Data Science

(BSc, 4 years)

Duration

4 Years

Qualification Awarded

BSc in Data Science

Level of Qualification

Bachelor Degree (1st Cycle)

Language of Instruction

English

Mode of Study

Full-time or Part-time

Minimum ECTs Credits

240

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Data Science (BSc, 4 years)

Duration	4 Years
Qualification Awarded	BSc in Data Science
Level of Qualification	Bachelor Degree (1^st Cycle)
Language of Instruction	English
Mode of Study	Full-time and part-time
Minimum ECTS Credits	240

Apply Today

Overview

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:

Providing students with the technical and analytical skills required for acquiring,
managing, analyzing, and extracting insight from data.
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.
Providing students with software engineering and machine learning skills to design and
implement scalable, reliable, and maintainable solutions for data-oriented problems.
Enable students to assess the level of privacy and security of a technical solution to a data science problem.
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).
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.

Admission

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) – 60 and above
Cambridge Exams (First Certificate) – 160 and above or Pass
Cambridge Exams (Proficiency Certificate) – 180 and above or Pass

Assessment

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

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.

Learning Outcomes

Key Learning Outcomes

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

Apply theory and methodologies of several data science-oriented topics in mathematics, statistics and computing to solve problems in real-world contexts.
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.
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).
Apply data management to clean, transform and query data.
Select and apply suitable machine learning algorithms and software tools to perform data analysis.
Perform data visualization and apply inference procedures to analyze data and interpret and communicate results.
Assess the data privacy and security issues raised during the various stages data management.
Demonstrate professional and ethical responsibility in data ownership, security and sensitivity of data.
Communicate technical ideas effectively through both oral presentations and written reports.

Academic Path

Section A: Computer Science Requirements
ECTS: Min.108 Max.108

Course ID	Course Title	ECTS Credits
COMP-111	Programming Principles I	6
COMP-113	Programming Principles II	6
COMP-140	Introduction to Data Science	6
COMP-142	Software Development Tools for Data Science	6
COMP-221	Advanced Programming and Paradigms	6
COMP-240	Data Programming	6
COMP-242	Data Privacy and Ethics	6
COMP-244	Machine Learning and Data Mining I	6
COMP-248	Project in Data Science	6
COMP-270	Data Structures and Algorithms	6
COMP-302	Database Management Systems	6
COMP-340	Big Data	6
COMP-342	Data Visualization	6
COMP-344	Machine Learning and Data Mining II	6
COMP-405	Artificial Intelligence	6
COMP-447	Neural Networks and Deep Learning	6
COMP-494	Data Science Final Year Project I	6
COMP-495	Data Science Final Year Project II	6

Section B: Mathematics and Statistics Requirements
ECTS: Min. 54 Max. 54

Course ID	Course Title	ECTS Credits
MATH-111	Mathematics and Logic for Computation	6
MATH-195	Calculus I	6
MATH-196	Calculus II	6
MATH-225	Probability and Statistics I	6
MATH-280	Linear Algebra I	6
MATH-325	Probability and Statistics II	6
MATH-326	Linear Models I	6
MATH-329	Bayesian Statistics	6
MATH-335	Optimization Techniques	6

Section C: Major Electives
ECTS: Min. 30 Max. 42

Course ID	Course Title	ECTS Credits
COMP-201	Systems Analysis and Design	6
COMP-212	Object-Oriented Programming	6
COMP-213	Visual Programming	6
COMP-263	Human Computer Interaction	6
COMP-341	Knowledge Management	6
COMP-343	Business Analytics	6
COMP-345	Robot Programming	6
COMP-348	Natural Language Processing	6
COMP-349	Special Topics in Data Science	6
COMP-358	Networks and Data Communication	6
COMP-387	Blockchain Programming	6
COMP-446	Web and Social Data Mining	6
COMP-448	Computer Vision	6
COMP-449	Industry Placement in Data Science	6
COMP-474	Cloud Computing	6
COMP-475	Internet of Things and Wearable Technologies	6
COMP-476	Generative AI	6
MATH-343	Numerical Methods for Data Science	6
MATH-420	Times Series Modeling and Forecasting	6

Section D: Science and Engineering Electives
ECTS: Min.6 Max. 12

Course ID	Course Title	ECTS Credits
BIOL-110	Elements of Biology	6
CHEM-104	Introduction to Organic and Biological Chemistry	6
ECE-110	Digital Systems	6
PHYS-110	Elements of Physics	6

Section E: Business Electives
ECTS: Min.6 Max.12

Course ID	Course Title	ECTS Credits
BADM-234	Organizational Behavior	6
BUS-111	Accounting	6
ECON-200	Fundamental Economics	6
MGT-281	Introduction to Management	6
MGT-370	Management of Innovation and Technology	6
MIS-215	Project Management	6
MIS-251	Information Systems Concepts	6
MIS-303	Database Applications Development	6
MIS-390	E-Business	6
MKTG-291	Marketing	6

Section F: Language Expression
ECTS: Min.12 Max.12

Course ID	Course Title	ECTS Credits
BADM-332	Technical Writing and Research	6
ENGL-101	English Composition	6

Section G: Humanities and Social Sciences Electives
ECTS: Min.6 Max.12

Course ID	Course Title	ECTS Credits
FREN-101	French Language and Culture I	6
GERM-101	German Language and Culture I	6
ITAL-101	Italian Language and Culture I	6
PHIL-101	Introduction to Philosophy	6
PHIL-120	Ethics	6
PSY-110	General Psychology I	6
SOC-101	Principles of Sociology	6
UNIC-100	University Experience	6

Semester Breakdown

Semester 1

Course ID	Course Title	ECTS Credits
COMP-140	Introduction to Data Science	6
COMP-111	Programming Principles I	6
MATH-111	Mathematics and Logic for Computation	6
MATH-195	Calculus I	6
ENGL-101	English Composition	6

Semester 2

Course ID	Course Title	ECTS Credits
COMP-113	Programming Principles II	6
COMP-142	Software Development Tools for Data Science	6
MATH-196	Calculus II	6
MATH-225	Probability and Statistics I	6
PSY-110	General Psychology I	6

Semester 3

Course ID	Course Title	ECTS Credits
COMP-221	Advanced Programming and Paradigms	6
COMP-240	Data Programming	6
MATH-325	Probability and Statistics II	6
MATH-280	Linear Algebra I	6
BIOL-110	Elements of Biology	6

Semester 4

Course ID	Course Title	ECTS Credits
COMP-244	Machine Learning and Data Mining I	6
COMP-270	Data Structures and Algorithms	6
COMP-302	Database Management Systems	6
MATH-329	Bayesian Statistics	6
COMP-248	Project in Data Science	6

Semester 5

Course ID	Course Title	ECTS Credits
COMP-344	Machine Learning and Data Mining II	6
MATH-335	Optimization Techniques	6
COMP-342	Data Visualization	6
COMP-242	Data Privacy and Ethics	6
MKTG-291	Marketing	6

Semester 6

Course ID	Course Title	ECTS Credits
COMP-340	Big Data	6
MATH-326	Linear Models I	6
BADM-332	Technical Writing and Research	6
COMP-343	Business Analytics	6
COMP-348	Natural Language Processing	6

Semester 7

Course ID	Course Title	ECTS Credits
COMP-405	Artificial Intelligence	6
COMP-447	Neural Networks and Deep Learning	6
COMP-494	Data Science Final Year Project I	6
COMP-446	Web and Social Data Mining	6
COMP-449	Industry Placement in Data Science	6

Semester 8

Course ID	Course Title	ECTS Credits
COMP-495	Data Science Final Year Project II	6
COMP-448	Computer Vision	6
COMP-474	Cloud Computing	6
COMP-476	Generative AI	6
BADM-234	Organizational Behavior	6

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”.