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Profile of the Programme

The aim of the program is to equip students with foundational knowledge, technical skills and practical insight to the inter-disciplinary field of Data Science. The DS program combines theory and practice, based on three main disciplines, Computer Science, Statistics and Mathematics, and real-world application domains. It has been designed to enable graduates of the program to meet the demands of the data-driven economy of the future.

More specifically, the program aims at:

1. Providing students with the technical and analytical skills required for acquiring, managing, analyzing, and extracting insight from data.
2. Providing students with a strong mathematical and statistics foundation that will enable them to appropriately formulate models and apply optimization techniques for data analyses challenges.
3. Providing students with software engineering and machine learning skills to design and implement scalable, reliable, and maintainable solutions for data-oriented problems.
4. Enable students to assess the level of privacy and security of a technical solution to a data science problem.
5. Preparing students to pursue further postgraduate education and research that require expertise in data science and analytical reasoning (such as business analytics, finance, health, bioinformatics).
6. Providing students with a strong sense of social commitment, global vision and independent self-learning ability

Career Prospects

A BSc in Data Science equips students with the skills to pursue a wide range of career opportunities, such as Data Analyst, Business Intelligence, and Quantitative Analyst; Data Engineer; AI/ML Engineer; Data Consultant and Strategist; Data Protection Officer, and more. Data Scientists can work across various industries and professional fields, including:

• Technology & IT (Big Tech, Startups, Software Companies)
• Finance & FinTech (Banks, Investment Firms, Financial Technology)
• Healthcare & Biomedicine (Bioinformatics, Pharmaceutical Companies, Hospitals)
• Industry & Energy (IoT, Smart Grids, Manufacturing)
• Retail & Marketing (E-commerce, Digital Marketing, Customer Analytics)
• Public Sector & Policy Analysis (Data-Driven Policy Making, AI in Government Services)

Access to Further Studies

Graduates of the programme can be accepted into Second Cycle degrees including MSc and PhD programs.

Academic Admission

The minimum admission requirement for an Undergraduate (1st Cycle/Bachelor’s) Degree is a recognised High School Leaving Certificate (HSLC) or equivalent internationally recognized qualifications.

Students with a lower than 7.5/10 or 15/20 or equivalent, HSLC grade depending on the grading system of the country issuing the HSLC, are provided with extra academic guidance and monitoring during the first year of their studies.

English Language Proficiency

The list below provides the minimum English Language Requirements (ELR) for enrollment to the programme of study. Students who do not possess any of the qualifications or stipulated grades listed below and hold IELTS with 4.5 and above, are required to take UNIC’s NEPTON English Placement Test (with no charge) and will receive English Language support classes.

• IELTS, 6 and above
• Anglia Examinations, Advanced and above
• Cambridge GCE AS Level English Language, C and above
• Cambridge GCE English A Levels, C and above
• Cambridge IGCSE or GCSE English as a First language, C and above
• Cambridge IGCSE or GCSE English as a Second language, B and above
• IB English A: Literature SL & HL, 4 and above
• IB English Standard Level (SL), 5 and above
• IB English High Level (HL), 4 and above
• Michigan Language Assessment (also known as Proficiency of Michigan), 650 and above
• Password Test, 6 and above
• TOEFL (IBT), 72 and above
• Cambridge Exams (First Certificate), 160 and above or Pass
• Cambridge Exams (Proficiency Certificate), 180 and above or Pass

Examination Regulations, Assessment and Grading

Course assessment usually comprises of a comprehensive final exam and continuous assessment. Continuous assessment can include amongst others, mid-terms, projects and class participation.

Letter grades are calculated based on the weight of the final exam and the continuous assessment and the actual numerical marks obtained in these two assessment components. Based on the course grades the student’s semester grade point average (GPA) and cumulative point average (CPA) are calculated.

Graduation Requirements

The student must complete 240 ECTS and all programme requirements.

A minimum cumulative grade point average (CPA) of 2.0 is required. Thus, although a ‘D-‘ is a PASS grade, in order to achieve a CPA of 2.0 an average grade of ‘C’ is required.

Key Learning Outcomes

Upon successful completion of this program, the students should be able to:

1. Apply theory and methodologies of several data science-oriented topics in mathematics, statistics and computing to solve problems in real-world contexts.
2. Apply contemporary computing technologies, such as machine learning and data mining, artificial intelligence (AI), parallel and distributed computing, to solve practical problems characterized by big data.
3. Implement algorithms for fundamental data science tasks such as machine learning and data mining, data wrangling, statistical inference etc., using high-level languages suitable for data science (e.g. Python, R, GenAI).
4. Apply data management to clean, transform and query data.
5. Select and apply suitable machine learning algorithms and software tools to perform data analysis.
6. Perform data visualization and apply inference procedures to analyze data and interpret and communicate results.
7. Assess the data privacy and security issues raised during the various stages data management.
8. Demonstrate professional and ethical responsibility in data ownership, security and sensitivity of data.
9. Communicate technical ideas effectively through both oral presentations and written reports.

Semester 1

Semester 2

Semester 3

Semester 4

Semester 5

Semester 6

Semester 7

Semester 8

Note: The above semester breakdown is an indicative one. A few of the courses are electives and can be substituted by others. Students may contact their academic advisor and consult their academic pathway found on this website under “Schools & Programmes”.