A cutting-edge centre that combines innovation, technology, and data analysis is the Department of Artificial Intelligence and Data Science. The department seeks to provide researchers and students with superior knowledge of intelligent systems design, predictive modelling, and data processing. With an emphasis on interdisciplinary cooperation and practical problem solving, the programme offers educational modules that blend fundamental knowledge with contemporary technology. With a wide range of applications in fields including education, healthcare, and finance, research topics include machine learning, natural language processing, big data analytics, and AI ethics. In addition to training graduates to lead in a fast-evolving digital world, the department aims to create an inventive environment that advances the boundaries of data science and artificial intelligence.

A vibrant hub of innovation, analytics, and technology is the Department of Artificial Intelligence and Data Science. Its main goal is to give researchers and students state-of-the-art abilities in data processing, predictive modelling, and intelligent system design. Emphasising multidisciplinary cooperation and practical problem-solving, the department's curriculum bridges the gap between fundamental knowledge and advanced methodologies. Research topics address issues in healthcare, banking, education, and other fields, ranging from machine learning and natural language processing to big data analytics and ethical AI. The department hopes to push the limits of AI and data science by cultivating an innovative ecosystem, equipping graduates to take the lead in a quickly changing digital world.

The main objectives of the department are:

1. Provide comprehensive and up-to-date education in AI and data science, covering foundational concepts, advanced techniques, and practical applications.
2. Emphasise the importance of ethical considerations in AI and data science, instilling a sense of responsibility and ethical decision-making among learners.
3. Offer practical, hands-on experiences to reinforce theoretical knowledge, ensuring that learners can apply concepts in real-world scenarios.
4. Create an inclusive learning environment that embraces diversity and fosters collaboration, recognising the value of diverse perspectives in the field.
5. Facilitate market collaborations to bridge the gap between academia and real-world applications, ensuring that education remains relevant to industry needs.
6. Encourage and support research initiatives that contribute to the advancement of AI and data science, promoting a culture of innovation and exploration.