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Fees
₹1,30,500
Placement
96.5%
Avg Package
₹9,50,000
Highest Package
₹18,00,000
Fees
₹1,30,500
Placement
96.5%
Avg Package
₹9,50,000
Highest Package
₹18,00,000
Seats
120
Students
600
Seats
120
Students
600
The Computer Science program at Aisect University Hazaribagh is designed to provide students with a comprehensive foundation in computing principles, followed by specialized knowledge and practical experience. The curriculum is structured over eight semesters, ensuring a progressive learning journey that balances theoretical understanding with real-world application.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | None |
| 1 | CS102 | Mathematics for Computing | 4-0-0-4 | None |
| 1 | CS103 | Computer Organization | 3-0-0-3 | None |
| 1 | CS104 | English for Technical Communication | 2-0-0-2 | None |
| 1 | CS105 | Introduction to Computer Science Lab | 0-0-3-1 | None |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101, CS102 |
| 2 | CS202 | Digital Logic Design | 3-0-0-3 | CS103 |
| 2 | CS203 | Object-Oriented Programming | 3-0-0-3 | CS101 |
| 2 | CS204 | Probability and Statistics | 3-0-0-3 | CS102 |
| 2 | CS205 | Data Structures Lab | 0-0-3-1 | CS201, CS101 |
| 3 | CS301 | Databases and SQL | 3-0-0-3 | CS201, CS203 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS103, CS202 |
| 3 | CS303 | Computer Networks | 3-0-0-3 | CS202 |
| 3 | CS304 | Software Engineering | 3-0-0-3 | CS203 |
| 3 | CS305 | Databases Lab | 0-0-3-1 | CS301, CS201 |
| 4 | CS401 | Artificial Intelligence | 3-0-0-3 | CS201, CS204 |
| 4 | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS203, CS303 |
| 4 | CS403 | Data Science and Analytics | 3-0-0-3 | CS204, CS301 |
| 4 | CS404 | Human-Computer Interaction | 3-0-0-3 | CS203 |
| 4 | CS405 | AI and Machine Learning Lab | 0-0-3-1 | CS401, CS201 |
| 5 | CS501 | Cloud Computing | 3-0-0-3 | CS302, CS303 |
| 5 | CS502 | Internet of Things | 3-0-0-3 | CS202, CS301 |
| 5 | CS503 | Software Architecture and Design Patterns | 3-0-0-3 | CS304 |
| 5 | CS504 | Advanced Algorithms | 3-0-0-3 | CS201 |
| 5 | CS505 | DevOps and CI/CD Tools | 0-0-3-1 | CS304 |
| 6 | CS601 | Machine Learning Projects | 3-0-0-3 | CS401, CS403 |
| 6 | CS602 | Cybersecurity Projects | 3-0-0-3 | CS402 |
| 6 | CS603 | Data Visualization and Reporting | 3-0-0-3 | CS403 |
| 6 | CS604 | Game Development | 3-0-0-3 | CS203 |
| 6 | CS605 | Capstone Project Preparation | 0-0-3-1 | CS501, CS502 |
| 7 | CS701 | Research Methodology | 3-0-0-3 | CS601 |
| 7 | CS702 | Advanced Topics in AI | 3-0-0-3 | CS401 |
| 7 | CS703 | Blockchain Technologies | 3-0-0-3 | CS301 |
| 7 | CS704 | Quantum Computing | 3-0-0-3 | CS204, CS401 |
| 7 | CS705 | Capstone Project Implementation | 0-0-6-2 | CS601, CS602, CS603 |
| 8 | CS801 | Internship | 0-0-0-6 | CS705 |
The following are detailed descriptions of advanced departmental elective courses that build upon foundational knowledge and prepare students for specialized roles in the industry.
This course provides an in-depth exploration of neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students learn to implement models using TensorFlow and apply them to real-world datasets such as image classification, natural language processing, and time series forecasting. The curriculum includes hands-on labs where students experiment with different architectures and optimize model performance using techniques like transfer learning and regularization.
This course delves into modern cryptographic protocols including quantum-resistant encryption, blockchain security, and secure multi-party computation. Students study advanced concepts such as elliptic curve cryptography, zero-knowledge proofs, and homomorphic encryption. The course emphasizes practical implementation of security measures in network environments, preparing students for roles in cybersecurity consulting, penetration testing, and secure system design.
Students learn advanced techniques for extracting patterns from large datasets using tools like Hadoop, Spark, and MapReduce. The course covers graph analytics, time-series forecasting, clustering algorithms, and anomaly detection. Through projects involving real-world datasets, students gain experience in data preprocessing, feature engineering, and building predictive models that can handle scalability challenges.
This course focuses on automated testing frameworks, continuous integration pipelines, and quality metrics used in enterprise software development. Students learn to design test cases, implement regression testing strategies, and integrate testing into agile development processes. The curriculum includes exposure to tools like Selenium, JUnit, Jenkins, and SonarQube, equipping students with industry-standard practices for ensuring software reliability.
This course studies algorithms for object detection, segmentation, and recognition using deep learning. Students work with real-world image datasets and build end-to-end vision systems for applications in autonomous vehicles, medical imaging, and surveillance. The curriculum includes hands-on labs where students experiment with different architectures such as YOLO, Faster R-CNN, and U-Net, optimizing performance for various deployment scenarios.
An exploration of language models, sentiment analysis, machine translation, and dialogue systems. Students implement transformer-based architectures for text generation and understanding using frameworks like Hugging Face Transformers. Projects involve building chatbots, translating documents between languages, and analyzing textual content for business intelligence applications.
This course teaches students how to build cross-platform mobile applications using Flutter and React Native, with a focus on user experience and performance optimization. Students learn about mobile design principles, app store deployment, and backend integration. The curriculum includes developing apps for both iOS and Android platforms, ensuring compatibility and responsiveness across devices.
Students learn about microcontrollers, real-time operating systems, and low-level programming for IoT devices. The course covers hardware-software co-design, interrupt handling, and power management strategies. Through hands-on labs, students design and prototype embedded systems for various applications including smart sensors, wearable devices, and industrial control systems.
This course emphasizes designing technology solutions that are accessible, inclusive, and user-friendly. Students conduct user research, create prototypes, and evaluate design effectiveness using usability testing methods. The curriculum focuses on inclusive design principles and accessibility standards, preparing students to develop products that cater to diverse user needs and preferences.
This course covers advanced topics in database architecture, query optimization, indexing strategies, and distributed databases. Students learn to optimize database performance for large-scale applications, implementing techniques such as partitioning, caching, and replication. The curriculum includes hands-on experience with relational and NoSQL databases, preparing students for roles in data architecture and database administration.
An introduction to reinforcement learning algorithms and their applications in robotics, gaming, and autonomous systems. Students implement RL agents using Python and Gym environments, exploring concepts like Q-learning, policy gradients, and actor-critic methods. The course includes practical projects involving game-playing AI, robotic navigation, and decision-making under uncertainty.
This course explores the principles of quantum computing and develops algorithms for solving computational problems faster than classical computers. Students study quantum gates, superposition, entanglement, and measurement. The curriculum includes implementing quantum circuits using Qiskit and simulating quantum algorithms on quantum computers.
Students learn about decentralized ledgers, smart contract programming using Solidity, and blockchain-based applications in finance, supply chain, and governance. The course covers consensus mechanisms, cryptographic hashing, and token economics. Through projects, students build decentralized applications (dApps) and explore use cases in digital identity, voting systems, and asset management.
This course studies distributed computing architectures that bring computation closer to data sources. Students explore edge devices, latency optimization, and network reliability in edge environments. Projects involve designing edge computing solutions for smart cities, autonomous vehicles, and industrial IoT applications.
This course covers AR/VR development frameworks, 3D modeling, and immersive user interfaces for entertainment, education, and training applications. Students learn to build interactive experiences using Unity and Unreal Engine, incorporating motion tracking, spatial audio, and haptic feedback.
Our department believes that learning by doing is the most effective way to master complex concepts. Project-based learning is integrated throughout the curriculum, beginning with small individual assignments and progressing to large-scale collaborative projects.
The structure of these projects follows a systematic approach: problem identification, literature review, design phase, implementation, testing, documentation, and presentation. Students work in teams, often guided by faculty mentors who provide feedback and ensure alignment with industry standards.
Evaluation criteria include technical competency, innovation, teamwork, presentation skills, and adherence to deadlines. The final-year thesis or capstone project allows students to showcase their expertise and contribute original research or practical solutions to real-world challenges.
