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Fees
₹12,00,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Fees
₹12,00,000
Placement
92.0%
Avg Package
₹4,50,000
Highest Package
₹8,00,000
Seats
100
Students
600
Seats
100
Students
600
The curriculum for the Engineering program at Future University Bareilly is meticulously designed to ensure a balanced and progressive learning experience. The program spans eight semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MAT101 | Calculus I | 3-1-0-4 | - |
| 1 | PHY101 | Physics I | 3-1-0-4 | - |
| 1 | CHE101 | Chemistry I | 3-1-0-4 | - |
| 1 | ENG101 | English Communication | 2-0-0-2 | - |
| 1 | COM101 | Computer Programming | 2-1-0-3 | - |
| 1 | LAB101 | Programming Lab | 0-0-3-1 | - |
| 2 | MAT102 | Calculus II | 3-1-0-4 | MAT101 |
| 2 | PHY102 | Physics II | 3-1-0-4 | PHY101 |
| 2 | CHE102 | Chemistry II | 3-1-0-4 | CHE101 |
| 2 | ENG102 | Technical Writing | 2-0-0-2 | - |
| 2 | COM102 | Data Structures and Algorithms | 3-1-0-4 | COM101 |
| 2 | LAB102 | Data Structures Lab | 0-0-3-1 | COM101 |
| 3 | MAT201 | Differential Equations | 3-1-0-4 | MAT102 |
| 3 | PHY201 | Electromagnetic Fields | 3-1-0-4 | PHY102 |
| 3 | ECE201 | Basic Electronics | 3-1-0-4 | - |
| 3 | MECH201 | Engineering Mechanics | 3-1-0-4 | - |
| 3 | CIVIL201 | Strength of Materials | 3-1-0-4 | - |
| 3 | COM201 | Object Oriented Programming | 3-1-0-4 | COM102 |
| 3 | LAB201 | OOP Lab | 0-0-3-1 | COM102 |
| 4 | MAT202 | Linear Algebra | 3-1-0-4 | MAT201 |
| 4 | ECE202 | Circuit Analysis | 3-1-0-4 | ECE201 |
| 4 | MECH202 | Thermodynamics | 3-1-0-4 | MECH201 |
| 4 | CIVIL202 | Structural Analysis | 3-1-0-4 | CIVIL201 |
| 4 | COM202 | Database Management Systems | 3-1-0-4 | COM201 |
| 4 | LAB202 | DBMS Lab | 0-0-3-1 | COM201 |
| 5 | MAT301 | Probability and Statistics | 3-1-0-4 | MAT202 |
| 5 | ECE301 | Digital Electronics | 3-1-0-4 | ECE202 |
| 5 | MECH301 | Mechanics of Materials | 3-1-0-4 | MECH202 |
| 5 | CIVIL301 | Geotechnical Engineering | 3-1-0-4 | CIVIL202 |
| 5 | COM301 | Software Engineering | 3-1-0-4 | COM202 |
| 5 | LAB301 | Software Engineering Lab | 0-0-3-1 | COM202 |
| 6 | MAT302 | Numerical Methods | 3-1-0-4 | MAT301 |
| 6 | ECE302 | Signals and Systems | 3-1-0-4 | ECE301 |
| 6 | MECH302 | Mechatronics | 3-1-0-4 | MECH301 |
| 6 | CIVIL302 | Transportation Engineering | 3-1-0-4 | CIVIL301 |
| 6 | COM302 | Computer Networks | 3-1-0-4 | COM301 |
| 6 | LAB302 | Computer Networks Lab | 0-0-3-1 | COM301 |
| 7 | ECE401 | Control Systems | 3-1-0-4 | ECE302 |
| 7 | MECH401 | Design of Machine Elements | 3-1-0-4 | MECH302 |
| 7 | CIVIL401 | Hydraulic Engineering | 3-1-0-4 | CIVIL302 |
| 7 | COM401 | Artificial Intelligence | 3-1-0-4 | COM302 |
| 7 | LAB401 | AI Lab | 0-0-3-1 | COM302 |
| 8 | ECE402 | Embedded Systems | 3-1-0-4 | ECE401 |
| 8 | MECH402 | Advanced Manufacturing Processes | 3-1-0-4 | MECH401 |
| 8 | CIVIL402 | Environmental Engineering | 3-1-0-4 | CIVIL401 |
| 8 | COM402 | Machine Learning | 3-1-0-4 | COM401 |
| 8 | LAB402 | ML Lab | 0-0-3-1 | COM401 |
The department offers several advanced elective courses that cater to specialized interests and emerging trends in engineering. These courses are designed to deepen students' understanding of specific domains while encouraging innovation and interdisciplinary thinking.
This course introduces students to the foundational concepts of artificial intelligence, including search algorithms, knowledge representation, reasoning systems, and machine learning basics. Students learn to implement AI models using Python and TensorFlow libraries, preparing them for advanced specialization in AI-related fields.
Students explore the principles of cybersecurity, network security protocols, encryption techniques, and ethical hacking methodologies. Through hands-on labs, they gain experience in penetration testing, vulnerability assessment, and secure coding practices essential for protecting digital assets.
This course focuses on designing and analyzing renewable energy systems such as solar photovoltaic panels, wind turbines, and hydroelectric generators. Students evaluate system efficiency, perform cost-benefit analyses, and develop solutions for integrating clean energy into existing power grids.
Students learn to analyze biological signals using digital signal processing techniques. The course covers EEG, ECG, and EMG data analysis, medical imaging, and biosensors. Practical applications include developing diagnostic tools and monitoring systems for healthcare environments.
This course examines sustainable approaches to urban development, including green building design, smart city technologies, and environmental impact assessment. Students work on real-world projects involving waste management, energy efficiency, and transportation planning in urban settings.
Students study the principles of robotics, control systems, sensors, actuators, and automation technologies. Through lab sessions, they build robots and implement control algorithms for autonomous navigation, object recognition, and manipulation tasks.
This course provides students with tools and techniques for extracting insights from large datasets using statistical methods, machine learning algorithms, and data visualization software. Students learn to apply these skills in business intelligence, marketing analytics, and scientific research contexts.
Students explore the properties, synthesis, characterization, and applications of advanced materials including ceramics, composites, polymers, and nanomaterials. They study material selection criteria for engineering applications and conduct experiments in modern materials testing facilities.
This course delves into the architecture, operation, and control of smart electrical grids. Students examine renewable energy integration, demand response systems, energy storage technologies, and grid stability challenges. They also explore policy frameworks governing smart grid deployment.
Students are introduced to quantum mechanics, qubits, quantum gates, and quantum algorithms. The course covers current research trends in quantum computing, including error correction, quantum cryptography, and potential applications in optimization and simulation problems.
The department emphasizes project-based learning as a core component of the curriculum. Students begin with mini-projects in their second year, progressing to more complex capstone initiatives in their final year.
Mini-projects are typically completed over a period of 4-6 weeks and involve small teams working on specific engineering challenges. These projects encourage experimentation, problem-solving, and teamwork while reinforcing theoretical concepts learned in class.
The final-year thesis or capstone project is a significant undertaking that spans several months. Students select topics aligned with their interests and career goals, often in collaboration with faculty mentors or industry partners. The project involves literature review, design, implementation, testing, and documentation phases.
Evaluation criteria include technical depth, innovation, presentation quality, peer feedback, and final deliverables. Faculty members guide students throughout the process, ensuring that projects meet academic standards while fostering creativity and independence.
