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Scholarships & exams

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Pune, Maharashtra, India

Duration

4 Years

Bachelor of Technology in Engineering

NIIT University Alwar
Duration
4 Years
Engineering UG OFFLINE

Duration

4 Years

Bachelor of Technology in Engineering

NIIT University Alwar
Duration
Apply

Fees

₹2,50,000

Placement

92.0%

Avg Package

₹6,00,000

Highest Package

₹12,00,000

OverviewAdmissionsCurriculumFeesPlacements
4 Years
Engineering
UG
OFFLINE

Fees

₹2,50,000

Placement

92.0%

Avg Package

₹6,00,000

Highest Package

₹12,00,000

Seats

1,200

Students

1,200

ApplyCollege

Seats

1,200

Students

1,200

Curriculum

Course Structure Overview

The engineering program at Niit University Alwar is structured over eight semesters, with each semester containing a balanced mix of core engineering courses, departmental electives, science electives, and laboratory sessions. The curriculum is designed to build foundational knowledge in the first two years, followed by specialization and advanced application in later semesters.

First Year Courses

  • Mathematics I & II
  • Physics I & II
  • Chemistry I & II
  • Engineering Drawing & Computer Graphics
  • Communication Skills
  • Introduction to Engineering
  • Computer Programming Lab
  • Basic Electrical & Electronics

Second Year Courses

  • Data Structures and Algorithms
  • Database Management Systems
  • Digital Logic Design
  • Signals and Systems
  • Engineering Mechanics
  • Materials Science
  • Computer Organization & Architecture
  • Mathematical Methods

Third Year Courses

  • Operating Systems
  • Compiler Design
  • Computer Networks
  • Machine Learning
  • Control Systems
  • Power Electronics
  • Heat Transfer
  • Fluid Mechanics

Fourth Year Courses

  • Capstone Project I
  • Capstone Project II
  • Advanced Topics in AI/ML
  • Network Security
  • Sustainable Energy Systems
  • Project Management
  • Entrepreneurship
  • Industrial Internship

Detailed Course List by Semester

SemesterCourse CodeCourse TitleCredit Structure (L-T-P-C)Prerequisites
1MTH101Calculus I3-0-0-3-
1MTH102Calculus II3-0-0-3MTH101
1PHY101Physics I3-0-0-3-
1PHY102Physics II3-0-0-3PHY101
1CHM101Chemistry I3-0-0-3-
1CHM102Chemistry II3-0-0-3CHM101
1ENG101Engineering Drawing & Computer Graphics2-0-2-4-
1COM101Communication Skills2-0-0-2-
1INT101Introduction to Engineering2-0-0-2-
1CPP101Computer Programming Lab0-0-3-3-
1BAS101Basic Electrical & Electronics3-0-0-3-
2MTH201Mathematical Methods3-0-0-3MTH102
2PHY201Electromagnetic Fields3-0-0-3PHY102
2CHM201Organic Chemistry3-0-0-3CHM102
2ELE201Basic Electronics3-0-0-3BAS101
2MEC201Engineering Mechanics3-0-0-3-
2MAT201Materials Science3-0-0-3-
2CSE201Computer Organization & Architecture3-0-0-3CPP101
2MTH202Differential Equations3-0-0-3MTH102
3CSE301Data Structures and Algorithms3-0-0-3CPP101
3CSE302Database Management Systems3-0-0-3CSE301
3ECE301Digital Logic Design3-0-0-3BAS101
3MEC301Heat Transfer3-0-0-3MTH201
3ELE301Signals and Systems3-0-0-3MTH201
3CHM301Physical Chemistry3-0-0-3CHM201
3MAT301Metallurgy & Materials3-0-0-3MAT201
4CSE401Operating Systems3-0-0-3CSE301
4CSE402Compiler Design3-0-0-3CSE301
4ECE401Computer Networks3-0-0-3ECE301
4MEC401Control Systems3-0-0-3ELE301
4ELE401Power Electronics3-0-0-3ELE301
4CHM401Chemical Reaction Engineering3-0-0-3CHM301
4MAT401Advanced Materials3-0-0-3MAT301
5CSE501Machine Learning3-0-0-3CSE401
5ECE501Network Security3-0-0-3ECE401
5MEC501Sustainable Energy Systems3-0-0-3MEC401
5ELE501Electrical Machines3-0-0-3ELE401
5CHM501Environmental Chemistry3-0-0-3CHM401
5MAT501Nanomaterials3-0-0-3MAT401
6CSE601Advanced Topics in AI/ML3-0-0-3CSE501
6ECE601Embedded Systems3-0-0-3ECE401
6MEC601Fluid Mechanics3-0-0-3MEC401
6ELE601Power System Analysis3-0-0-3ELE501
6CHM601Biotechnology3-0-0-3CHM501
6MAT601Computational Materials3-0-0-3MAT501
7CSE701Capstone Project I0-0-6-6-
7ECE701Research Methodology2-0-0-2-
7MEC701Project Management2-0-0-2-
7ELE701Industrial Internship0-0-4-4-
7CHM701Special Topics in Chemistry2-0-0-2-
7MAT701Advanced Computational Techniques2-0-0-2-
8CSE801Capstone Project II0-0-6-6CSE701
8ECE801Entrepreneurship2-0-0-2-
8MEC801Advanced Project Work0-0-6-6-
8ELE801Final Internship Report0-0-4-4ELE701
8CHM801Industry Collaboration Project2-0-0-2-
8MAT801Research Thesis0-0-6-6-

Advanced Departmental Electives

Departmental electives provide students with opportunities to explore specialized topics within their field of study. These courses are designed to deepen understanding and enhance practical skills.

Machine Learning (CSE501)

This course introduces students to the fundamental concepts of machine learning, including supervised and unsupervised learning, neural networks, and deep learning architectures. Students learn to implement algorithms using Python and libraries like scikit-learn and TensorFlow. The course emphasizes real-world applications in areas such as computer vision, natural language processing, and recommendation systems.

Network Security (ECE501)

This elective covers essential topics in network security, including cryptography, firewall configurations, intrusion detection systems, and secure communication protocols. Students gain hands-on experience with tools like Wireshark, Snort, and Kali Linux. The course also explores legal and ethical aspects of cybersecurity.

Sustainable Energy Systems (MEC501)

This course examines renewable energy technologies such as solar photovoltaics, wind turbines, hydroelectric systems, and geothermal power. Students learn about energy storage solutions, smart grid integration, and environmental impact assessments. Practical sessions include designing small-scale renewable energy systems.

Electrical Machines (ELE501)

This course provides a comprehensive understanding of electrical machines such as transformers, induction motors, synchronous generators, and DC machines. Students study the principles of operation, performance characteristics, and applications in power generation and distribution.

Environmental Chemistry (CHM501)

This elective explores the chemical processes that occur in the environment, including pollution chemistry, water quality analysis, and atmospheric chemistry. Students learn to analyze environmental samples and assess the impact of human activities on ecosystems.

Nanomaterials (MAT501)

This course introduces students to nanotechnology and the properties of materials at the nanoscale. Topics include synthesis methods, characterization techniques, and applications in electronics, medicine, and energy storage. Students work with advanced laboratory equipment such as scanning electron microscopes and atomic force microscopes.

Advanced Topics in AI/ML (CSE601)

This course delves into advanced topics in artificial intelligence and machine learning, including reinforcement learning, generative adversarial networks, transfer learning, and explainable AI. Students develop complex models using frameworks like PyTorch and Hugging Face Transformers.

Embedded Systems (ECE601)

This elective focuses on designing embedded systems for real-time applications. Students learn to program microcontrollers, interface sensors, design hardware-software co-design solutions, and optimize system performance. Projects include building IoT devices and robotics controllers.

Fluid Mechanics (MEC601)

This course covers fluid behavior under various conditions, including laminar and turbulent flow, boundary layer theory, and computational fluid dynamics. Students apply theoretical concepts to real-world problems in aerodynamics, hydraulics, and chemical engineering processes.

Power System Analysis (ELE601)

This course explores the analysis and design of power systems, including load flow studies, fault analysis, stability, and protection schemes. Students use software tools like MATLAB and PSCAD to simulate and analyze complex power system scenarios.

Biotechnology (CHM601)

This elective introduces students to biotechnological processes and applications in medicine, agriculture, and industry. Topics include genetic engineering, fermentation technology, bioinformatics, and regulatory frameworks for biotech products.

Computational Materials (MAT601)

This course combines materials science with computational methods to predict material properties and behavior. Students learn to use software packages like VASP and Quantum Espresso for electronic structure calculations and molecular dynamics simulations.

Project-Based Learning Philosophy

The engineering program at Niit University Alwar places a strong emphasis on project-based learning as a means of integrating theoretical knowledge with practical application. This approach ensures that students are not only proficient in academic concepts but also capable of solving real-world problems.

Mini-Projects Structure

Mini-projects are introduced in the second year and continue throughout the program. These projects typically span one semester and involve small teams of 3-5 students working under faculty supervision. Projects are selected based on current industry trends, research areas, or societal needs.

Mini-projects are evaluated using a rubric that considers technical feasibility, innovation, teamwork, presentation quality, and documentation. Students must submit progress reports, mid-term presentations, and final deliverables. The project work contributes significantly to the overall grade in each semester.

Final-Year Thesis/Capstone Project

The capstone project is a comprehensive endeavor that spans two semesters (7th and 8th). Students select a topic aligned with their specialization or interest, develop a research proposal, conduct experiments or simulations, and present findings to a panel of experts.

Faculty mentors are assigned based on the student's chosen area of focus. The project is expected to contribute original knowledge or provide innovative solutions to existing challenges. Many capstone projects result in patents, publications, or startup ventures.

Project Selection Process

Students can choose their project topics from a list provided by faculty members or propose their own ideas after consultation with advisors. The selection process ensures that projects are feasible within the given timeframe and resources.

Evaluation Criteria

Projects are evaluated based on multiple criteria, including innovation, technical depth, clarity of presentation, adherence to deadlines, and impact on the field. Regular feedback from mentors helps students refine their work and improve outcomes.