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    Scholarships & exams

    support@collegese.com
    +91 88943 57155
    Pune, Maharashtra, India

    Duration

    4 Years

    Computer Science

    Plaksha University, Mohali
    Duration
    4 Years
    Computer Science UG OFFLINE

    Duration

    4 Years

    Computer Science

    Plaksha University, Mohali
    Duration
    Apply

    Fees

    ₹3,50,000

    Placement

    94.5%

    Avg Package

    ₹7,50,000

    Highest Package

    ₹12,00,000

    OverviewAdmissionsCurriculumFeesPlacements
    4 Years
    Computer Science
    UG
    OFFLINE

    Fees

    ₹3,50,000

    Placement

    94.5%

    Avg Package

    ₹7,50,000

    Highest Package

    ₹12,00,000

    Seats

    150

    Students

    600

    ApplyCollege

    Seats

    150

    Students

    600

    Curriculum

    Comprehensive Course Structure

    The Computer Science curriculum at Plaksha University Mohali is meticulously structured across eight semesters to ensure a progressive and holistic learning experience. The program includes core courses, departmental electives, science electives, and laboratory components that are designed to build both theoretical understanding and practical application.

    SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Prerequisites
    1CSE101Introduction to Programming with Python3-0-0-3-
    1CSE102Mathematics for Computer Science I4-0-0-4-
    1CSE103Engineering Graphics and Design2-0-0-2-
    1SC101Physics for Engineers3-0-0-3-
    1SC102Chemistry Laboratory0-0-2-1-
    2CSE201Data Structures and Algorithms3-0-0-3CSE101
    2CSE202Mathematics for Computer Science II4-0-0-4CSE102
    2CSE203Digital Logic and Computer Organization3-0-0-3-
    2SC201Biology for Engineers3-0-0-3-
    2SC202Mathematics Lab I0-0-2-1-
    3CSE301Database Management Systems3-0-0-3CSE201
    3CSE302Operating Systems3-0-0-3CSE203
    3CSE303Computer Networks3-0-0-3CSE201
    3DE301Introduction to Software Engineering3-0-0-3-
    3DE302Human Computer Interaction3-0-0-3-
    4CSE401Machine Learning3-0-0-3CSE201
    4CSE402Computer Vision3-0-0-3CSE201
    4CSE403Distributed Systems3-0-0-3CSE203
    4DE401Advanced Software Engineering3-0-0-3DE301
    4DE402Cybersecurity Fundamentals3-0-0-3-
    5CSE501Deep Learning3-0-0-3CSE401
    5CSE502Natural Language Processing3-0-0-3CSE401
    5DE501Cloud Computing3-0-0-3-
    5DE502Big Data Analytics3-0-0-3-
    6CSE601Reinforcement Learning3-0-0-3CSE401
    6CSE602Internet of Things3-0-0-3-
    6DE601Blockchain Technologies3-0-0-3-
    7CSE701Research Methodology2-0-0-2-
    7DE701Capstone Project I3-0-0-3-
    8DE801Capstone Project II4-0-0-4DE701

    Advanced Departmental Electives

    Departmental electives play a crucial role in allowing students to explore areas of personal interest and professional relevance. These courses are designed to provide deep insights into specialized domains such as AI, cybersecurity, cloud computing, and data science.

    • Machine Learning: This course delves into supervised and unsupervised learning algorithms, neural networks, and reinforcement learning techniques, preparing students for roles in data science and artificial intelligence.
    • Computer Vision: Students learn how to process and interpret visual information using deep learning models, with applications in robotics, medical imaging, and autonomous vehicles.
    • Distributed Systems: This course focuses on the design and implementation of systems that span multiple computers, covering topics like consensus algorithms, fault tolerance, and scalability principles.
    • Advanced Software Engineering: Emphasizes modern software development practices including agile methodologies, DevOps, and system design patterns.
    • Cybersecurity Fundamentals: Covers essential concepts in information security, including encryption, network security, and ethical hacking.
    • Cloud Computing: Introduces cloud platforms like AWS, Azure, and GCP, focusing on deployment strategies, virtualization, and scalable architecture design.
    • Big Data Analytics: Explores tools such as Hadoop, Spark, and NoSQL databases to analyze large datasets for business intelligence and predictive modeling.
    • Reinforcement Learning: Focuses on decision-making in uncertain environments using algorithms that learn optimal actions through trial and error.
    • Internet of Things (IoT): Covers sensor networks, embedded systems, and wireless communication protocols used in smart cities and industrial automation.
    • Blockchain Technologies: Provides an overview of blockchain architecture, smart contracts, and decentralized applications with real-world use cases.

    Project-Based Learning Philosophy

    At Plaksha University Mohali, project-based learning is the cornerstone of our academic approach. Students engage in both mini-projects and a final-year thesis that reflect their interests and career aspirations. The program emphasizes hands-on experimentation, collaboration, and innovation.

    Mini-projects are introduced in the second year and require students to work in teams on real-world problems assigned by faculty or industry partners. These projects are evaluated based on technical execution, teamwork, and presentation skills.

    The final-year capstone project is a significant component of the program, where students select an area of interest and work under the guidance of a faculty mentor. Projects may lead to publications, patents, or startup ventures, offering students tangible outcomes that enhance their professional profiles.