Data Science MSc
What will you learn?
Today, data is being generated and stored at an unprecedented pace across all industries, from science to society. To accurately comprehend this vast amount of information, it is crucial to have individuals with specialised training and skills to analyse massive data sets and communicate their findings effectively. The MSc in Data Science programme equips students with these skills, making them highly sought-after professionals in a data-driven future.
Data science is an interdisciplinary field of computational principles, methods and systems for extracting and structuring knowledge from data. The objective is not only to find patterns in data but to make predictions based on past knowledge and to develop and improve algorithms to better accommodate various types of data and new data science applications. Important tasks in data science span a wide range, from manipulating unstructured data and merging heterogeneous data sources to mastering the art of visualizing data to convey insights meaningfully and intuitively.
How will you learn?
Our MSc in Data Science blends specialised technical knowledge with practical skills.
Personalised academic guidance
Each new student is assigned an Academic Advisor—a faculty member who helps select courses and tailor a customised study plan that aligns with their academic interests.
Programme highlights
- Small class sizes: Benefit from a more personalised education experience, with most classes limited to no more than 20 students, allowing for close interaction with faculty.
- Research centre integration: Upon commencing their thesis, students are integrated into the research centre associated with their supervisor. This integration offers numerous benefits, including invitations to seminars and other activities, exposure to international visiting faculty, and access to an extensive network of researchers.
- Independent study: Delve into your research interests under faculty guidance through independent research projects.
Programme delivery
Our MSc programmes are delivered onsite and designed for full-time study. For students who wish to extend their study period, it’s possible to take fewer courses each semester and complete the programme over a longer duration. Please note that all classes require attendance during daytime hours.
Research and industry collaboration
- Thesis options: Engage in deep research with a one-semester or full-year thesis project. For those not pursuing a full-year project, we offer independent research opportunities for up to 16 ECTS.
- Industry partnerships: Benefit from our collaborations with leading companies like Veitur and Marel, which provide specially designed projects and internships. You may also collaborate with these partners on your thesis or engage in a project at your current place of employment.
Earn while you learn: teaching assistant opportunities
Graduate students can enrich their educational experience and earn a salary by working as teaching assistants in our BSc programme, gaining valuable teaching and academic skills.
Lára Margrét H Hólmfríðardóttir: We can learn so much about the world and ourselves
Careers
Future prospects
A master's degree in data science is a strong foundation for any employment that requires data processing and analysis. Data science teams are becoming a reality in forward-thinking private-sector companies and public-private partnerships. Aside from that, there is a growing demand for data-savvy individuals across departments who understand the value of learning from data to improve operations and customer service. Municipalities and the public sector have also taken significant steps to improve data utilisation.
In today's culture, particularly in the digital age, many decisions depend on data and the outcomes of artificial intelligence models trained on it. Furthermore, data is increasingly being used to inform service developments, such as those in health sciences, education, financial technology and banking services, tourism, public sector services, etc.
Structure
Programme structure
We offer two unique tracks to cater to students with different levels of expertise in computer science and mathematics. Students do not need to declare which track they are taking until the end of their first year, allowing them time to assess their interests and academic direction before making a commitment
Course-based track
The course-based track is suited for students with foundational knowledge in computer science and mathematics. The route requires the completion of 90 ECTS of courses over 18 months, followed by a 30 ECTS MSc thesis. The track provides a comprehensive and structured approach to help students improve their skills and knowledge in the field before undertaking a thesis project.
Research-based track
The research-based track is intended for students with a strong computer science and mathematics foundation. Students are expected to possess sufficient knowledge to enroll in our advanced courses during their first year of studies. This ensures they can complete the necessary coursework to undertake a research-oriented thesis of 60 ECTS in their second year. This track is particularly suitable for students who have a specific research focus or are considering continuing onto postgraduate (PhD) studies.
Mandatory courses
Students are required to complete the following mandatory courses. Refer to the programme structure below for details on when these courses are offered.
- T-705-ASDS Applied Statistics for Data Science
- T-740-SPMM Software Project Management
- T-711-FOML Fundamentals of Machine Learning
- T-750-SMAC Statistical Modelling & Computation
- T-701-REM4 Research Methodology
- T-820-DEEP Deep Learning
- T-786-APDS Applied Data Science
Term structure
Each semester is divided into two parts:
- 12-week Semester: During this period, students usually take 3–4 courses, equivalent to 24 ECTS.
- 3-week Semester: This shorter, intensive period focuses on a single course. Classes take place daily, fostering an immersive and practical learning experience.
Entry requirements
Tuition and scholarships
For information on tuition fees, please see tuition fees.