Master of Science (MSc) in Computer Science (Blockchain and Fintech)

The full-time  Master of Science(MSc) in Computer Science will be carried out over an 18-month period, into 4 semesters and will be structured as follows:

1. Object Oriented Modelling MQF/EQF Level 7, 8 ECTS
2. Research Methods in Computer Science MQF/EQF Level 7, 6 ECTS
3. Fintech and Blockchain MQF/EQF Level 7, 12 ECTS
4. Final Project MQF/EQF Level 7, 20 ECTS OR Practicuum MQF/EQF Level 7, 20 ECTS

– Students need to then choose a few of the following modules, adding up to 44 ECTS:

1. Introduction to Artificial Intelligence MQF/EQF Level 7, 12 ECTS
2. Applied Artificial Intelligence MQF/EQF Level 7, 12 ECTS
3. Machine Learning MQF/EQF Level 7, 10 ECTS
4. Natural Language Processing MQF/EQF Level 7, 10 ECTS
5. Software Engineering MQF/EQF Level 7, 10 ECTS
6. Database Systems Implementation MQF/EQF Level 7, 10 ECTS
7. Data Intensive Systems MQF/EQF Level 7, 8 ECTS
8. Data Visualisation MQF/EQF Level 7, 8 ECTS
9. Formal Verification MQF/EQF Level 7, 8 ECTS
10. Introduction to Computer Security MQF/EQF Level 7, 12 ECTS
11. Cryptography MQF/EQF Level 7, 12 ECTS

Students who have no training in the field must have completed a bachelor’s in Computer Science, Information technology or in a STEM subject. This applies to students applying for the MSc program, the Post-graduate certificate, the Post-graduate diploma and the awards.

Students without the required background may be allowed to join the course depending on the students’ circumstances and background (2 to 5 years of industry experience may also be considered). Please find our RPL policy as authorised by the MFHEA at the end of the document.

A good grasp of scientific English is also required in order to follow the course. Students will be asked to provide an IELTS certificate higher than grade 7 (or equivalent) or proof of an equivalent level of English before commencing the course if the student has not followed their BSc in a primarily English-speaking country.

Candidates will be asked to present their previously obtained qualifications along with their respective transcripts.

1. DLT developer,
2. Software Engineer,
3. Trading Analyst,
4. Machine Learning Engineer,
5. Team lead.

Object Oriented Modelling –  200 (Contact Hours : 40 | Self-Study : 58 | Supervised, Placement and Tutorials : 2 | Assessment : 100).

Research Methods in Computer Science –  150 (Contact Hours : 30 | Self-Study : 68 | Supervised, Placement and Tutorials : 2 | Assessment : 50).

Final Project –  500 (Contact Hours : 100 | Self-Study : 250 | Supervised, Placement and Tutorials : 10 | Assessment : 140).

Practicuum –  500 (Contact Hours : 5 | Self-Study : 235 | Practicuum : 240 | Assessment : 20).

Fintech and Blockchain –  300 (Contact Hours : 60 | Self-Study : 188 | Supervised, Placement and Tutorials: 2 | Assessment : 50).

– Students need to then choose a few of the following modules, adding up to 32 ECTS:

1. Introduction to Artificial Intelligence MQF/EQF Level 7, 12 ECTS
2. Applied Artificial Intelligence MQF/EQF Level 7, 12 ECTS
3. Machine Learning MQF/EQF Level 7, 10 ECTS
4. Natural Language Processing MQF/EQF Level 7, 10 ECTS
5. Software Engineering MQF/EQF Level 7, 10 ECTS
6. Database Systems Implementation MQF/EQF Level 7, 10 ECTS
7. Data Intensive Systems MQF/EQF Level 7, 8 ECTS
8. Data Visualisation MQF/EQF Level 7, 8 ECTS
9. . Formal Verification MQF/EQF Level 7, 8 ECT
10. Fintech and Blockchain MQF/EQF Level 7, 12 ECTS

1. Advanced understanding of security and cryptography theories and techniques: Graduates of the program should have a deep understanding of the fundamental concepts and principles of security and cryptography, including network security, web security, cryptography algorithms, intrusion detection and prevention, and secure coding practices.
2. Ability to design and implement secure systems: Graduates should be able to design and implement secure systems, using a range of programming languages, software development methodologies, and tools.
3. Familiarity with security standards and regulations: Graduates should be familiar with relevant security standards and regulations, such as those related to data privacy and compliance, and be able to navigate them and make informed decisions in their professional practice.
4. Ability to evaluate and improve the security of systems: Graduates should have the ability to evaluate the security of systems and to use a variety of techniques to improve their security, such as penetration testing, threat modelling, and incident response.