usability testing, prototyping tools (Figma, Sketch), accessibility standards, and interaction design patterns for web and mobile applications.
- Machine Learning for Natural Language Processing: Focuses on NLP preprocessing techniques, language modeling, sentiment analysis, named entity recognition, machine translation, question answering systems, and dialogue management using transformer-based models.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a cornerstone of the educational approach. Projects are structured to reflect real-world challenges and provide students with hands-on experience in applying theoretical knowledge.
Mini-Projects: Conducted in the third and fourth semesters, these projects allow students to explore specific areas of interest under faculty guidance. Mini-projects typically involve a small team of 3-4 members and last for 6-8 weeks.
Final-Year Thesis/Capstone Project: The capstone project is the culmination of the student's academic journey, requiring independent research or application of advanced technologies to solve complex problems. Students work closely with a faculty mentor throughout the process, which spans several months and involves literature review, prototype development, testing, documentation, and presentation.
Project selection is done based on student interest, available resources, industry relevance, and faculty expertise. Faculty mentors are chosen from departments with strong research backgrounds and industry exposure to ensure quality supervision and timely progress.
Evaluation criteria include technical competency, creativity, teamwork, documentation quality, presentation skills, and overall project impact. Regular milestones and progress reports help track performance and provide feedback for improvement.