| Program start date | Application deadline |
| 2023-10-02 | 2023-05-01 |
| 2024-01-08 | 2023-08-01 |
| 2024-04-22 | 2023-11-01 |
| 2023-09-01 | - |
| 2024-09-01 | - |
Program Overview
The Master's in Computer Science program provides a comprehensive foundation in theoretical and practical computer science, equipping graduates with advanced programming skills, problem-solving abilities, and expertise in emerging technologies. Through a combination of lectures, tutorials, practical sessions, and collaborative projects, students gain a deep understanding of algorithms, data structures, software development, and various specialized areas within computer science. The program prepares graduates for successful careers in diverse computer science-related fields, including software engineering, data science, artificial intelligence, and cybersecurity.
Program Outline
Degree Overview
Master's Degree in Computer Science
This program provides students with a solid foundation in theoretical computer science, practical software development, and the ability to solve challenging real-world problems using computational methods and advanced programming languages.
Program Goals
The Master's in Computer Science aims to equip graduates with:
- In-depth understanding of advanced algorithms, data structures, and foundational computer science principles.
- Expertise in object-oriented programming, software design, and development methodologies.
- Proficiency in modern programming languages and emerging technologies.
- Strong problem-solving and analytical skills for designing and implementing complex software solutions.
- Effective communication and collaboration abilities to work within diverse teams.
- Capability to conduct independent research and contribute to the field of computer science.
Program Description
The Master's in Computer Science is a challenging and rewarding program designed to prepare graduates for successful careers in various computer science-related fields. The curriculum balances theoretical and practical aspects, emphasizing hands-on learning and collaborative projects. Throughout the program, students gain a strong understanding of key concepts while engaging in practical application and real-world problem-solving.
Outline
Program Structure
The Master's in Computer Science program consists of 10 modules, 8 core and 2 optional. Within these modules, students explore various areas of computer science, including:
Core Modules:
- Algorithms and Data Structures
- Object-Oriented Programming
- Software Engineering
- Advanced Web Technologies
- Machine Learning and Artificial Intelligence
- Database Systems
- Computer Graphics
- Information Security
Optional Modules:
- Big Data Analytics
- Computer Networks and Distributed Systems
- Computer Vision
- Robotics
- Quantum Computing
- Mobile App Development
Course Schedule
The Master's in Computer Science program operates on a semester system, with each semester comprising 4 modules. The full-time program typically lasts for one year, with each module delivered over 10 weeks. Students complete two modules per semester, attending lectures, tutorials, and practical sessions during this period. Part-time students complete the program over two years, taking one module per semester.
Individual Module Descriptions
Each module delves into specific areas of computer science, providing detailed knowledge and practical skills:
Algorithms and Data Structures:
This module explores advanced algorithms and data structures, along with their implementation and analysis. Students learn about searching, sorting, recursion, graphs, and other algorithms, gaining experience in analyzing algorithm efficiency and complexity.
Object-Oriented Programming:
This module focuses on object-oriented programming concepts and skills. Students learn about classes, objects, inheritance, polymorphism, and encapsulation, mastering object-oriented design principles for developing robust and reusable software solutions.
Software Engineering:
This module introduces software development methodologies and best practices. Students learn about software design patterns, project management, testing and debugging techniques, understanding the different stages of the software development lifecycle and gaining the skills to develop high-quality software.
Advanced Web Technologies:
This module dives into advanced web technologies and development tools. Students learn about client-server architectures, web frameworks, RESTful APIs, and front-end JavaScript libraries, gaining the ability to create dynamic and interactive web applications.
Machine Learning and Artificial Intelligence:
This module introduces the principles and techniques of machine learning and artificial intelligence. Students explore various algorithms, including supervised learning, unsupervised learning, and reinforcement learning, gaining the ability to employ these algorithms for data analysis, prediction, and automated decision-making.
Database Systems:
This module provides an in-depth understanding of database systems, their design and implementation. Students learn about relational database models, SQL queries, database normalization, transaction management, and data warehousing, gaining the ability to design, implement, and manage efficient database systems.
Computer Graphics:
This module explores the principles of computer graphics and visualization techniques. Students learn about 3D modeling, rendering, animation, and virtual reality, gaining the ability to create realistic and interactive 3D environments.
Information Security:
This module examines the fundamentals of information security and cybersecurity principles. Students learn about cryptography, network security, access control, and risk management, gaining the ability to develop and implement secure software solutions and protect information assets.
Big Data Analytics:
This optional module focuses on big data analytics techniques and tools. Students learn about distributed data processing frameworks, data visualization, and machine learning algorithms for big data analysis, gaining the ability to extract insights and knowledge from large-scale datasets.
Computer Networks and Distributed Systems:
This optional module explores the concepts and design principles of computer networks and distributed systems. Students learn about network protocols, network topologies, cloud computing, and distributed algorithms, gaining the ability to design, implement, and manage reliable and scalable network systems.
Computer Vision:
This optional module focuses on computer vision techniques and applications. Students learn about image acquisition, image processing, feature extraction, and object recognition, gaining the ability to develop computer vision systems for various tasks, such as object detection and tracking.
Robotics:
This optional module explores the principles of robotics and robotic systems. Students learn about robot kinematics, dynamics, control systems, and sensor integration, gaining the ability to design, program, and control robots for various tasks.
Quantum Computing:
This optional module introduces the emerging field of quantum computing and its potential applications. Students learn about quantum physics, quantum algorithms, and quantum hardware, gaining an understanding of how quantum computers work and the potential for solving complex problems that are intractable for classical computers.
Mobile App Development:
This optional module focuses on mobile app development for iOS and Android platforms. Students learn about native app development, cross-platform frameworks, and mobile software design patterns, gaining the ability to create high-performance and user-friendly mobile applications.
Assessment
Assessment Methods
Assessment methods in the Master's in Computer Science program vary depending on the specific module and learning outcomes. Typical assessments include:
- Examinations: Written examinations are used to assess students' understanding of theoretical concepts and knowledge acquired in lectures and readings.
- Coursework: Assignments, projects, and practical exercises assess students' ability to apply their knowledge and skills to solve problems, design solutions, and implement software systems.
- Presentations: Students may be required to present their work or research findings to peers and faculty, demonstrating their communication skills and ability to explain complex technical concepts effectively.
- Group work: Collaborative projects allow students to work in teams, enhancing their teamwork, communication, and problem-solving abilities.
Assessment Criteria
The assessment criteria vary based on the assessment method and learning outcomes being evaluated. However, general criteria include:
- Understanding of concepts and theories: Students' comprehension and ability to apply theoretical knowledge and principles from the course content.
- Problem-solving: Students' ability to identify problems, analyze requirements, design solutions, and implement them using appropriate techniques and technologies.
- Code quality and functionality: Students' ability to write well-structured, efficient, and functional code that meets the requirements of the assignment or project.
- Critical analysis and evaluation: Students' ability to critically analyze and evaluate existing research, literature, and technical approaches, demonstrating a higher-order understanding of the subject.
- Communication and presentation: Students' ability to communicate ideas, concepts, and technical information effectively to both technical and non-technical audiences through written and oral communication.
- Teamwork and collaboration: Students' ability to work effectively within a team, contributing to the group project or assignment while demonstrating respect and consideration for other team members. # Teaching
Teaching Methods
The Master's in Computer Science program employs various teaching methods to cater to different learning styles and ensure effective knowledge transfer:
- Lectures: Faculty-led lectures introduce key concepts and theories, providing a foundation for further exploration and application of knowledge.
- Tutorials: Smaller group tutorials allow for interactive discussions, problem-solving activities, and clarification of concepts presented in lectures.
- Practical sessions: Students gain hands-on experience through practical sessions in computer labs, implementing algorithms, designing software, and working with various technologies.
- Independent study: Students are encouraged to independently study and research topics related to their coursework, deepening their understanding and developing critical thinking skills.
- Guest speakers: Industry experts and researchers may be invited to deliver guest lectures, sharing real-world insights and perspectives on current trends and applications within the field of computer science.
Faculty
The Master's in Computer Science program benefits from a dedicated team of experienced faculty members who are experts in their respective areas of computer science. Faculty members have extensive research and industry experience, bringing real-world relevance and valuable knowledge to their teaching and interactions with students. They are committed to providing personalized guidance, mentorship, and support to help students succeed in their academic journey.
Unique Approaches
The Master's in Computer Science program incorporates several unique approaches to enhance learning and engagement:
- Problem-based learning: Students engage in real-world problem-solving activities and projects, applying their knowledge and skills to address practical challenges and develop innovative solutions.
- Collaborative learning: Students work together in groups on projects and assignments, fostering teamwork, communication, and problem-solving abilities.
