M.Sc. Data Science

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 specialized training and skills to analyze massive data sets and communicate their findings effectively. The MSc in Data Science program equips students with these skills, making them highly sought-after professionals in a data-driven future.

Overview

Lara-Margret-Tolvunarfraedi

Level: Graduate
Credits: 120 ECTS
Language of instruction: English
Duration: Two years, full-time
Start date: August
Program leader: Dr. María Óskarsdóttir

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 as well as to develop and improve algorithms to better accommodate various types of data and new applications of data science. 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 the insights in a meaningful and intuitive manner.


Structure

We offer two unique tracks to cater to students with different levels of expertise.

Course-based track

The course-based track is designed for students with a weaker computer science and mathematics background. This program 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.

MSc Data Science, Course-based track  
Year 1, Fall

  • T-705-ASDS Applied Statistics for Data Science 8 ECTS
  • T-740-SPMM Software Project Management 8 ECTS
  • T-809-DATA Data Mining & Machine Learning 8 ECTS

  • T-750-SMAC Statistical Modelling & Computation 6 ECTS


Year 1, Spring

  • T-701-REM4 Research Methodology 8 ECTS
  • T-606-PROB Probability & Stochastic Processes 6 ECTS
  • T-820-DEEP Deep Learning 8 ECTS

  • T-786-APDS Applied Data Science
    6 ECTS

Year 2, Fall

  • Elective 8 ECTS
  • Elective 8 ECTS
  • Elective 8 ECTS
  • 3 week elective 6 ECTS

Year 2, Spring

  • T-899-MSTH MS Thesis 24 ECTS
  • T-991-TPDE Thesis Project Defense 6 ECTS

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.

MSc Data Science, Research-based  track 
  
 Year 1, Fall

  • T-705-ASDS Applied Statistics for Data Science 8 ECTS
  • T-740-SPMM Software Project Management 8 ECTS
  • T-809-DATA Data Mining & Machine Learning 8 ECTS

  • T-750-SMAC Statistical modelling & computation 6 ECTS

Year 1, Spring

  • T-701-REM4 Research Methodology 8 ECTS
  • T-606-PROB Probability & Stochastic Processes 6 ECTS
  • T-820-DEEP Deep Learning 8 ECTS
  • T-786-APDS Applied Data Science
    6 ECTS
 Year 2, Fall

  • T-879-MSRS MSc Research 30 ECTS






Year 2 Spring

  • T-899-MSTH MSc Thesis 24 ECTS
  • T-991-TPDE Thesis Project Defense 6 ECTS
 

Electives

This is not an exhaustive list. Students may request permission to take other courses as electives.

  • T-725-MALV Natural Language Processing
  • T-630-NSMA Network Science
  • T-764-DATA Big Data Management
  • T-720-ATAI Advanced Topics in AI
  • T-710-MLCS Machine Learning in Cyber Security
  • T-742-CSDA Computer Security: Defense Against the Dark Arts
  • T-702-MDGH Digital Health
  • T-517-FSIM Financial Simulation
  • T-521-RELE Reinforcement Learning
  • T-766-BLMR Black Mirror
  • T-768-SMAI Informed Search Methods in AI




Admission

MSc in Data Science(120 ECTS):

- BSc degree in Computer Science or a related field is required.

Students whose first degree is not in Computer Science may be required to complete preparatory classes from the undergraduate computer Science degree at Reykjavik University.

Supporting documents

  • CV/resume
  • Official university transcript/s, including educational career and academic results.
  • Submission of a letter of motivation (approx. 300 words) that includes reasoning for pursuing graduate work and academic goals.
  • Two academic references. You should select two referees who can provide an informed view of your academic or professional ability and suitability for your chosen programme of study. The letters of recommendation should be sent directly to  td@ru.is
  • Applicants are only invited to an interview after they have sent all necessary documents.

Contact us

If you have any questions or want more information about studies at the School of Computer Science at Reykjavik University, please contact td@ru.is .

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