Master of Science (MSc) in Computer Science (Information Security and Cryptography)

3.1 Target Group

Students who have a background in STEM who want to specialise in Computer Science whilst obtaining a holistic background on the applications of Computer Science and its fields. Students need to have completed a BSc in a STEM subject and a C1 level of English.

3.2 Entry Requirements

• Bachelor's degree in Computer Science, Information Technology, or a STEM-related field.
• Applicants without the required academic background may be considered based on 2–5 years of relevant industry experience.
• English proficiency: IELTS 7.0 or above (or equivalent), unless previous studies were completed in English.
• Submission of academic qualifications and transcripts is required.

MSc in Computer Science (Information Security and Cryptography)

Policy Maker, Software Engineer, Risk Analyst, Threat Management, Secure Systems Designer/Programmer, Information Systems Administrator, Specialist System Administrator, Forensic Analyst, Lead Forensic Analyst, Cyber Penetration and Verification Analyst, Cyber Security Analyst, Cyber Security Manager, Cyber Solutions Architect, Cyber Security Consultant, Information Security Specialist, Cyber Security Risk Consultant

5.1 Knowledge and Understanding

• Advanced understanding of computer science theory and principles: Graduates of the program should have a deep understanding of the fundamental concepts and principles that underpin computer science, including algorithms, data structures, complexity theory, and programming languages.
• Ability to design, implement and evaluate complex computer systems: Graduates should be able to design, implement and evaluate large and complex computer systems, using a range of programming languages, software development methodologies, and tools.
• Ability to conduct research and contribute to the advancement of computer science: Graduates should have the ability to conduct original research in computer science, and to contribute to the advancement of the field through publications, presentations, and other forms of scholarly communication.
• Ability to apply computer science to real-world problems: Graduates should be able to apply their knowledge of computer science to solve real-world problems, and to work effectively in interdisciplinary teams.

5.2 Skills

• Strong problem solving, analytical and critical thinking skills: Graduates should have strong problem-solving, analytical and critical thinking skills which they should be able to apply to complex computer science problems.
• Proficiency in the use of current technologies and tools: Graduates should have a good proficiency with current technologies and tools, including programming languages, development environments, and software engineering tools.
• Communication and leadership skills. Graduates should have strong communication and leadership skills, enabling them to effectively explain complex technical ideas to non-technical audiences and to manage teams of developers.
• Awareness and understanding of ethical, social and professional issues in computing. Graduates should be aware of ethical, social and professional issues in computing and be able to navigate them and make informed decisions in their professional practice.

8.1 General Pedagogical Methods

• When it comes to pedagogical methods, the programme includes a wide variety of student-centric methods to cater for different learning styles. All components of the programmes will be delivered face-to-face. The modes of delivery include lectures, tutorials, group work, project work, hackathons and competitions.
• In classes with students, lecturers draw from academic and professional literature, present real-world case studies, present current affairs, show videos such as TED talks, invite experts on the topic as guest speakers, and ask students to actively participate in their own learning. Students are also asked to prepare questions and comments on current affairs or on an agreed article to be discussed during tutorials.
• For students who require additional assistance, lecturers can also be reached by email to support students in their learning process. Facilities include classrooms equipped with presentation tools, computer labs with workstations, virtual machine images, high-speed internet connectivity, virtual learning environments, and cloud computing resources.

8.2 General Assessment Methods

Assessment methods include written examinations, coursework assignments, capstone projects, presentations, case studies, project work, and class participation. Most modules have a substantial assignment component which may vary from term papers to implementing algorithms stemming from the unit. The specific assessment weighting is detailed in each module descriptor.

8.3 Grading and Progression

For each module, students are required to achieve the minimum pass mark. Students who fail the module have an opportunity to resit. Should the student fail a second time, they will need to repeat the complete module.