120 ECTS MSc - Data Science in collaboration with MITx

Data Science in collaboration with MITx

The 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:https://micromasters.mit.edu/ds/.

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: https://do5zh7b0lqdye.cloudfront.net/documents/MITx_MicroMasters_Program_SDS_Schedule_2020_VC.pdf

(*) 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.

120 ECTS Program

The 120 ECTS program is intended for full-time study. There are 2 tracks in this program. The first track, 60 ECTS thesis track, is aimed for students with a strong background in computer science and mathematics. The second track, 30 ECTS thesis track, is for students with a weaker background in computer science and mathematics.

Track for students with strong background in computing and mathematics:

This track admits students with strong computer science and mathematics background. They are expected to have sufficient knowledge to take advanced courses in the first year alongside the MIT micromaster. This means they can complete enough courses in the first year to take on a 60 ECTS research-oriented thesis in the second year. There is also an option to do more course work in the second year and complete the program with a 30 ECTS thesis. The schedule for the two years is as follows. Compulsory courses are underlined

● Year 1

MIT MicroMaster (full-year - 30 ECTS)

Software Project Management/Elective (autumn - 8 ECTS)

Deep Learning/ Reinforcement Learning 1(*) (autumn - 8 ECTS)

Applied Data Science (autumn - 6 ECTS)

Research Methodology (spring - 8 ECTS)

Network Science/Big Data Management/Reinforcement Learning 2(**) (spring - 6(8) ECTS)

● Year 2

● Electives (autumn - 22/24 ECTS)

● Thesis (spring - 30 ECTS)

OR

● Thesis (full year - 60 ECTS)

(*)Student chooses one of the two courses Deep Learning/ Reinforcement Learning 1

(**)Student chooses one of the three courses Network Science/Big Data Management/ Reinforcement Learning 2

Below is a proposed course schedule for students starting in fall 2020. 

Fall 2020 ECTS Spring ECTS Summer ECTS
Probability Sep1-Dec23 Machine Learning Feb3-May11

Statistics

Data Analysis

May11-Aug31

Jun1-Sep14

Software Project Managment* 8 Research M 8
Deep/Reinforcement learning 8 NetSci/Reinf2/BigDat 8
Applied Data Science (3-weed) 6
Fall 2021 ECTS Spring ECTS
Capstone Sep1-15 Thesis 30
Restricted Elective 8
Restricted Elective 8
Elective 6/8
OR
Thesis Thesis 60

*Taken in the fall semester in the first or second year

Track for students without a strong background in computing and mathematics:

This track admits students who do not have a strong background in computer science and/or mathematics. They may need to strengthen their knowledge in the first year by taking courses such as algorithms, programming, databases, linear algebra and calculus and statistics. These courses may or may not count towards the degree. This track is completed with a 30 ECTS master thesis. The schedule for the two years is as follows. Compulsory courses are underlined

Year 1

MIT MicroMaster (full year - 30 ECTS)

Software Project Management (autumn - 8 ECTS)

● Electives (autumn - 6/8 ECTS)

Research Methodology (spring - 8 ECTS)

Network Science/Big Data Management(**) (spring - 8 ECTS)

Year 2

Deep Learning/ Reinforcement Learning/ Natural Language Processing(*) (autumn - 8 ECTS)

Applied Data Science (autumn - 6 ECTS)

● Electives (autumn - 16 ECTS)

● Thesis (spring - 30 ECTS)

(*)Student chooses one of the three courses Deep Learning/ Reinforcement Learning/ Natural Language Processing

(**)Student chooses one of the two courses Network Science/Big Data Management

Below is a proposed course schedule for students starting in fall 2020. 

Fall 2020 ECTS Spring ECTS Summer ECTS
Probability Sep1-Dec23 Machine Learning Feb3-May11

Statistics

Data Analysis

May11-Aug31

Jun1-Sep14

Software Project Management* 8 Research M 8
Elective 6/8 NetSci/BigDat 8
Fall 2021 ECTS Spring ECTS
Capstone Sep1-15 Thesis 30
Deep Learning or Reinforcement Learning 8
Restricted Elective 8
Restricted Elective 8
Applied Data Science (3-week course) 6

*Taken in the fall semester in the first or second year

Restricted Electives

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

Fall Spring
12 week 12 week
Natural Language Processing Network Science
Reinforcement learning Big Data Management
Advanced Topic in AI Reinforcement learning
Deep learning Data-driven security
3 week 3 week
Data-driven e-heath
Financial Simulation

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