Applied Data Science in collaboration with MITx

90 ECTS MSc MicroMasters program in Statistics and Data Science in collaboration with MITx

Motivation for starting the program

People and the systems around them are generating and storing data at an unprecedented rate and in massive volumes in almost every activity in science, society and industry. At the same time, there is a need for highly skilled people with specialized training that can efficiently find patterns in vast data sources and communicate them in an understandable way. 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.

To take the Master in Data Science program at RU, students have two options: The first option is a 90 ECTS program completed in 2 years intended for part-time (working) students. The second option is a regular 120 ECTS full-time study program, completed in 2 years. Both options build on the MIT Micromaster in Statistics and Data Science, which RU evaluates for 30 ECTS.

The MIT MicroMaster in Statistics and Data Science

Information about the program can be found here:

In their own words:

This MicroMasters® program in Statistics and Data Science was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization.

The MIT micromaster is a 13-month program starting in January or September and is intended for part-time study. This fully online program consists of 5 courses: Probability, Machine Learning, Statistics, Data Analysis and Capstone. The annual course schedule can be found here:

(*) In case the MIT micromaster is discontinued or changed drastically, we can compensate by adapting courses at RU, or offering alternative courses at other universities or MOOCS.

90 ECTS Program - Professional Master in Data Science

The 90 ECTS program is intended for part-time study and students who are also working. Students are expected to attend lectures/problem sessions at least once a week, HMV style. The schedule for the two years is as follows.

MSc in Applied Data Science - 90 ECTS

MSc in Applied Data Science 90 ECTS
Year 1Year 2
  • MIT MicroMaster in Statistics and Data Science - 30 ECTS

Two mandatory courses:

  • Software Project Management - Fall 8 ECTS
  • Research Methodology - Spring 8 ECTS

One restricted elective course:

  • Deep Learning/ or Reinforcement Learning - Fall 8 ECTS*
  • Applied Data Science - Fall 6 ECTS


  • Applied Thesis - Spring 30 ECTS

(*)Student chooses one of the two courses Deep Learning/ Reinforcement Learning



Restricted Electives

This is not an exhaustive list. Students can ask for permission to take other courses counting towards their restricted elective credits.

  • Network Science
  • Big Data Management
  • Advanced Topics in AI
  • Data Driven Security
  • Data Driven e-health
  • Financial Simulation

Contact us

If you have any questions or want to obtain more information about studies at the School of Computer Science at Reykjavík University please contact .

Was the content helpful? Yes No