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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 and part-time
Minimum ECTS Credits 240

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Profile of the Programme

The aim of the program is to provide students with technical skills and practical insight to 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. Provide 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. Enabling 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.

Academic Admission

The minimum admission requirement to an undergraduate programme of study is a recognized High School Leaving Certificate (HSLC) or equivalent internationally recognized qualification(s). Students with a lower HSLC grade than 7.5/10 or 15/20 or equivalent 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.

  • TOEFL – 525 and above
  • Computer-based TOEFL – 193 and above
  • Internet-based TOEFL – 80 and above
  • IELTS – 6 and above
  • Cambridge Exams [First Certificate] – B and above
  • Cambridge Exams [Proficiency Certificate – C and above
  • GCSE English Language “O” Level – C and above
  • Michigan Examination of Proficiency in English (CaMLA) – Pass
  • Pearson PTE General – Level 3 and above
  • KPG (The Greek Foreign Language Examinations for the State Certificate of Language Proficiency) – Level B2 and above
  • Anglia – Level B2 and above
  • IEB Advances Programme English – Pass
  • Examination for the Certificate of Proficiency in English (ECPE) Michigan Language Assessment by: Cambridge Assessment English & University of Michigan – 650 average score for ALL skills and above

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 etc.

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, 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, statistical inference etc, using a high-level language which is suitable for data science (e.g. Python, R).
  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. Be able to communicate technical ideas effectively through both oral presentations and written reports.

Section A: Computer Science Requirements
ECTS: Min.114 Max.114

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-211 Data Structures 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-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-370 Algorithms 6
COMP-405 Artificial Intelligence 6
COMP-446 Web and Social Data Mining 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-101 Discrete Mathematics 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-213 Visual Programming 6
COMP-263 Human Computer Interaction 6
COMP-341 Knowledge Management 6
COMP-349 Special Topics in Data Science 6
COMP-358 Networks and Data Communication 6
COMP-387 Blockchain Programming 6
COMP-449 Industry Placement in Data Science 6
COMP-470 Internet Technologies 6
COMP-474 Cloud Computing 6
COMP-475 Internet of Things and Wearable Technologies 6
MATH-281 Linear Algebra II 6
MATH-341 Numerical Analysis I 8
MATH-342 Numerical Analysis II 8
MATH-420 Times Series Modeling and Forecasting 6
MATH-426 Linear Models II 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-303 Database Applications Development 6
MIS-351 Information Systems Concepts 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: Liberal Arts 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 1

Course ID Course Title ECTS Credits
COMP-140 Introduction to Data Science 6
COMP-111 Programming Principles I 6
MATH-101 Discrete Mathematics 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
SOC-101 Principles of Sociology 6

Semester 3

Course ID Course Title ECTS Credits
COMP-211 Data Structures 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-370 Algorithms 6
COMP-302 Database Management Systems 6
MATH-329 Bayesian Statistics 6
COMP-244 Machine Learning and Data Mining I 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
COMP-213 Visual Programming 6

Semester 6

Course ID Course Title ECTS Credits
COMP-340 Big Data 6
COMP-446 Web and Social Data Mining 6
MATH-326 Linear Models I 6
BADM-332 Technical Writing and Research 6
COMP-341 Knowledge Management 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-387 Blockchain Programming 6
MKTG-291 Marketing 6

Semester 8

Course ID Course Title ECTS Credits
COMP-495 Data Science Final Year Project II 6
COMP-449 Industry Placement in Data Science 6
MATH-420 Times Series Modeling and Forecasting 6
COMP-474 Cloud Computing 6
MATH-281 Linear Algebra II 6
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”.
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