Curriculum Overview
The curriculum for the Skill Development program at Itm Skills University Navi Mumbai is designed to provide a comprehensive understanding of both theoretical concepts and practical applications. The following table outlines all courses across 8 semesters, including course codes, titles, credit structure (L-T-P-C), and prerequisites.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Engineering Mathematics I | 3-0-0-3 | - |
| 1 | CS102 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CS103 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS104 | Basic Electrical and Electronics | 3-0-0-3 | - |
| 1 | CS105 | Engineering Graphics | 2-0-0-2 | - |
| 1 | CS106 | Communication Skills | 2-0-0-2 | - |
| 2 | CS201 | Engineering Mathematics II | 3-0-0-3 | CS101 |
| 2 | CS202 | Data Structures and Algorithms | 3-0-0-3 | CS103 |
| 2 | CS203 | Digital Logic Design | 3-0-0-3 | CS104 |
| 2 | CS204 | Object-Oriented Programming | 3-0-0-3 | CS103 |
| 2 | CS205 | Computer Organization and Architecture | 3-0-0-3 | CS104 |
| 2 | CS206 | Professional Ethics and Social Responsibility | 2-0-0-2 | - |
| 3 | CS301 | Database Management Systems | 3-0-0-3 | CS202 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS205 |
| 3 | CS303 | Software Engineering | 3-0-0-3 | CS204 |
| 3 | CS304 | Computer Networks | 3-0-0-3 | CS205 |
| 3 | CS305 | Probability and Statistics for Engineers | 3-0-0-3 | CS101 |
| 3 | CS306 | Design Thinking and Innovation | 2-0-0-2 | - |
| 4 | CS401 | Machine Learning | 3-0-0-3 | CS301 |
| 4 | CS402 | Cybersecurity Fundamentals | 3-0-0-3 | CS304 |
| 4 | CS403 | Internet of Things (IoT) | 3-0-0-3 | CS204 |
| 4 | CS404 | Human-Computer Interaction | 3-0-0-3 | CS303 |
| 4 | CS405 | Embedded Systems | 3-0-0-3 | CS205 |
| 4 | CS406 | Project Management | 2-0-0-2 | - |
| 5 | CS501 | Deep Learning and Neural Networks | 3-0-0-3 | CS401 |
| 5 | CS502 | Advanced Cybersecurity | 3-0-0-3 | CS402 |
| 5 | CS503 | Big Data Analytics | 3-0-0-3 | CS301 |
| 5 | CS504 | Robotics and Automation | 3-0-0-3 | CS405 |
| 5 | CS505 | Renewable Energy Systems | 3-0-0-3 | CS205 |
| 5 | CS506 | Innovation and Entrepreneurship | 2-0-0-2 | - |
| 6 | CS601 | Quantitative Finance | 3-0-0-3 | CS305 |
| 6 | CS602 | Digital Forensics | 3-0-0-3 | CS402 |
| 6 | CS603 | Advanced Embedded Systems | 3-0-0-3 | CS405 |
| 6 | CS604 | Human-Centered Design | 3-0-0-3 | CS404 |
| 6 | CS605 | Sustainable Technology | 3-0-0-3 | CS505 |
| 6 | CS606 | Leadership and Team Dynamics | 2-0-0-2 | - |
| 7 | CS701 | Research Methodology | 3-0-0-3 | CS601 |
| 7 | CS702 | Capstone Project I | 3-0-0-3 | CS605 |
| 7 | CS703 | Advanced Software Engineering | 3-0-0-3 | CS303 |
| 7 | CS704 | Advanced Network Security | 3-0-0-3 | CS402 |
| 7 | CS705 | AI for Decision Making | 3-0-0-3 | CS501 |
| 7 | CS706 | Project Planning and Execution | 2-0-0-2 | - |
| 8 | CS801 | Capstone Project II | 3-0-0-3 | CS702 |
| 8 | CS802 | Industry Internship | 3-0-0-3 | - |
| 8 | CS803 | Final Thesis | 3-0-0-3 | CS701 |
| 8 | CS804 | Professional Development Workshop | 2-0-0-2 | - |
| 8 | CS805 | Graduation Portfolio | 3-0-0-3 | - |
| 8 | CS806 | Career Counseling and Job Placement | 2-0-0-2 | - |
Advanced departmental elective courses form a crucial part of the curriculum. These courses are designed to deepen students' understanding of specific domains within skill development.
Deep Learning and Neural Networks: This course explores the principles and applications of neural networks, including convolutional networks, recurrent networks, and transformers. Students learn to design and train complex models for image recognition, natural language processing, and time-series forecasting using frameworks like TensorFlow and PyTorch.
Cybersecurity Fundamentals: This course provides a comprehensive overview of cybersecurity principles, including network security, cryptography, and risk management. Students gain hands-on experience with tools such as Wireshark, Nmap, and Metasploit, preparing them for roles in security analysis, penetration testing, and secure system design.
Internet of Things (IoT): This course introduces students to the architecture and implementation of IoT systems. Topics include sensor networks, edge computing, cloud integration, and smart device development. Practical labs involve building real-time IoT applications using platforms like Arduino, Raspberry Pi, and Node-RED.
Human-Computer Interaction: This course focuses on designing user-friendly interfaces for digital products. Students learn about usability principles, prototyping techniques, and evaluation methods. The course emphasizes the importance of inclusive design and accessibility in creating products that meet diverse user needs.
Embedded Systems: This course covers the design and development of embedded systems used in automotive, medical, and industrial applications. Students work with microcontrollers and real-time operating systems to build efficient and reliable embedded solutions.
Big Data Analytics: This course introduces students to tools and techniques for processing large datasets. Topics include data mining, machine learning algorithms, and distributed computing frameworks like Hadoop and Spark. Students learn how to extract actionable insights from complex datasets.
Robotics and Automation: This course provides an in-depth exploration of robotics principles and automation technologies. Students study robot kinematics, control systems, and sensor integration. Practical labs involve building and programming robots for various applications.
Renewable Energy Systems: This course examines the design and implementation of renewable energy technologies. Students learn about solar, wind, hydroelectric, and geothermal systems. The curriculum includes policy frameworks, economic considerations, and environmental impacts.
Digital Forensics: This course teaches students how to investigate digital crimes and recover evidence from electronic devices. Topics include data recovery, network forensics, and legal aspects of digital investigations. Students practice using forensic tools and techniques in simulated scenarios.
Advanced Software Engineering: This course focuses on advanced software development practices, including agile methodologies, DevOps, and cloud deployment. Students gain experience with version control systems, continuous integration pipelines, and scalable architecture design.
The department’s philosophy on project-based learning is centered around experiential education and real-world problem-solving. Mini-projects are introduced in the second year, allowing students to apply foundational knowledge to practical challenges. These projects are typically completed in teams and involve iterative development cycles.
The final-year thesis/capstone project is a comprehensive endeavor that integrates all aspects of the student's learning journey. Students select a topic aligned with their interests or industry needs, working closely with faculty mentors to define research questions, design experiments, and present findings. The project culminates in a public presentation and a detailed written report.