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

Program Overview

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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

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 Criteria

• 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.

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 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.

Key Learning Outcomes

Upon successful completion of this program, the student is expected to:

1. Critically collect requirements, design, implement, and assess the performance of a data science solution.
2. 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).
3. 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.
4. 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.
5. Design solutions real world challenges of data mining (big data, streaming data, heterogeneous data, noisy data, etc.).
6. Synthesize reports and presentations for communicating analysis results and debating on data-driven decisions.
7. 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

• COMP-501DL: Research Seminars and Methodology (4 ECTS)
• COMP-540DL: Mathematics for Data Science (10 ECTS)
• COMP-542DL: Data Programming (10 ECTS)
• COMP-543DL: Managing and Visualizing Data (10 ECTS)
• COMP-544DL: Machine Learning (10 ECTS)
• COMP-592DL: Project in Data Science (6 ECTS)

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

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

Semester Breakdown

Semester 1

• COMP-542DL: Data Programming (10 ECTS)
• COMP-540DL: Mathematics for Data Science (10 ECTS)
• COMP-552DL: Data Privacy and Ethics (10 ECTS)

Semester 2

• COMP-544DL: Machine Learning (10 ECTS)
• COMP-543DL: Managing and Visualizing Data (10 ECTS)
• COMP-501DL: Research Seminars and Methodology (4 ECTS)
• COMP-592DL: Project in Data Science (6 ECTS)

Semester 3 (Non-Thesis Option)

• COMP-546DL: Deep and Reinforcement Learning (10 ECTS)
• COMP-548DL: Big Data Management and Processing (10 ECTS)
• COMP-549DL: Artificial Intelligence (10 ECTS)

Semester 3

• COMP-593DL: Thesis (30 ECTS)

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”.

Program Outline

Degree Overview

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Profile of the Programme

This program equips students with advanced technical skills and a scientific understanding of Data Science. It aims to:

• Provide students with technical and analytical skills for acquiring, managing, analyzing, and extracting knowledge from heterogeneous data sources.
• Develop research competency so students can design their scientific methods and push the boundaries of this emerging field.
• Focus on industry-relevant skills through collaborations with industry instructors.
• Develop full-stack research data scientists who can collect requirements, innovate, design, implement, and critically evaluate data science solutions.
• Enable students to assess and provide solutions for privacy and ethical issues arising from applying data science methods.
• Provide opportunities for students to work on real-world problems with real data and collaborate with industrial partners.
• Gain hands-on experience with state-of-the-art data science technologies like Deep and Reinforcement learning.
• Prepare students for pursuing a PhD in data science or other data science-related fields (e.g., bioinformatics, computational social science, etc.)
• Instill a strong sense of social commitment, global vision, and independent self-learning ability.

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Learning Outcomes

Upon completing the program, students will be able 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 other interdisciplinary fields requiring Data Science skills.
• Identify and communicate data privacy and ethics issues as they arise from specific real-world applications and synthesize solutions to alleviate these issues.
• Communicate and collaborate with teams on interdisciplinary problems.
• Design solutions for 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 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.
## Outline

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Program Structure:

• Section A: Major Requirements (50 ECTS): Covers foundational topics in data science, including research seminars and methodology, mathematics for data science, data programming, managing and visualizing data, machine learning, and a data science project.
• Section B: Electives (40 ECTS): Allows students to specialize in specific areas of interest, with choices like Deep and Reinforcement Learning, Social and Web Data Mining, Big Data Management and Processing, Artificial Intelligence, Business Intelligence, Data Privacy and Ethics, Data Science in Bioinformatics and Medicine, and Thesis.

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Semester Breakdown:

• Semester 1: Data Programming, Mathematics for Data Science, Data Privacy and Ethics.
• Semester 2: Machine Learning, Managing and Visualizing Data, Research Seminars and Methodology, Project in Data Science.
• Semester 3 (Non-Thesis Option): Deep and Reinforcement Learning, Big Data Management and Processing, Artificial Intelligence.
• Semester 3 (Thesis Option): Thesis (30 ECTS).

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Course Descriptions:

• COMP-542DL Data Programming: Introduces fundamental programming skills for data science.
• COMP-540DL Mathematics for Data Science: Covers mathematical concepts crucial for data science, such as linear algebra, probability, and statistics.
• COMP-543DL Managing and Visualizing Data: Explores data management techniques and visualization tools for data exploration and analysis.
• COMP-544DL Machine Learning: Provides a comprehensive understanding of machine learning algorithms and techniques.
• COMP-592DL Project in Data Science: Allows students to apply their acquired skills to a real-world data science project.
• COMP-546DL Deep and Reinforcement Learning: Delves into advanced topics like deep learning and reinforcement learning techniques.
• COMP-547DL Social and Web Data Mining: Explores data mining techniques applicable to social and web data.
• COMP-548DL Big Data Management and Processing: Focuses on managing and processing large-scale datasets efficiently.
• COMP-549DL Artificial Intelligence: Examines the principles and approaches of artificial intelligence.
• COMP-551DL Business Intelligence: Applies data science techniques to business intelligence applications.
• COMP-552DL Data Privacy and Ethics: Discusses critical data privacy and ethical issues in data science.
• COMP-553DL Data Science in Bioinformatics and Medicine: Explores applications of data science in bioinformatics and medicine.
• COMP-593DL Thesis: Provides opportunities for students to pursue original research in their chosen area of data science.
## Assessment Course assessment typically combines a final exam and continuous assessment through interactive activities, projects, forum participation, and assignments. The final grade for each course is calculated based on the weight of these components and the actual numerical marks obtained. Students need to maintain a minimum cumulative grade point average (CPA) of 2.0 to graduate and achieve a minimum grade of 'C' to meet individual course requirements. ## Teaching The program features diverse teaching methods, including:
• Interactive lectures: Engaging classroom sessions with faculty actively guiding students through concepts and practical applications.
• Hands-on workshops: Practical sessions for students to apply acquired knowledge and gain valuable practical experience.
• Project-based learning: Collaborative projects challenging students to apply their data science skills to real-world problems.
• Guest lectures: Industry experts providing insights into current data science trends and practical applications.
The program boasts an experienced faculty with expertise in data science, machine learning, and related fields. The faculty includes both resident professors and adjunct faculty from renowned institutions and organizations. ## Careers This program prepares graduates for various career paths in the rapidly growing field of data science, such as:
• Data Scientist
• Machine Learning Engineer
• Artificial Intelligence Specialist
• Business Intelligence Analyst
• Data Analyst
• Big Data Engineer
• Data Architect
• Research Scientist
• Research Engineer
Graduates can work in diverse industries, including:
• Information and Communication Technologies
• Healthcare
• Finance
• Business Intelligence
• Marketing
• Education
• Research and Development
• E-commerce
• Social Media
## Other
• The program offers a unique opportunity for students to work directly with real-world data and collaborate with industrial partners.
• It provides access to state-of-the-art data science technologies and software.
• The program emphasizes practical skills and problem-solving abilities, preparing graduates for immediate impact in the job market.