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MSc in Data Science

How to apply

The Programme

The Programme

For almost half a century now, the world has been producing and collecting data in digital form. However, the past decade hardware and network advances have allowed for very large, cost-effective storage and powerful processing as well as fast transfer speeds. At the same time, scientists have been developing software tools for the analysis of big and complex data, to extract valuable knowledge from them. The goal of data scientists is to make sense of large volumes of different forms of real-world data that may come from the entire spectrum of human activity.
Data Science nowadays is an “umbrella term” that encompasses a variety of scientific fields. Essentially, it is an interdisciplinary field that combines a multitude of different disciplines, some of the most important being the following:

  • Artificial Intelligence & Machine Learning
  • Statistics & Mathematics
  • Database and Big Data technology
  • Software Development & Algorithmics

However, the above list is not exhaustive, as a data scientist often needs to employ other skills, such as hacking, coding, critical thinking, problem understanding etc. All those make the job of the data scientist to be a mashup of different skills that are rarely found together.

Students who apply for the MSc in Data Science of the International Hellenic University, are mainly graduates with a STEM (Science, Technology, Engineering and Mathematics) or an Economics degree, who have a background in statistics and a good knowledge of fundamental concepts of databases and programming.

 

The Structure

The MSc in Data Science (full-time) is a 14-month programme taught over three terms. Lectures mainly take place on weekday evenings. The MSc in Data Science programme is also available in part-time mode over 26 months for those who cannot commit to a full-time programme either for work or other reasons.

Upon arrival at the IHU all students follow an intensive foundation course titled "Applied Mathematics in ICT" that aims to bring all incoming students to the same level with respect to some of the mathematics knowledge that is required to excel in the programme. During the first term, all students are required to attend five mandatory core courses. During the second term, all students follow a further three required courses and a combination of two elective courses. Finally, during the third term, work is dedicated exclusively to the Master's dissertation.

 

Applications are open!

 

Programme brochure

 

Programme announcement

 

Downloads

 

Courses

Courses

During the second term students tailor their programme further by choosing elective courses. The choice of elective courses must sum up to 12 ECTS (2 courses). Some of the elective courses may not be offered in a particular year, depending entirely on student demand.

 

1st Term Core Courses

 

2nd Term Core Courses

 

2nd Term Elective Courses

 

Dissertation

Dissertation

During the third term, students work on their Masters Dissertation project, the thematic area of which is relevant to their programme of studies and their interests. The dissertation provides a good opportunity to apply theory and concepts learned in different courses to a real-world Data Science problem or challenge. Students are supervised throughout their projects by a member of the academic faculty and the academic assistants. After submission of the dissertation, students present their projects to classmates and faculty at a special event.

 

Career Paths

Career Paths

According to the European Commission’s European Data Market study the number of “data companies” as well as the need for “data workers” are already high and it is expected to grow even more in the near future. Depending on the focus of your study and skills, there are several career paths you can follow; the list below, although it is non-exhaustive, it covers the spectrum of roles you can play in an organisation:

  • Data Management Professional: Focuses on managing the infrastructure and storage of (usually big) data.
  • Data Engineer/Data Architect: Focuses on the design and implementation of (usually big) data infrastructure, choosing the right database and cloud technologies and deploying them to serve the analytics needs of the organization.
  • Business Analyst: Focuses on the analytics part, trying to process data to build models to form useful and actionable insights. It includes anything related to Business Intelligence, such as creating reports, dashboards etc.
  • Data Analyst/Data Scientist: Focuses on developing and applying machine learning and statistical models on the data at hand. They need to have coding skills, with Python and R being the most popular options right now as well as knowledge of algorithmics, statistics and databases.
  • Machine Learning Researcher: Focuses on developing and testing predictive and descriptive models from data. They need to have a deep understanding of machine learning and statistics to run experiments and evaluate the results. Machine learning research positions are available not only in universities (e.g. PhD candidateships, PostDoc Associates etc.) but also in industry as big companies are being staffed with analytics researchers who try to create custom models for their needs instead of using off-the-shelf products and applying sub-optimal, generic solutions.


In addition to technical skills gained through study, our students benefit from the University's excellent Careers Office in order to attain essential soft skills (e.g. communication skills, interview preparation, CV writing etc.) to better prepare for the job market.