Data Science - Master of Science Degree Programme, Part-Time

A master's degree in Data Science makes you eligible for the most demanding and interesting work tasks within data analysis, smart solutions (such as smart cities, smart energy), and digitalization. The Master’s programme in Data Science is an international programme where the teaching language is English.

Programme content, structure and composition

The University of Stavanger offers a master's programme aimed at students who have completed a 3-year engineering degree or similar with necessary background in programming and computer science (at least 20 ECTS). The master's degree in Data Science comprises 120 ECTS. In the part-time programme you can spread these courses over 4 years, taking one to two courses every semester.

The programme has practical courses that build on mathematics, statistics, and basic computer science courses from the bachelor's degree. The programme contains advanced statistical topics, processing of large datasets, Cloud solutions, machine learning, and data mining.

The programme offers a variety of study and learning activities, from traditional lecture series and exercises, project work, self-study and laboratory teaching to introduction and practice in the use of modern software. Which teaching forms are used varies between different subjects and topics.

The following is described in the individual course description:

• Forms of work and teaching

• Evaluation Forms

• Syllabus

• Assessment

Using technology for a better world

The United Nations' Sustainable Development Goals (SDGs) serve as the global roadmap for eradicating poverty, combating inequality, and addressing climate change by 2030. Through the master’s programme in Data Science, you can acquire the skills to directly contribute to achieving these goals. Information and Communication Technology (ICT) can be utilized to assist in all the SDGs.

Here are some examples based on our work at IDE:

Researchers at IDE are involved in analyzing image data to aid doctors in diagnosing diseases or studying heart data to detect cardiovascular conditions before they manifest. This aligns with the third SDG, which focuses on ensuring good health and well-being.

With the increasing power and climate crises, it has become crucial to generate as much energy as possible. By installing solar panels on rooftops and implementing small-scale hydropower plants on individual farms, a smarter grid system needs to be developed to manage electricity more efficiently. IDE has undertaken multiple master's and PhD projects concerning smart grids, making it a potential avenue for those pursuing a master's in data science. This work directly contributes to SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), and SDG 13 (climate action).

The university aims to offer all the study programs as planned but must make reservations about sufficient resources and / or students to complete the offer. Over time, it will be natural for the academic content and offering of courses to change due to the general developments in the field of study, the use of technology and changes in society at large.

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Learning outcomes

After having completed the master’s programme in Data Science, the student shall have acquired the following learning outcomes, in terms of knowledge, skills and general competences:

Knowledge

K1: Advanced knowledge within Data Science, which includes data processing, machine learning, data extraction, statistics and typical programming languages for the area, including: Pythonand R.

K2: Specialised insight into data analysis.

K3: In-depth knowledge of scientific theory and methods in Data Science.

K4: Apply knowledge about algorithms for statistical analysis, machine learning or data extraction in new areas within data science.

K5: Analyse professional issues based on the fourth science paradigm, 4Vs of big data (volume, velocity, variety, and variability), data-driven approach, CRISP-DM (cross-industry standard process for data mining).

Skills

S1: Analyse and relate critically to different sources of information, datasets and data processes; and apply these to structure and formulate data-driven reasoning.

S2: Analyse existing theories, methods and interpretations within the subject area and work independently in applying and evaluating different storage and data processing technologies.

S3: Use CRISP-DM and scientific methods to develop data analysis programs in an independent way.

S4: Conduct independent, limited data collection, analysis and evaluation according to established engineering principles in accordance with current research ethical standards.

General Competence

G1: Analyse relevant ethical issues arising from data usage and data recovery.

G2: Apply their knowledge and skills in new areas to carry out advanced tasks and projects related to data processing, data analysis and optimisation.

G3: Communicate results of comprehensive data analysis and development work, and master Data Science expressions.

G4: Communicate on issues, analyses and conclusions related to data-driven research and development, both with specialists and to the general public.

G5: Contribute to new ideas and innovation processes by introducing data-driven approaches, comprehensive data analysis and development work, and master Data Science expressions.

Career prospects

With a master’s degree in Data Science, you can get a position in almost all industries. Some examples of businesses where you can find employment are consulting companies, telecommunications companies, energy related businesses, hospitals, and other public agencies. Specialisation in Data Science provides a basis for work in data analysis and development of data processing systems for the whole data lifecycle. It builds knowledge and skills in advanced statistics, data mining, machine learning and processing of large data volumes.

Completed master’s degree in Applied Data Science provides the basis for admission the PhD programme in Information technology, mathematics and physics.

Course assessment

Schemes for quality assurance and evaluation of studies are stipulated in the Quality system for education