Program Overview

---

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

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.

Admission

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

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 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

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:

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. Communicate technical ideas effectively through both oral presentations and written reports.

Academic Path

Section A: Computer Science Requirements

ECTS: Min.114 Max.114

• 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

• 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

• 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

• 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

• 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

• BADM-332: Technical Writing and Research (6)
• ENGL-101: English Composition (6)

Section G: Liberal Arts Electives

ECTS: Min.6 Max.12

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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)

Faculty

• Dr George Chailos
• Professor Ioanna Dionysiou
• Professor Harald Gjermundrod
• Professor Ioannis Katakis
• Professor Constandinos Mavromoustakis
• Professor Nectarios Papanicolaou
• Dr George Portides
• Professor Philippos Pouyioutas
• Dr Andreas Savva
• Professor Athena Stassopoulou
• Dr Vasso Stylianou
• Dr Demetris Trihinas
• Professor Haritini Tsangari

Adjunct Faculty

• Dr Michalis Agathocleous
• Dr Konstantinos Karasavvas
• Dr Nicholas Loulloudes
• Makrides Andreas