Master of Science (MSc) in Computer Science (Data Science and Artificial Intelligence)

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. Introduction to Artificial Intelligence MQF/EQF Level 7, 12 ECTS
4. Applied Artificial Intelligence MQF/EQF Level 7, 12 ECTS
5. 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 32 ECTS:

1. Machine Learning MQF/EQF Level 7, 10 ECTS
2. Natural Language Processing MQF/EQF Level 7, 10 ECTS
3. Software Engineering MQF/EQF Level 7, 10 ECTS
4. Database Systems Implementation MQF/EQF Level 7, 10 ECTS
5. Data Intensive Systems MQF/EQF Level 7, 8 ECTS
6. Data Visualisation MQF/EQF Level 7, 8 ECTS
7. Formal languages and automataMQF/EQF Level 7, 8 ECTS
8. Introduction to Computer Security MQF/EQF Level 7, 12 ECTS
9. Cryptography MQF/EQF Level 7, 12 ECTS
10. Fintech and Blockchain 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.

The courses outlined below all stem from the main track (Master of Science (MSc) in Computer Science) –

Master of Science (MSc) in Computer Science (Data Science and Artificial Intelligence)
The Artificial Intelligence track is an advanced research-led course in the field of Data Science and Artificial intelligence, developing students’ skills in logic, language processing and machine learning applied to various problem domains in research and industry.

1. Data Scientist,
2. Data Engineer,
3. Data Analyst,
4. Research Analyst,
5. Software Engineer,
6. Machine Learning Engineer,
7. Senior Data Scientist,
8. Data Science team lead,
9. Senior Research Analyst.

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).

Introduction to Artificial Intelligence –  300 (Contact Hours : 60 | Self-Study : 158 | Supervised, Placement and Tutorials : 0 | Assessment : 80).

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).

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

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

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

1. Advanced understanding of AI theories and techniques: Graduates of the program should have a deep understanding of the fundamental concepts and principles of AI, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning.
2. Ability to design and implement AI systems: Graduates should be able to design and implement AI systems, using a range of programming languages, software development methodologies, and tools.
3. Ability to evaluate and improve the performance of AI systems: Graduates should have the ability to evaluate the performance of AI systems and to use a variety of techniques to improve their performance, such as data pre-processing, feature extraction, model selection, and hyper-parameter tuning.
4. Familiarity with ethical, social, and professional issues in AI: Graduates should be familiar with ethical, social and professional issues in AI, such as bias, privacy, transparency and explainability, and be able to navigate them and make informed decisions in their professional practice

Ability to conduct research and contribute to the advancement of AI: Graduates should have the ability to conduct original research in AI, and to contribute to the advancement of the field through publications, presentations, and other forms of scholarly communication.