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    +91 88943 57155
    Pune, Maharashtra, India

    Duration

    4 Years

    Computer Science

    Indira Gandhi Technological And Medical Science University Lower Subansiri
    Duration
    4 Years
     Computer Science UG OFFLINE

    Duration

    4 Years

    Computer Science

    Indira Gandhi Technological And Medical Science University Lower Subansiri
    Duration
    Apply

    Fees

    ₹1,50,000

    Placement

    92.0%

    Avg Package

    ₹7,50,000

    Highest Package

    ₹18,00,000

    OverviewAdmissionsCurriculumFeesPlacements
    4 Years
    Computer Science
    UG
    OFFLINE

    Fees

    ₹1,50,000

    Placement

    92.0%

    Avg Package

    ₹7,50,000

    Highest Package

    ₹18,00,000

    Seats

    80

    Students

    320

    ApplyCollege

    Seats

    80

    Students

    320

    Curriculum

    Comprehensive Course Structure

    The Computer Science program at Indira Gandhi Technological And Medical Science University Lower Subansiri is meticulously designed to provide a balanced mix of theoretical knowledge and practical skills. The curriculum spans 8 semesters, with each semester carrying a total credit load of approximately 16-18 credits.

    SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Prerequisites
    ICS101Introduction to Programming3-0-0-3-
    ICS102Mathematics for Computer Science4-0-0-4-
    ICS103Physics for Engineers3-0-0-3-
    ICS104Chemistry for Engineers3-0-0-3-
    ICS105Engineering Graphics and Design2-0-0-2-
    ICS106English for Engineers2-0-0-2-
    IICS201Data Structures and Algorithms3-0-0-3CS101
    IICS202Object-Oriented Programming3-0-0-3CS101
    IICS203Discrete Mathematics4-0-0-4CS102
    IICS204Digital Logic and Computer Organization3-0-0-3-
    IICS205Calculus for Engineers4-0-0-4-
    IIICS301Database Management Systems3-0-0-3CS201
    IIICS302Operating Systems3-0-0-3CS201
    IIICS303Computer Networks3-0-0-3CS204
    IIICS304Software Engineering3-0-0-3CS202
    IIICS305Probability and Statistics4-0-0-4CS102
    IVCS401Artificial Intelligence3-0-0-3CS301
    IVCS402Cybersecurity Fundamentals3-0-0-3CS303
    IVCS403Data Mining and Analytics3-0-0-3CS305
    IVCS404Distributed Systems3-0-0-3CS303
    IVCS405Human-Computer Interaction3-0-0-3CS201
    VCS501Machine Learning3-0-0-3CS401
    VCS502Cloud Computing3-0-0-3CS404
    VCS503Advanced Cybersecurity3-0-0-3CS402
    VCS504Big Data Technologies3-0-0-3CS301
    VCS505Research Methodology2-0-0-2-
    VICS601Deep Learning3-0-0-3CS501
    VICS602Internet of Things (IoT)3-0-0-3CS403
    VICS603Blockchain Technologies3-0-0-3CS503
    VICS604Mobile Application Development3-0-0-3CS202
    VICS605Project Planning and Management2-0-0-2-
    VIICS701Capstone Project I4-0-0-4CS505
    VIIICS801Capstone Project II6-0-0-6CS701

    Detailed Departmental Elective Courses

    Advanced departmental electives are offered to allow students to specialize further in their chosen fields:

    • Machine Learning (CS501): This course delves into supervised and unsupervised learning techniques, neural networks, reinforcement learning, and deep learning architectures. Students gain hands-on experience using frameworks like TensorFlow and PyTorch.
    • Cloud Computing (CS502): Explores cloud service models (IaaS, PaaS, SaaS), virtualization technologies, and deployment strategies. Students implement cloud-native applications using AWS, Azure, and Google Cloud Platform.
    • Advanced Cybersecurity (CS503): Covers advanced topics in cryptography, network security, ethical hacking, incident response, and compliance frameworks. Practical labs involve penetration testing and vulnerability assessments.
    • Big Data Technologies (CS504): Introduces students to Hadoop, Spark, NoSQL databases, and streaming analytics. Projects focus on processing large datasets for real-world applications.
    • Research Methodology (CS505): Teaches research design, data collection methods, statistical analysis, and academic writing. Prepares students for thesis development and publication opportunities.

    Project-Based Learning Philosophy

    The department strongly believes in experiential learning through project-based education. From the second year onwards, students are introduced to mini-projects that reinforce theoretical concepts. These projects are typically completed in teams of 3-5 members and involve mentorship from faculty members.

    For the final-year capstone project, students select a domain-specific problem and work closely with a faculty advisor to design, implement, and present a solution. The evaluation criteria include innovation, technical depth, documentation quality, and presentation skills.