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

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Overview

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:

Providing students with the technical and analytical skills required for acquiring, managing, analyzing and extracting insight from data.
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.
Providing students with software engineering and machine learning skills to design and implement scalable, reliable and maintainable solutions for data-oriented problems.
Enabling 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.

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:

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

Academic Path

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 Breakdown

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”.