Key Information
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Campus |  |
Level |  |
Format |
| Ascencia Malta | Masters | On Campus | |||
 |
Duration |  |
Pace |  |
Fees |
| 36 months | Full Time | € 12,000 | |||
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Application Deadline |  |
Start Date |  |
Assessment |
| April/July/Oct 24 | April/July/Oct 24 | Assignments |
About this Course
This is an Accredited Course by the Malta Further & Higher Education Authority (MFHEA), at MQF/EQF Level 7, with 90 ECTS.
Graduates of the Master of Science (MSc) in Computer Science (Data Science and Artificial Intelligence) program will develop a deep understanding of the fundamental concepts and principles of AI, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning.
Upon completion of the program, students will be able to design and implement AI systems, using a range of programming languages, software development methodologies, and tools. Students will develop 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.
Students will become 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.
Finally, students will 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.
Learning Outcomes
By the end of the course, the learner will have:
- 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.
- 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.
- 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.
- 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.
Target Audience
- Data Scientist,
- Data Engineer,
- Data Analyst,
- Research Analyst,
- Software Engineer,
- Machine Learning Engineer,
- Senior Data Scientist,
- Data Science team lead,
- Senior Research Analyst.
Awarding Body
This qualification is awarded by Ascencia Malta. This qualification is accredited by the Malta Further and Higher Education Authority. The MFHEA deems this Master of Science (MSc) in Computer Science (Data Science and Artificial Intelligence) to be at MQF/EQF Level 7 with 90 ECTS in the Malta Qualifications Framework and the European Qualifications Framework for lifelong learning.
Entry Requirements
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.
If an applicant does not meet the above entry requirements, you are encouraged to contact the Academy for an interview.
Assessment Mode
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.
Language of Instruction & Delivery Mode
This programme will be delivered in English in our Campus in Floriana.
The course will be delivered through a flexible combination of modern face-to-face lectures, webinars, seminars and discussion forums, tutorials, group work, case studies, guest speakers, organisational visits and independent study.
Course Funding Schemes
This course is eligible for individual funding through these schemes: GET QUALIFIED SCHEME Candidates can benefit from a 70% rebate of the course fee via tax credits. Learn more about this scheme
