Master of Science Information Technology Syllabus 2025: Subjects, Semester-wise Syllabus PDF, Books
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by Kritika
January 02, 2025 04:47 PM
Master of Science Information Technology: Syllabus
The Master of Science Information Technology program includes all the necessary courses that help equip students with the skills required to forecast the tendencies of the market and make correct decisions.
Semester-Wise Syllabus:
Semester 1:
Course Name | Credits | Description |
Advanced Database Management Systems | 3 | Covers advanced topics in database design and management, including normalisation, indexing, and query optimization. |
Data Structures and Algorithms | 3 | Explores fundamental data structures and algorithm design techniques, including arrays, linked lists, trees, sorting, and searching. |
Information Technology Management | 3 | Introduces principles and practices of IT management, including strategic planning, project management, and organisational behaviour. |
Software Engineering | 3 | Covers software development methodologies, requirements engineering, design patterns, and software testing. |
Elective Course 1 | 3 | Students choose one elective course based on their interests and career goals. |
Semester 2:
Course Name | Credits | Description |
Network Security | 3 | Explores techniques and technologies for securing computer networks, including cryptography, firewalls, and intrusion detection systems. |
Cloud Computing | 3 | Introduces concepts and technologies for building and deploying applications in cloud environments, including virtualization, scalability, and service-oriented architectures. |
Machine Learning | 3 | Covers algorithms and techniques for building intelligent systems that can learn from data and make predictions or decisions. |
Web Development | 3 | Provides hands-on experience in designing and developing web applications using HTML, CSS, JavaScript, and server-side scripting languages. |
Elective Course 2 | 3 | Students choose one elective course from a list of specialised topics such as cybersecurity, mobile application development, or data analytics. |
Semester 3:
Course Name | Credits | Description |
Big Data Analytics | 3 | Covers techniques for processing, analysing, and visualising large volumes of data using tools and technologies such as Hadoop, Spark, and Tableau. |
Artificial Intelligence | 3 | Explores advanced topics in artificial intelligence, including knowledge representation, reasoning, planning, and natural language processing. |
Information Systems Security | 3 | Focuses on strategies and technologies for protecting information systems from various security threats and vulnerabilities. |
Research Methodology | 3 | Introduces research methods and techniques for conducting academic research and writing scholarly papers. |
Elective Course 3 | 3 | Students choose one elective course to deepen their knowledge in a specific area of information technology. |
Semester 4:
Course Name | Credits | Description |
Master's Thesis | 12 | Students work on a research project under the supervision of a faculty advisor, culminating in the submission and defence of a master's thesis. |
Recommended Books
Below, we have compiled a list of recommended books that can provide valuable insights:
Book Title | Author(s) | Description |
"Database Management Systems" | Raghu Ramakrishnan, Johannes Gehrke | Comprehensive coverage of database management systems, including database design, implementation, and optimization. |
"Introduction to Algorithms" | Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein | Classic textbook covering fundamental algorithms, data structures, and algorithmic techniques. |
"Software Engineering: A Practitioner's Approach" | Roger S. Pressman | Provides a practical approach to software engineering principles, methodologies, and best practices. |
"Computer Networking: A Top-Down Approach" | James F. Kurose, Keith W. Ross | Top-down approach to computer networking, covering protocols, network architecture, and applications. |
"Data Mining: Concepts and Techniques" | Jiawei Han, Micheline Kamber | Covers fundamental concepts and techniques of data mining, including classification, clustering, and association analysis. |
"Cloud Computing: Concepts, Technology & Architecture" | Thomas Erl, Ricardo Puttini, Zaigham Mahmood | Comprehensive guide to cloud computing concepts, technologies, and architectural patterns. |
"Machine Learning: A Probabilistic Perspective" | Kevin P. Murphy | Offers a probabilistic view of machine learning algorithms, covering both basic concepts and advanced techniques. |
"Web Development with Node and Express" | Ethan Brown | Hands-on guide to building web applications using Node.js and the Express framework. |
"Information Security: Principles and Practices" | Mark S. Merkow, James Breithaupt | Introduction to information security principles, practices, technologies, and management strategies. |
"Mobile Application Development: Concepts and Best Practices" | Chris Haseman, Kevin Grant | Covers mobile application development concepts, design principles, and best practices for iOS and Android platforms. |
