CSCI Stream

In the Intelligence Science stream, students mainly learn how to design and build intelligent systems that can perceive data, learn from experience, and make decisions. The curriculum typically covers foundations and applications of artificial intelligence, machine learning, deep learning, data mining, pattern recognition, natural language processing, computer vision, and statistical reasoning, together with hands-on projects that apply these techniques to real-world problems such as text analysis, image understanding, recommendation, and intelligent decision support.

 

Graduates from this stream are well prepared for careers such as AI engineer, machine learning engineer, data scientist, software engineer with an AI focus, quantitative analyst, or research assistant, and they are also competitive candidates for postgraduate studies in AI, data science, and related interdisciplinary fields.

 

For the stream requirements, please refer to the study scheme of your entry year.

In the Database & Information Systems stream, students focus on how large-scale data is modeled, stored, managed, and transformed into useful information for organizations and applications. They learn core topics such as database system design, data modeling, SQL and query optimization, transaction management, distributed and cloud databases, data warehousing, information retrieval, and data-intensive system architectures, often combined with software engineering and web-based system development. Through practical projects, students gain experience building reliable, scalable, and efficient information systems that support business operations and decision-making.

 

Graduates from this stream typically pursue roles such as database engineer, data engineer, backend or full‑stack software engineer, system analyst, IT consultant, business intelligence developer, or data analyst, and they are well suited for careers in industries like finance, e-commerce, logistics, government, and enterprise IT, as well as further studies in data-centric computing fields.

 

For the stream requirements, please refer to the study scheme of your entry year.

In the Rich Media stream, students learn how to represent, process, analyze, and create multimedia content using computational techniques. The curriculum typically covers topics such as computer graphics, image and video processing, multimedia systems, animation, human–computer interaction (HCI), computer vision, audio processing, virtual and augmented reality, and interactive media technologies, with an emphasis on both technical foundations and creative applications. Through projects and hands-on assignments, students gain experience in building multimedia applications, interactive systems, and visually rich digital content.

 

Graduates from this stream often pursue careers as software engineers in multimedia or graphics, computer vision engineers, UI/UX engineers, game developers, AR/VR developers, multimedia system engineers, or digital media technologists, and they are well positioned for work in industries such as gaming, film and entertainment, advertising, media technology, education technology, and creative tech startups, as well as for further studies in graphics, vision, and interactive media research.

 

For the stream requirements, please refer to the study scheme of your entry year.

In the Distributed Systems, Networks & Security streamm students learn how modern computing systems are connected, scaled, and protected in real-world environments. The curriculum emphasizes computer networks, distributed and cloud systems, operating systems concepts, network protocols, system performance and reliability, cybersecurity fundamentals, cryptography, secure system design, and network defense mechanisms, with practical exposure through labs and system-building projects. Students develop a strong understanding of how large-scale services (such as cloud platforms, web services, and enterprise systems) are designed to be efficient, fault-tolerant, and secure against attacks.

 

Graduates from this stream are well suited for roles such as software engineer (systems or backend), distributed systems engineer, network engineer, cloud engineer, site reliability engineer (SRE), cybersecurity analyst, security engineer, or infrastructure engineer, and they are in demand across industries including technology companies, cloud service providers, finance, telecommunications, cybersecurity firms, and government, as well as for advanced studies in systems and security research.

 

For the stream requirements, please refer to the study scheme of your entry year.

In the Theoretical Computer Science stream, students develop a deep understanding of the mathematical and conceptual foundations of computing. The stream emphasizes topics such as algorithm design and analysis, data structures, computational complexity, computability theory, formal languages and automata, logic, cryptography foundations, and randomized or approximation algorithms, training students to think rigorously about correctness, efficiency, and limits of computation. Through proofs, problem solving, and abstract modeling, students learn how to design optimal algorithms and reason about computational problems at a fundamental level.

 

Graduates from this stream are well suited for careers as software engineers with strong algorithmic focus, algorithm engineers, quantitative developers, cryptography engineers, and technical problem solvers in finance and technology firms, and they are particularly competitive for research-oriented roles and postgraduate studies (MPhil/PhD) in computer science, mathematics, data science, and related theoretical or interdisciplinary fields.

 

For the stream requirements, please refer to the study scheme of your entry year.

In the Data Analytics stream, students learn how to extract insights and value from large and complex datasets using computational and statistical techniques. The curriculum typically covers data analytics foundations, statistical analysis, data mining, machine learning, data visualization, big data processing, and practical tools for handling real-world data, often emphasizing problem formulation, interpretation of results, and data-driven decision making. Through projects and case-based learning, students gain experience working with data from domains such as business, finance, social networks, and science.

 

Graduates from this stream commonly pursue careers as data analyst, data scientist, business intelligence analyst, data engineer, machine learning practitioner, or software engineer with a data focus, and they are well prepared for roles in finance, technology, consulting, healthcare, e-commerce, and government, as well as for further studies in data science, analytics, or related interdisciplinary fields.

 

For the stream requirements, please refer to the study scheme of your entry year.