University of Nicosia, UNIC Athens
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Data Science

BSc, 4 years

Level
Bachelor Degree (1st Cycle)
Duration of studies (full time)
4 Years
Qualification awarded
Bachelor of Science in Data Science
Minimum ECTS Credits
240
Taught in
English
Mode of study
Full-time or Part-time
Campus
Athens

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Programme Information

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:

  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.

Admission

Academic Criteria

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, including the International Baccalaureate (IB) Diploma with a minimum total of 24/45 points and successful completion of the core components.

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, or who are admitted on the basis of an IB Diploma with a total score below 24/45, are provided with extra academic guidance and monitoring during the first year of their studies.

Furthermore, applicants eligible for admission to undergraduate programmes under the Greek legal framework, which establishes minimum admission criteria, include:

a) Greek or foreign nationals holding a General High School Diploma (ΓΕ.Λ.) or a Vocational High School Diploma (ΕΠΑ.Λ.) with an average grade in the four (4) nationwide examination subjects equal to or greater than the minimum admission threshold. This threshold is determined by multiplying the lowest average grade among all examinees within a given scientific field by a coefficient of 0.8 in the year of participation.

b) Holders of equivalent secondary education diplomas from recognized foreign schools in Greece (level 4 of the National Qualifications Framework).

c) Holders of international secondary education diplomas issued by schools in Greece or abroad, foreign nationals with an equivalent high school diploma, or an equivalent vocational education qualification granting them the right to access higher education institutions in the country where they studied.

d) For graduates mentioned in point (a) before the academic year 2021-2022, as well as holders of a first-cycle degree, the terms and conditions for selection and admission are set by the parent institution (University of Nicosia).

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

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

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.

Upon successful completion of the programme, the degree is awarded by the University of Nicosia, the parent institution of UNIC Athens. Each graduate also receives a Diploma Supplement clearly specifying the place of study.

The normal duration of studies (full-time) is 4 years and the maximum duration is 6 years.

Learning Outcomes

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.

Academic Path

Section: A Computer Science Requirements

ECTS: Min.108 Max.108

Course IDCourse TitleECTS Credits
COMP-111Programming Principles I6
COMP-113Programming Principles II6
COMP-140Introduction to Data Science6
COMP-142Software Development Tools for Data Science6
COMP-221Advanced Programming and Paradigms6
COMP-240Data Programming6
COMP-242Data Privacy and Ethics6
COMP-244Machine Learning and Data Mining I6
COMP-248Project in Data Science6
COMP-270Data Structures and Algorithms6
COMP-302Database Management Systems6
COMP-340Big Data6
COMP-342Data Visualization6
COMP-344Machine Learning and Data Mining II6
COMP-405Artificial Intelligence6
COMP-447Neural Networks and Deep Learning6
COMP-494Data Science Final Year Project I6
COMP-495Data Science Final Year Project II6

Section B: Mathematics and Statistics Requirements

ECTS: Min. 54 Max. 54

Course IDCourse TitleECTS Credits
MATH-111Mathematics and Logic for Computation6
MATH-195Calculus I6
MATH-196Calculus II6
MATH-225Probability and Statistics I6
MATH-280Linear Algebra I6
MATH-325Probability and Statistics II6
MATH-326Linear Models I6
MATH-329Bayesian Statistics6
MATH-335Optimization Techniques6

Section C: Major Electives

ECTS: Min. 30 Max. 42

Course IDCourse TitleECTS Credits
COMP-201Systems Analysis and Design6
COMP-212Object-Oriented Programming6
COMP-213Visual Programming6
COMP-263Human Computer Interaction6
COMP-341Knowledge Management6
COMP-343Business Analytics6
COMP-345Robot Programming6
COMP-348Natural Language Processing6
COMP-349Special Topics in Data Science6
COMP-358Networks and Data Communication6
COMP-387Blockchain Programming6
COMP-446Web and Social Data Mining6
COMP-448Computer Vision6
COMP-449Industry Placement in Data Science6
COMP-474Cloud Computing6
COMP-475Internet of Things and Wearable Technologies6
COMP-476Generative AI6
MATH-343Numerical Methods for Data Science6
MATH-420Times Series Modeling and Forecasting6

Section D: Science and Engineering Electives

ECTS: Min.6 Max. 12

Course IDCourse TitleECTS Credits
BIOL-110Elements of Biology6
CHEM-104Introduction to Organic and Biological Chemistry6
ECE-110Digital Systems6
PHYS-110Elements of Physics6

Section E: Business Electives

ECTS: Min.6 Max.12

Course IDCourse TitleECTS Credits
BADM-234Organizational Behavior6
BUS-111Accounting6
ECON-200Fundamental Economics6
MGT-281Introduction to Management6
MGT-370Management of Innovation and Technology6
MIS-215Project Management6
MIS-251Information Systems Concepts6
MIS-303Database Applications Development6
MIS-390E-Business6
MKTG-291Marketing6

Section F: Language Expression

ECTS: Min.12 Max.12

Course IDCourse TitleECTS Credits
BADM-332Technical Writing and Research6
ENGL-101English Composition6

Section G: Humanities and Social Sciences Electives

ECTS: Min.6 Max.12

Course IDCourse TitleECTS Credits
FREN-101French Language and Culture I6
GERM-101German Language and Culture I6
ITAL-101Italian Language and Culture I6
PHIL-101Introduction to Philosophy6
PHIL-120Ethics6
PSY-110General Psychology I6
SOC-101Principles of Sociology6
UNIC-100University Experience6

Semester Breakdown

Semester 1

Course IDCourse TitleECTS Credits
COMP-140Introduction to Data Science6
COMP-111Programming Principles I6
MATH-111Mathematics and Logic for Computation6
MATH-195Calculus I6
ENGL-101English Composition6

Semester 2

Course IDCourse TitleECTS Credits
COMP-113Programming Principles II6
COMP-142Software Development Tools for Data Science6
MATH-196Calculus II6
MATH-225Probability and Statistics I6
PSY-110General Psychology I6

Semester 3

Course IDCourse TitleECTS Credits
COMP-221Advanced Programming and Paradigms6
COMP-240Data Programming6
MATH-325Probability and Statistics II6
MATH-280Linear Algebra I6
BIOL-110Elements of Biology6

Semester 4

Course IDCourse TitleECTS Credits
COMP-244Machine Learning and Data Mining I6
COMP-270Data Structures and Algorithms6
COMP-302Database Management Systems6
MATH-329Bayesian Statistics6
COMP-248Project in Data Science6

Semester 5

Course IDCourse TitleECTS Credits
COMP-344Machine Learning and Data Mining II6
MATH-335Optimization Techniques6
COMP-342Data Visualization6
COMP-242Data Privacy and Ethics6
MKTG-291Marketing6

Semester 6

Course IDCourse TitleECTS Credits
COMP-340Big Data6
MATH-326Linear Models I6
BADM-332Technical Writing and Research6
COMP-343Business Analytics6
COMP-348Natural Language Processing6

Semester 7

Course IDCourse TitleECTS Credits
COMP-405Artificial Intelligence6
COMP-447Neural Networks and Deep Learning6
COMP-494Data Science Final Year Project I6
COMP-446Web and Social Data Mining6
COMP-449Industry Placement in Data Science6

Semester 8

Course IDCourse TitleECTS Credits
COMP-495Data Science Final Year Project II6
COMP-448Computer Vision6
COMP-474Cloud Computing6
COMP-476Generative AI6
BADM-234Organizational Behavior6

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”.

Tuition & Fees

For detailed information about tuition and fees for this programme, visit the Tuition and Fees page.

Accreditation

The programme of study has been accredited by both the Hellenic Authority for Higher Education (HAHE) in Greece and the Cyprus Agency of Quality Assurance and Accreditation in Higher Education (CYQAA).