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₹8,00,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,50,000
Fees
₹8,00,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,50,000
Seats
250
Students
250
Seats
250
Students
250
The curriculum at Sai Nath University Ranchi is designed to provide a robust foundation in computer science principles while offering flexibility to explore specialized areas. The program spans 8 semesters and includes core courses, departmental electives, science electives, and laboratory sessions.
| Year | Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| I | I | CS101 | Introduction to Computer Science | 3-1-0-4 | - |
| CS102 | Mathematics for Computer Science | 4-0-0-4 | - | ||
| CS103 | Programming and Problem Solving | 3-0-2-5 | - | ||
| CS104 | Computer Organization and Architecture | 3-1-0-4 | - | ||
| I | II | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS103 |
| CS202 | Discrete Mathematics | 3-0-0-3 | - | ||
| CS203 | Object Oriented Programming | 3-0-2-5 | CS103 | ||
| CS204 | Database Systems | 3-1-0-4 | CS103 | ||
| II | III | CS301 | Software Engineering | 3-1-0-4 | CS203 |
| CS302 | Operating Systems | 3-1-0-4 | CS104 | ||
| CS303 | Computer Networks | 3-1-0-4 | CS204 | ||
| CS304 | Design and Analysis of Algorithms | 3-1-0-4 | CS201 | ||
| II | IV | CS401 | Artificial Intelligence | 3-1-0-4 | CS201 |
| CS402 | Cybersecurity Fundamentals | 3-1-0-4 | CS303 | ||
| CS403 | Data Science and Analytics | 3-1-0-4 | CS201 | ||
| CS404 | Web Technologies | 3-1-0-4 | CS203 | ||
| III | V | CS501 | Machine Learning | 3-1-0-4 | CS401 |
| CS502 | Network Security | 3-1-0-4 | CS402 | ||
| CS503 | Big Data Analytics | 3-1-0-4 | CS403 | ||
| CS504 | Cloud Computing | 3-1-0-4 | CS301 | ||
| III | VI | CS601 | Advanced Software Engineering | 3-1-0-4 | CS301 |
| CS602 | Internet of Things | 3-1-0-4 | CS303 | ||
| CS603 | Mobile Application Development | 3-1-0-4 | CS203 | ||
| CS604 | Human Computer Interaction | 3-1-0-4 | CS301 | ||
| IV | VII | CS701 | Capstone Project I | 0-0-6-6 | - |
| CS702 | Research Methodology | 3-0-0-3 | - | ||
| CS703 | Specialized Elective I | 3-1-0-4 | - | ||
| CS704 | Specialized Elective II | 3-1-0-4 | - | ||
| IV | VIII | CS801 | Capstone Project II | 0-0-6-6 | - |
| CS802 | Internship | 0-0-0-12 | - | ||
| CS803 | Specialized Elective III | 3-1-0-4 | - | ||
| CS804 | Specialized Elective IV | 3-1-0-4 | - |
The department emphasizes project-based learning as a core component of the educational experience. This approach ensures that students can apply theoretical concepts to practical problems and develop real-world skills.
Our advanced departmental elective courses are designed to provide in-depth knowledge and practical skills in specialized areas of computer science. Here are detailed descriptions of several key electives:
This course provides a comprehensive introduction to machine learning concepts and techniques. Students will learn various algorithms including supervised learning, unsupervised learning, reinforcement learning, and deep learning. The course covers both theoretical foundations and practical implementation using industry-standard frameworks like TensorFlow and PyTorch.
Learning objectives include understanding different types of machine learning problems, implementing common algorithms, evaluating model performance, and applying machine learning techniques to real-world scenarios. Students will work on projects involving data preprocessing, feature engineering, model selection, and deployment.
This course explores the principles and practices of network security. It covers topics such as cryptography, secure network protocols, intrusion detection systems, firewall design, and vulnerability assessment. Students will learn how to identify and mitigate security threats in network environments.
The course emphasizes practical applications through hands-on labs and case studies. Students will gain experience with security tools and techniques used in industry practice. The learning objectives include designing secure network architectures, implementing cryptographic solutions, and conducting security audits.
This course focuses on the techniques and technologies used for processing and analyzing large datasets. Students will learn about distributed computing frameworks like Hadoop and Spark, data warehousing concepts, and advanced analytics techniques.
Topics include data mining algorithms, statistical analysis methods, visualization tools, and machine learning applications in big data contexts. The course emphasizes hands-on experience with real-world datasets and industry-standard tools such as Apache Kafka, Elasticsearch, and Tableau.
This course introduces students to cloud computing concepts and architectures. It covers virtualization technologies, cloud service models (IaaS, PaaS, SaaS), cloud deployment models, and security considerations in cloud environments.
Students will gain practical experience with major cloud platforms including AWS, Microsoft Azure, and Google Cloud Platform. The course includes hands-on labs for designing and deploying cloud applications, understanding cost optimization strategies, and implementing scalable solutions.
This advanced course focuses on modern software engineering practices and methodologies. It covers topics such as agile development, DevOps practices, continuous integration and delivery, microservices architecture, and software testing strategies.
Students will work on large-scale software projects using industry-standard tools and methodologies. The learning objectives include understanding software architecture patterns, implementing CI/CD pipelines, managing software quality, and leading software development teams.
This course explores the design and implementation of IoT systems. It covers sensor networks, embedded systems programming, wireless communication protocols, and data processing in IoT environments.
Students will learn about various IoT platforms, device connectivity, security challenges in IoT, and applications in smart cities, healthcare, and industrial automation. The course includes hands-on projects involving Raspberry Pi, Arduino, and other IoT development platforms.
This course provides comprehensive training in mobile application development for both Android and iOS platforms. It covers user interface design principles, mobile programming frameworks, app deployment strategies, and performance optimization techniques.
Students will develop full-featured mobile applications using modern development tools and methodologies. The learning objectives include creating responsive interfaces, integrating with backend services, implementing offline capabilities, and ensuring app security and performance.
This course focuses on the design and evaluation of user interfaces for computer systems. It covers cognitive psychology principles, interaction design techniques, usability testing methods, and accessibility considerations.
Students will learn about user-centered design processes, prototyping tools, and evaluation frameworks. The course includes hands-on projects involving interface design, user research, and usability testing with real users.
This course provides an in-depth exploration of computer graphics techniques and applications. It covers topics such as 3D modeling, rendering algorithms, animation techniques, and virtual reality systems.
Students will learn to create visual effects using industry-standard software and programming languages. The learning objectives include understanding geometric transformations, implementing lighting models, creating animations, and developing interactive graphics applications.
This course focuses on the principles and techniques of data visualization. It covers visual encoding methods, chart types, interactive visualization tools, and storytelling with data.
Students will learn to create compelling visual representations of complex datasets using tools like D3.js, Python libraries, and commercial software. The learning objectives include selecting appropriate visualization methods, designing effective dashboards, and communicating insights through visual means.
The department's philosophy on project-based learning is centered on providing students with opportunities to apply theoretical knowledge to real-world problems. This approach emphasizes collaborative work, problem-solving skills, and practical application of concepts learned in class.
Mini-projects are assigned throughout the program to reinforce learning objectives and provide early exposure to industry practices. These projects typically involve working in small teams to solve specific problems within a defined timeframe. Students receive guidance from faculty mentors and are evaluated on both technical execution and teamwork skills.
The final-year thesis/capstone project is a significant component of the program that allows students to demonstrate their mastery of computer science concepts. The project involves developing a substantial software system or conducting original research in an area of interest. Students work closely with faculty mentors throughout the process, which includes project planning, implementation, testing, and documentation.
Students select their projects based on their interests and career aspirations, often aligning with ongoing research initiatives or industry collaborations. Faculty mentors are assigned based on their expertise in relevant areas and availability to guide students through their projects.
The evaluation criteria for projects include technical correctness, innovation, presentation quality, documentation completeness, and overall contribution to the field. Students are required to present their work at departmental symposiums and potentially at national or international conferences.
