Data Science

(MSc, 3 Semesters) - E-Learning/Distance Learning (Online)

Duration

3 Semesters

Qualification Awarded

Master of Science in Data Science

Level of Qualification

Master Degree (2nd Cycle)

Language of Instruction

English

Mode of Study

E-Learning/Distance Learning (Online)

Minimum ECTs Credits

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Data Science (MSc, 3 Semesters) – E-Learning/Distance Learning (Online)

Duration	3 Semesters
Qualification Awarded	Master of Science in Data Science
Level of Qualification	Master Degree (2nd Cycle)
Language of Instruction	English
Mode of Study	E-Learning/Distance Learning (Online)
Minimum ECTS Credits	90

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Overview

Profile of the Programme

The aim of this program is to provide the students with advanced technical skills and a scientific understanding of Data Science. Moreover, the MSc will aid students in developing research competency so they can design their own scientific methods pushing the frontiers of this new emerging field. Finally, special emphasis is given to the development of skills that are required by the relevant cutting-edge industries.

Data Science is an applied science providing innovations and disrupting multiple industries ranging from Information and Communication Technologies and Medicine, to Journalism and Finance. The University of Nicosia has developed partnerships with instructors from the industry and this will enable the development of skills that are currently required by the industry. The MSc will develop full-stack research data scientists that are able to collect requirements, innovate, design, implement and critically evaluate a data science solution.

More specifically, the program aims at:

Providing students with the technical and analytical skills required for acquiring, managing, analyzing and extracting knowledge from heterogeneous data sources. Critical skills will be developed that aid students in making decisions on the appropriate data analysis pipeline. Students will be able to collect requirements, design, implement and evaluate a data science solution.
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 develop data programming skills for multiple business domains and possible challenges (Big Data, Streaming Data, Noisy Data, etc.).
Enabling students to assess and provide solutions for the privacy and ethical issues that arise at the application of data science methods to many real-world problems.
In collaboration with instructors from the industry, the student will be aware of the challenges that a professional comes across when moving from theory to practice and know how to overcome these challenges.
Giving the opportunity to the student to work in real world problems with real data in collaboration with industrial partners. Students will gain hands-on experience with the state-of-the-art data science technologies like Deep and Reinforcement learning.
Preparing students to pursue a PhD in data science or to any other field where data science skills are required (e.g. bioinformatics, computational social science, data driven journalism, etc.)
Providing students with a strong sense of social commitment, global vision and independent self-learning ability.

Admission

Admission Criteria

General:
Applications for admission to the program will be considered only from candidates that fulfill the minimum entrance criteria as described below:

A Bachelor Degree in numerate subjects such as, Computer Science, Computer Engineering, Mathematics, Physics, Biology, Economics, Electrical Engineering, from a recognized university with a CPA of at least 2.5. Applicants with lower CPA will be considered on an individual basis.
The students should provide proof of knowledge (such as a certificate from a recognized entity or other relevant documentation) of basic programming and basic mathematics (probabilities or statistics or linear algebra or calculus) unless this background is evident from the list of courses in their previous studies.
Proficiency in the English Language: Students satisfy the English requirements if their first degree was taught in English. Otherwise, they would need to present at least a TOEFL score of 550 paper-based or 213 computer-based, or GCSE “O” Level with “C” or IELTS with a score of 6.0 or score placement at the ENGL-100 level of the University of Nicosia Placement Test.

Specific:

A completed application form;
A Curriculum Vitae indicating the student’s education, academic and
professional experience, any publications, awards, etc.;
List of Courses undertaken along with the grades received in previous degrees. The applicant should highlight the courses that prove basic knowledge of programming and mathematics. In case there are not such courses, the applicant should submit documentation/certificates from recognized organizations that prove such knowledge;
Letters of Recommendation: Two recommendation letters from academic or professional advisors;
Personal Statement: A letter highlighting the applicant’s individual competences and strengths and providing his/her reflections regarding the expectations and value of the program as well as to his/her personal advancement and career development.

Assessment

Course assessment usually comprises of a comprehensive final exam and continuous assessment. Continuous assessment can include amongst others, weekly interactive activities, projects, positive online forum participation 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 90 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 student is expected to:

Critically collect requirements, design, implement, and assess the performance of a data science solution.
Conduct research and develop novel methods for Data Science or any other interdisciplinary field that requires Data Science skills (e.g. Bioinformatics, Data driven journalism, computational social science, business intelligence).
Identify and communicate the issues of data privacy and ethics as they rise from specific real-world applications. The graduates will be able to synthesize solutions that alleviate those issues.
Communicate and collaborate with teams on interdisciplinary problems. The graduate will be able to communicate on low, technical level but also on a high, non-technical level.
Design solutions real world challenges of data mining (big data, streaming data, heterogeneous data, noisy data, etc.).
Synthesize reports and presentations for communicating analysis results and debating on data-driven decisions.
Define, compare, and combine recent research developments in data science, machine learning and artificial intelligence and invent novel potential applications with social and business value.

Academic Path

Section A: Major Requirements
ECTS: Min.50 Max.50

Course ID	Course Title	ECTS Credits
COMP-501DL	Research Seminars and Methodology	4
COMP-540DL	Mathematics for Data Science	10
COMP-542DL	Data Programming	10
COMP-543DL	Managing and Visualizing Data	10
COMP-544DL	Machine Learning	10
COMP-592DL	Project in Data Science	6

Section B: Electives
ECTS: Min. 40 Max. 40
Notes: In order to conduct a Thesis a student has to have all major requirements completed and a minimum CPA of 3.0/4.0

Course ID	Course Title	ECTS Credits
COMP-546DL	Deep and Reinforcement Learning	10
COMP-547DL	Social and Web Data Mining	10
COMP-548DL	Big Data Management and Processing	10
COMP-549DL	Artificial Intelligence	10
COMP-551DL	Business Intelligence	10
COMP-552DL	Data Privacy and Ethics	10
COMP-553DL	Data Science in Bioinformatics and Medicine	10
COMP-593DL	Thesis	30

Semester Breakdown

Semester 1

Course ID	Course Title	ECTS Credits
COMP-542DL	Data Programming	10
COMP-540DL	Mathematics for Data Science	10
COMP-552DL	Data Privacy and Ethics	10

Semester 2

Course ID	Course Title	ECTS Credits
COMP-544DL	Machine Learning	10
COMP-543DL	Managing and Visualizing Data	10
COMP-501DL	Research Seminars and Methodology	4
COMP-592DL	Project in Data Science	6

Semester 3 (Non-Thesis Option)

Course ID	Course Title	ECTS Credits
COMP-546DL	Deep and Reinforcement Learning	10
COMP-548DL	Big Data Management and Processing	10
COMP-549DL	Artificial Intelligence	10

Semester 3

Course ID	Course Title	ECTS Credits
COMP-593DL	Thesis	30

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”.