Duration

12 months full-time / 24 months part-time

Mode

Face-to-face

Address where the program will be delivered

23, Vincenzo Dimech Street, Floriana, Malta

Language of instruction

English language

Assessment

When it comes to assessment methods, we have included quite a variety that will allow learners with different learning styles and abilities to complete the programme successfully. Students will also have to prepare individual and team reports and presentations, apart from written and multiple-choice examinations. Most modules have a heavy assignment component which vary from term papers to implementing algorithms stemming from the unit.

For pass marks, grading and resist systems please refer to the Grading System at the end of the document. In specific reference to the situation where a student fails a module, they will be given one chance to resit, and if they fail the resit too, they will need to redo the module.

MQF Level / EQF Level / RNCP

Level 7

ACCREDITING BODY

Malta Further & Higher Education Authority

ECTS

60 Credits

Accreditation category

Higher Education Programme

Semester 1:

1. 1. Object Oriented Modelling MQF/EQF Level 7, 8 ECTS
2. 2. Research Methods in Computer Science MQF/EQF Level 7, 6 ECTS
3. 3. Introduction to Artificial Intelligence MQF/EQF Level 7, 12 ECTS
4. 4. Applied Artificial Intelligence MQF/EQF Level 7, 12 ECTS

Semesters 2 & 3 (Students need to then choose between the following modules, adding up to 22 ECTS):

1. 1. Machine Learning MQF/EQF Level 7, 10 ECTS
2. 2. Natural Language Processing MQF/EQF Level 7, 10 ECTS
3. 3. Software Engineering MQF/EQF Level 7, 10 ECTS
4. 4. Database Systems Implementation MQF/EQF Level 7, 10 ECTS
5. 5. Data Intensive Systems MQF/EQF Level 7, 12 ECTS
6. 6. Data Visualisation MQF/EQF Level 7, 12 ECTS
7. 7. Formal languages and automata MQF/EQF Level 7, 12 ECTS
8. 8. Introduction to Computer Security MQF/EQF Level 7, 12 ECTS
9. 9. Cryptography MQF/EQF Level 7, 12 ECTS
10. 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) – Post Graduate Diploma 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. 1. Data Scientist,
2. 2. Data Engineer,
3. 3. Data Analyst,
4. 4. Research Analyst,
5. 5. Software Engineer,
6. 6. Machine Learning Engineer,
7. 7. Senior Data Scientist,
8. 8. Data Science team lead,
9. 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. 1. Machine Learning MQF/EQF Level 7, 10 ECTS
2. 2. Natural Language Processing MQF/EQF Level 7, 10 ECTS
3. 3. Software Engineering MQF/EQF Level 7, 10 ECTS
4. 4. Database Systems Implementation MQF/EQF Level 7, 10 ECTS
5. 5. Data Intensive Systems MQF/EQF Level 7, 8 ECTS
6. 6. Data Visualisation MQF/EQF Level 7, 8 ECTS
7. 7. Formal Verification MQF/EQF Level 7, 8 ECTS
8. 8. Introduction to Computer Security MQF/EQF Level 7, 12 ECTS
9. 9. Cryptography MQF/EQF Level 7, 12 ECTS
10. 10. Fintech and Blockchain MQF/EQF Level 7, 12 ECTS

1. 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. 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. 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. 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
5. 5. 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.