Course Structure Overview
The engineering program at G D Goenka University Gurugram is meticulously structured to ensure a seamless transition from foundational knowledge to specialized expertise. The curriculum spans eight semesters, with each semester comprising core subjects, departmental electives, science electives, and laboratory sessions designed to build both theoretical understanding and practical application.
| SEMESTER | COURSE CODE | COURSE TITLE | L-T-P-C | PRE-REQUISITES |
|---|---|---|---|---|
| I | PH101 | Physics for Engineers | 3-1-0-4 | - |
| I | CH101 | Chemistry for Engineers | 3-1-0-4 | - |
| I | MA101 | Mathematics I | 4-0-0-4 | - |
| I | CS101 | Introduction to Programming | 2-0-2-3 | - |
| I | EE101 | Basic Electrical Engineering | 3-1-0-4 | - |
| I | ME101 | Engineering Drawing and Graphics | 2-0-2-3 | - |
| I | HS101 | English for Engineers | 2-0-0-2 | - |
| I | PH102 | Physics Lab I | 0-0-3-1 | PH101 |
| I | CH102 | Chemistry Lab I | 0-0-3-1 | CH101 |
| I | CS102 | Programming Lab | 0-0-3-1 | CS101 |
| II | PH201 | Electromagnetic Fields and Waves | 3-1-0-4 | PH101 |
| II | CH201 | Organic Chemistry | 3-1-0-4 | CH101 |
| II | MA201 | Mathematics II | 4-0-0-4 | MA101 |
| II | CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| II | EE201 | Circuit Analysis | 3-1-0-4 | EE101 |
| II | ME201 | Mechanics of Materials | 3-1-0-4 | ME101 |
| II | HS201 | Communication Skills | 2-0-0-2 | - |
| II | PH202 | Electromagnetic Lab | 0-0-3-1 | PH201 |
| II | CH202 | Organic Chemistry Lab | 0-0-3-1 | CH201 |
| II | CS202 | Data Structures Lab | 0-0-3-1 | CS201 |
| III | PH301 | Quantum Physics and Applications | 3-1-0-4 | PH201 |
| III | CH301 | Inorganic Chemistry | 3-1-0-4 | CH201 |
| III | MA301 | Mathematics III | 4-0-0-4 | MA201 |
| III | CS301 | Database Management Systems | 3-1-0-4 | CS201 |
| III | EE301 | Signals and Systems | 3-1-0-4 | EE201 |
| III | ME301 | Thermodynamics | 3-1-0-4 | ME201 |
| III | HS301 | Leadership and Ethics | 2-0-0-2 | - |
| III | PH302 | Quantum Physics Lab | 0-0-3-1 | PH301 |
| III | CH302 | Inorganic Chemistry Lab | 0-0-3-1 | CH301 |
| III | CS302 | DBMS Lab | 0-0-3-1 | CS301 |
| IV | PH401 | Statistical Mechanics | 3-1-0-4 | PH301 |
| IV | CH401 | Physical Chemistry | 3-1-0-4 | CH301 |
| IV | MA401 | Mathematics IV | 4-0-0-4 | MA301 |
| IV | CS401 | Software Engineering | 3-1-0-4 | CS301 |
| IV | EE401 | Control Systems | 3-1-0-4 | EE301 |
| IV | ME401 | Fluid Mechanics | 3-1-0-4 | ME301 |
| IV | HS401 | Entrepreneurship Development | 2-0-0-2 | - |
| IV | PH402 | Statistical Mechanics Lab | 0-0-3-1 | PH401 |
| IV | CH402 | Physical Chemistry Lab | 0-0-3-1 | CH401 |
| IV | CS402 | Software Engineering Lab | 0-0-3-1 | CS401 |
| V | PH501 | Advanced Electromagnetic Theory | 3-1-0-4 | PH401 |
| V | CH501 | Chemistry of Polymers | 3-1-0-4 | CH401 |
| V | MA501 | Advanced Mathematics | 4-0-0-4 | MA401 |
| V | CS501 | Machine Learning | 3-1-0-4 | CS401 |
| V | EE501 | Digital Signal Processing | 3-1-0-4 | EE401 |
| V | ME501 | Heat Transfer | 3-1-0-4 | ME401 |
| V | HS501 | Global Perspectives | 2-0-0-2 | - |
| V | PH502 | Advanced Electromagnetic Lab | 0-0-3-1 | PH501 |
| V | CH502 | Polymers Lab | 0-0-3-1 | CH501 |
| V | CS502 | ML Lab | 0-0-3-1 | CS501 |
| VI | PH601 | Quantum Field Theory | 3-1-0-4 | PH501 |
| VI | CH601 | Nuclear Chemistry | 3-1-0-4 | CH501 |
| VI | MA601 | Numerical Methods | 4-0-0-4 | MA501 |
| VI | CS601 | Big Data Analytics | 3-1-0-4 | CS501 |
| VI | EE601 | Power Electronics | 3-1-0-4 | EE501 |
| VI | ME601 | Manufacturing Processes | 3-1-0-4 | ME501 |
| VI | HS601 | Project Management | 2-0-0-2 | - |
| VI | PH602 | Quantum Field Theory Lab | 0-0-3-1 | PH601 |
| VI | CH602 | Nuclear Chemistry Lab | 0-0-3-1 | CH601 |
| VI | CS602 | Big Data Lab | 0-0-3-1 | CS601 |
| VII | PH701 | Advanced Optics | 3-1-0-4 | PH601 |
| VII | CH701 | Environmental Chemistry | 3-1-0-4 | CH601 |
| VII | MA701 | Operations Research | 4-0-0-4 | MA601 |
| VII | CS701 | Computer Vision | 3-1-0-4 | CS601 |
| VII | EE701 | Renewable Energy Systems | 3-1-0-4 | EE601 |
| VII | ME701 | Advanced Materials | 3-1-0-4 | ME601 |
| VII | HS701 | Leadership in Technology | 2-0-0-2 | - |
| VII | PH702 | Advanced Optics Lab | 0-0-3-1 | PH701 |
| VII | CH702 | Environmental Chemistry Lab | 0-0-3-1 | CH701 |
| VII | CS702 | Computer Vision Lab | 0-0-3-1 | CS701 |
| VIII | PH801 | Condensed Matter Physics | 3-1-0-4 | PH701 |
| VIII | CH801 | Biochemistry | 3-1-0-4 | CH701 |
| VIII | MA801 | Stochastic Processes | 4-0-0-4 | MA701 |
| VIII | CS801 | Deep Learning | 3-1-0-4 | CS701 |
| VIII | EE801 | Smart Grid Technologies | 3-1-0-4 | EE701 |
| VIII | ME801 | Robotics and Automation | 3-1-0-4 | ME701 |
| VIII | HS801 | Technology and Society | 2-0-0-2 | - |
| VIII | PH802 | Condensed Matter Lab | 0-0-3-1 | PH801 |
| VIII | CH802 | Biochemistry Lab | 0-0-3-1 | CH801 |
| VIII | CS802 | Deep Learning Lab | 0-0-3-1 | CS801 |
Each course within the curriculum is designed to progressively build upon previous knowledge while introducing new concepts relevant to the field. The department places a strong emphasis on experiential learning through hands-on laboratory sessions, real-world case studies, and collaborative group projects.
Advanced Departmental Elective Courses
Departmental electives are offered in specialized areas that allow students to deepen their understanding and develop expertise in emerging technologies. Below are descriptions of several advanced elective courses:
1. Machine Learning (CS501)
This course provides an in-depth exploration of machine learning algorithms, including supervised learning, unsupervised learning, reinforcement learning, and deep learning techniques. Students learn to implement these methods using Python libraries like Scikit-learn, TensorFlow, and PyTorch. The course emphasizes practical applications through real-world datasets, preparing students for roles in data science, artificial intelligence, and algorithm development.
2. Big Data Analytics (CS601)
Focused on processing large-scale datasets, this course covers big data technologies such as Hadoop, Spark, and NoSQL databases. Students gain hands-on experience with tools like Hive, Pig, and Kafka, learning how to extract insights from complex data environments. The curriculum includes case studies from industries such as finance, healthcare, and e-commerce.
3. Computer Vision (CS701)
This elective introduces students to the principles and techniques of computer vision, including image processing, feature detection, object recognition, and neural network architectures for visual tasks. Through project-based learning, students develop applications such as facial recognition systems, autonomous vehicle navigation, and medical imaging analysis.
4. Renewable Energy Systems (EE701)
This course explores the design, implementation, and optimization of renewable energy technologies including solar panels, wind turbines, hydroelectric generators, and geothermal systems. Students analyze energy conversion processes, model system performance, and evaluate economic viability using simulation software.
5. Advanced Materials (ME701)
This course delves into the structure, properties, and applications of advanced materials such as composites, nanomaterials, smart materials, and biomaterials. Students conduct experiments to characterize material behavior under various conditions, gaining insights into cutting-edge developments in materials science.
6. Embedded Systems and IoT (CS602)
This course focuses on designing and developing embedded systems for Internet of Things (IoT) applications. Students learn about microcontrollers, sensors, wireless communication protocols, and real-time operating systems. Practical labs involve building IoT devices that collect environmental data, control home appliances, or monitor industrial processes.
7. Smart Grid Technologies (EE801)
This elective covers the integration of renewable energy sources into electrical grids, smart metering technologies, demand response programs, and grid stability management. Students study power system dynamics, cybersecurity in smart grids, and regulatory frameworks governing modern electricity markets.
8. Deep Learning (CS801)
This advanced course explores neural network architectures such as convolutional networks, recurrent networks, transformers, and generative adversarial networks. Students implement complex deep learning models for tasks like natural language processing, image generation, and predictive analytics using frameworks like TensorFlow and PyTorch.
9. Robotics and Automation (ME801)
This course combines mechanical design, electronics, control systems, and artificial intelligence to create robotic systems capable of performing complex tasks. Students build autonomous robots, program sensor integration, and apply machine learning techniques for navigation and manipulation.
10. Data Science with R (CS702)
While similar to Python-based analytics courses, this elective focuses specifically on using R for statistical modeling, data visualization, and exploratory analysis. Students learn advanced packages like dplyr, ggplot2, caret, and shiny, applying these tools to real-world datasets in fields such as business intelligence and public health.
Project-Based Learning Philosophy
At G D Goenka University Gurugram, project-based learning is central to the engineering education philosophy. The department recognizes that theoretical knowledge alone is insufficient for developing competent engineers capable of solving real-world problems. Therefore, students engage in both mini-projects and final-year capstone projects throughout their academic journey.
Mini-projects are introduced in the second year, allowing students to apply basic principles learned in coursework to practical scenarios. These projects often involve small teams working on short-term tasks such as designing a simple electronic circuit or conducting a basic software application. The goal is to foster teamwork, problem-solving skills, and early exposure to professional practices.
The final-year capstone project represents the culmination of the student's engineering education. Students select a topic aligned with their interests and career aspirations, often in collaboration with industry partners or faculty members. These projects typically span several months and require extensive research, design, implementation, and presentation components. The department supports students through dedicated mentorship, access to cutting-edge resources, and regular progress reviews.
Project selection is facilitated through a structured process involving proposal submissions, faculty guidance, and peer evaluation. Students are encouraged to propose innovative ideas that address societal challenges or leverage emerging technologies. The department also facilitates connections with industry experts who serve as external mentors, providing valuable insights into professional expectations and market trends.
The evaluation criteria for project work emphasize technical excellence, creativity, adherence to timelines, and effective communication. Students must document their methodology, present findings clearly, and demonstrate how their solutions meet stakeholder needs. This approach ensures that graduates are not only technically skilled but also capable of leading multidisciplinary teams in collaborative environments.