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Computer Science & IT
Master in Data Science (Online) (MSc) - UNIC (Nicosia)
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Master in Data Science (Online) (MSc) - UNIC (Nicosia)

University of Nicosia
 1206 Points
1 Year, 6 Months
Apply date
Sep 2024

Program Description

The aim of this master in Data Science (Online) (MSc) is to provide students with advanced technical skills and a scientific understanding of Data Science.

Moreover, the master in Data Science (Online) (MSc) will aid students in developing research competency so they can design their own scientific methods pushing the frontiers of this newly 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 disrupts 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 master in Data Science (Online) (MSc) will develop full-stack research data scientists that are able to collect requirements, innovate, design, implement, and critically evaluate a data science solution.

Entry Requirements

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Objective of the Program

  • 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.


Year 1

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

Year 2

Semester 3

Choose one of the two following options:

Non-Thesis Option:
Deep and Reinforcement Learning
Big Data Management and Processing
Artificial Intelligence

Thesis Option:

Learning Outcomes

Upon successful completion of this master in Data Science (Online) (MSc), 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.

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