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Fees
₹1,20,000
Placement
92.0%
Avg Package
₹7,00,000
Highest Package
₹15,00,000
Fees
₹1,20,000
Placement
92.0%
Avg Package
₹7,00,000
Highest Package
₹15,00,000
Seats
150
Students
300
Seats
150
Students
300
The curriculum for the Diploma In Information Technology program at Shirdi Sai Diploma In Engineering Technology Vizianagaram is meticulously designed to provide students with a comprehensive understanding of information technology concepts and practical skills. The program spans three years, divided into six semesters, with a carefully structured sequence of core subjects, departmental electives, science electives, and laboratory courses.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | IT101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | IT102 | Physics for IT | 3-1-0-4 | None |
| 1 | IT103 | Chemistry for IT | 3-1-0-4 | None |
| 1 | IT104 | Basic Programming Concepts | 3-1-0-4 | None |
| 1 | IT105 | Computer Fundamentals | 3-1-0-4 | None |
| 1 | IT106 | English for IT | 3-1-0-4 | None |
| 1 | IT107 | Workshop I | 0-0-3-1 | None |
| 2 | IT201 | Engineering Mathematics II | 3-1-0-4 | IT101 |
| 2 | IT202 | Electrical and Electronics | 3-1-0-4 | None |
| 2 | IT203 | Data Structures and Algorithms | 3-1-0-4 | IT104 |
| 2 | IT204 | Database Management Systems | 3-1-0-4 | IT104 |
| 2 | IT205 | Operating Systems | 3-1-0-4 | IT104 |
| 2 | IT206 | Computer Architecture | 3-1-0-4 | IT105 |
| 2 | IT207 | Workshop II | 0-0-3-1 | IT107 |
| 3 | IT301 | Discrete Mathematics | 3-1-0-4 | IT201 |
| 3 | IT302 | Software Engineering | 3-1-0-4 | IT203 |
| 3 | IT303 | Web Technologies | 3-1-0-4 | IT203 |
| 3 | IT304 | Object-Oriented Programming | 3-1-0-4 | IT104 |
| 3 | IT305 | Network Fundamentals | 3-1-0-4 | IT202 |
| 3 | IT306 | Mobile Application Development | 3-1-0-4 | IT303 |
| 3 | IT307 | Workshop III | 0-0-3-1 | IT207 |
| 4 | IT401 | Artificial Intelligence | 3-1-0-4 | IT301 |
| 4 | IT402 | Machine Learning | 3-1-0-4 | IT301 |
| 4 | IT403 | Cybersecurity | 3-1-0-4 | IT205 |
| 4 | IT404 | Data Science | 3-1-0-4 | IT204 |
| 4 | IT405 | Embedded Systems | 3-1-0-4 | IT206 |
| 4 | IT406 | Internet of Things | 3-1-0-4 | IT305 |
| 4 | IT407 | Workshop IV | 0-0-3-1 | IT307 |
| 5 | IT501 | Advanced Database Systems | 3-1-0-4 | IT204 |
| 5 | IT502 | Cloud Computing | 3-1-0-4 | IT205 |
| 5 | IT503 | Big Data Analytics | 3-1-0-4 | IT404 |
| 5 | IT504 | Computer Vision | 3-1-0-4 | IT402 |
| 5 | IT505 | Blockchain Technology | 3-1-0-4 | IT303 |
| 5 | IT506 | Human-Computer Interaction | 3-1-0-4 | IT303 |
| 5 | IT507 | Workshop V | 0-0-3-1 | IT407 |
| 6 | IT601 | Capstone Project | 3-1-0-4 | IT501 |
| 6 | IT602 | Internship | 0-0-3-1 | IT507 |
| 6 | IT603 | Final Year Thesis | 3-1-0-4 | IT601 |
| 6 | IT604 | Professional Ethics | 3-1-0-4 | None |
| 6 | IT605 | Project Management | 3-1-0-4 | IT302 |
| 6 | IT606 | Entrepreneurship | 3-1-0-4 | None |
| 6 | IT607 | Workshop VI | 0-0-3-1 | IT603 |
Departmental electives play a crucial role in allowing students to specialize in areas of interest and gain deeper insights into advanced topics. The following are some of the advanced departmental elective courses offered in the program:
Artificial Intelligence and Machine Learning: This course delves into the principles and techniques of AI and ML, including neural networks, deep learning, natural language processing, and computer vision. Students learn to build intelligent systems that can learn from data and make decisions autonomously. The course emphasizes hands-on implementation using frameworks like TensorFlow and PyTorch.
Cybersecurity: This course covers the fundamentals of network security, cryptography, ethical hacking, and incident response. Students gain practical experience through labs and simulations, preparing them for roles in security analysis, penetration testing, and risk management. The course also explores emerging threats and defensive strategies in the cybersecurity landscape.
Data Science: This course focuses on extracting insights from large datasets using statistical methods, data mining, and machine learning techniques. Students learn to use tools such as R, Python, and SQL to analyze data and build predictive models. The course emphasizes real-world applications in business intelligence, healthcare, and finance.
Cloud Computing: This course introduces students to cloud computing concepts, architectures, and services. Students learn to deploy and manage applications on cloud platforms such as AWS, Azure, and Google Cloud Platform. The course also covers security, scalability, and cost optimization in cloud environments.
Internet of Things (IoT): This course explores the architecture, protocols, and applications of IoT systems. Students learn to design and develop IoT solutions using sensors, microcontrollers, and communication protocols. The course emphasizes real-world applications in smart cities, agriculture, and healthcare.
Embedded Systems: This course focuses on the design and implementation of embedded systems using microcontrollers and real-time operating systems. Students gain hands-on experience with hardware-software integration, device drivers, and system optimization techniques.
Computer Vision: This course covers the principles and applications of computer vision, including image processing, object detection, and recognition. Students learn to build systems that can interpret and understand visual information from the world.
Blockchain Technology: This course explores the architecture and applications of blockchain technology beyond cryptocurrency. Students learn about smart contracts, decentralized applications, and blockchain security. The course emphasizes practical implementation and real-world use cases.
Human-Computer Interaction (HCI): This course focuses on designing user-friendly interfaces and experiences. Students learn to apply human-centered design principles to create intuitive and accessible digital products. The course covers user research, prototyping, and usability testing.
Big Data Analytics: This course introduces students to the tools and techniques used in big data analytics. Students learn to process and analyze large datasets using technologies such as Hadoop, Spark, and NoSQL databases. The course emphasizes real-world applications in business intelligence and data science.
The department's philosophy on project-based learning is centered around fostering innovation, creativity, and practical problem-solving skills. Students are encouraged to work on real-world projects that align with industry needs and current technological trends.
The structure of project-based learning involves three phases: project selection, implementation, and evaluation. In the first phase, students are guided to select projects that are both challenging and relevant. The implementation phase involves hands-on development, testing, and documentation. The evaluation phase includes peer reviews, faculty feedback, and presentation of results.
The scope of projects ranges from small-scale applications to large-scale systems. Students can work individually or in teams, with faculty mentors providing guidance and support throughout the process. The evaluation criteria include technical excellence, innovation, usability, and impact.
Mini-projects are introduced in the early semesters to build foundational skills and confidence. These projects are typically completed within a few weeks and are designed to reinforce concepts learned in class. Final-year capstone projects are more comprehensive and require students to integrate knowledge from multiple disciplines. These projects often lead to publications, patents, or startups.
Faculty mentors are selected based on their expertise and availability. Students are encouraged to choose mentors whose research interests align with their project goals. The mentorship process includes regular meetings, progress updates, and feedback sessions.
