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

    Duration

    4 Years

    Bachelor of Technology in Engineering

    Shridhar University Pilani
    Duration
    4 Years
    Engineering UG OFFLINE

    Duration

    4 Years

    Bachelor of Technology in Engineering

    Shridhar University Pilani
    Duration
    Apply

    Fees

    ₹4,50,000

    Placement

    95.0%

    Avg Package

    ₹6,50,000

    Highest Package

    ₹12,00,000

    OverviewAdmissionsCurriculumFeesPlacements
    4 Years
    Engineering
    UG
    OFFLINE

    Fees

    ₹4,50,000

    Placement

    95.0%

    Avg Package

    ₹6,50,000

    Highest Package

    ₹12,00,000

    Seats

    200

    Students

    2,000

    ApplyCollege

    Seats

    200

    Students

    2,000

    Curriculum

    Curriculum Overview

    The curriculum for the Engineering program at Shridhar University Pilani is designed to provide a comprehensive and rigorous education that combines theoretical knowledge with practical application. The program is structured over 8 semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory sessions. The curriculum emphasizes project-based learning, research, and industry relevance to ensure that students are well-prepared for their professional careers.

    SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Pre-requisites
    1ENG101Engineering Graphics2-0-2-3-
    1PHY101Physics for Engineers3-0-0-3-
    1MAT101Calculus and Differential Equations4-0-0-4-
    1CSE101Introduction to Programming2-0-2-3-
    1CHM101Chemistry for Engineers3-0-0-3-
    1ENG102Basic Electrical Engineering3-0-0-3-
    2MAT201Linear Algebra and Probability3-0-0-3MAT101
    2PHY201Electromagnetic Fields3-0-0-3PHY101
    2CHM201Organic Chemistry3-0-0-3CHM101
    2CSE201Data Structures and Algorithms3-0-0-3CSE101
    2ENG201Strength of Materials3-0-0-3ENG102
    2MAT202Statistics and Numerical Methods3-0-0-3MAT101
    3ENG301Thermodynamics3-0-0-3PHY201
    3ENG302Fluid Mechanics3-0-0-3PHY201
    3CSE301Database Management Systems3-0-0-3CSE201
    3ENG303Electrical Circuits3-0-0-3ENG102
    3MAT301Complex Analysis3-0-0-3MAT201
    3CHM301Physical Chemistry3-0-0-3CHM201
    4ENG401Heat Transfer3-0-0-3ENG301
    4ENG402Manufacturing Processes3-0-0-3ENG301
    4CSE401Machine Learning3-0-0-3CSE201
    4ENG403Control Systems3-0-0-3ENG303
    4MAT401Partial Differential Equations3-0-0-3MAT301
    4CHM401Chemical Engineering Principles3-0-0-3CHM301
    5ENG501Signals and Systems3-0-0-3ENG303
    5ENG502Structural Analysis3-0-0-3ENG301
    5CSE501Computer Networks3-0-0-3CSE201
    5ENG503Power Systems3-0-0-3ENG303
    5MAT501Advanced Mathematics3-0-0-3MAT401
    5CHM501Environmental Chemistry3-0-0-3CHM401
    6ENG601Robotics and Automation3-0-0-3ENG501
    6ENG602Geotechnical Engineering3-0-0-3ENG301
    6CSE601Deep Learning3-0-0-3CSE401
    6ENG603Renewable Energy Systems3-0-0-3ENG301
    6MAT601Operations Research3-0-0-3MAT501
    6CHM601Biochemistry3-0-0-3CHM501
    7ENG701Advanced Control Systems3-0-0-3ENG501
    7ENG702Advanced Materials3-0-0-3ENG602
    7CSE701Software Engineering3-0-0-3CSE601
    7ENG703Smart Grids3-0-0-3ENG503
    7MAT701Mathematical Modeling3-0-0-3MAT601
    7CHM701Industrial Chemistry3-0-0-3CHM601
    8ENG801Capstone Project4-0-0-4ENG701
    8ENG802Advanced Topics in Engineering3-0-0-3ENG701
    8CSE801Research Methodology3-0-0-3CSE701
    8ENG803Project Management3-0-0-3ENG701
    8MAT801Advanced Numerical Methods3-0-0-3MAT701
    8CHM801Chemical Process Design3-0-0-3CHM701

    Advanced Departmental Elective Courses

    The department offers several advanced elective courses that allow students to explore specialized areas of interest. These courses are designed to provide in-depth knowledge and practical skills in emerging fields. Below are detailed descriptions of some of the advanced elective courses:

    Machine Learning

    This course focuses on the principles and applications of machine learning algorithms. Students will learn about supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning. The course includes hands-on projects using Python and TensorFlow, providing students with practical experience in developing and deploying machine learning models.

    Deep Learning

    This course explores the theory and practice of deep learning, including convolutional neural networks, recurrent neural networks, and transformer models. Students will gain experience in building and training deep learning models using frameworks such as PyTorch and TensorFlow. The course also covers applications in computer vision, natural language processing, and speech recognition.

    Computer Vision

    This course introduces students to the fundamentals of computer vision and image processing. Topics include image filtering, feature detection, object recognition, and image segmentation. Students will work on projects involving real-world applications such as autonomous vehicles and medical image analysis.

    Database Management Systems

    This course covers the design and implementation of database systems. Students will learn about relational databases, SQL, normalization, and transaction management. The course includes hands-on experience with popular database management systems such as MySQL and PostgreSQL.

    Software Engineering

    This course provides an overview of software engineering principles and practices. Students will learn about software development life cycles, design patterns, testing, and project management. The course includes group projects that simulate real-world software development environments.

    Computer Networks

    This course covers the fundamentals of computer networking, including network protocols, architectures, and security. Students will learn about TCP/IP, routing, and wireless networks. The course includes hands-on experience with network simulation tools and practical networking exercises.

    Robotics and Automation

    This course introduces students to robotics and automation systems. Topics include robot kinematics, control systems, sensor integration, and autonomous navigation. Students will work on projects involving robotic platforms and automation technologies.

    Renewable Energy Systems

    This course explores the design and implementation of renewable energy systems. Students will learn about solar, wind, and hydroelectric power generation. The course includes hands-on projects involving renewable energy systems and energy storage technologies.

    Advanced Control Systems

    This course covers advanced topics in control systems, including state-space methods, optimal control, and robust control. Students will learn to design and analyze control systems for complex engineering applications. The course includes practical experience with control system simulation tools.

    Smart Grids

    This course focuses on the design and operation of smart grids. Students will learn about power system integration, energy management, and grid stability. The course includes hands-on experience with smart grid simulation tools and real-world case studies.

    Project-Based Learning Philosophy

    The department's philosophy on project-based learning emphasizes hands-on experience, real-world problem-solving, and collaboration. Students are encouraged to work on projects that address real-world challenges, applying their knowledge and skills in practical contexts.

    Mini-Projects

    Mini-projects are undertaken in the second and third years of the program. These projects are designed to reinforce theoretical concepts and provide students with practical experience. Students work in small groups and are mentored by faculty members. The projects are evaluated based on technical execution, presentation, and teamwork.

    Final-Year Thesis/Capstone Project

    The final-year thesis or capstone project is a significant component of the program. Students work on a substantial project that integrates their knowledge and skills. The project is typically sponsored by industry partners or conducted in collaboration with research labs. Students are assigned faculty mentors who guide them through the research and development process.

    Project Selection and Mentorship

    Students select their projects based on their interests and career goals. The selection process involves discussions with faculty mentors and industry partners. Faculty mentors are assigned based on their expertise and the relevance of their research to the student's project. The mentorship system ensures that students receive guidance and support throughout their project journey.