Study programme description for students

Data Science - Master

Facts

Studyprogram code M-APPDAT

Credits (ECTS) 120

Level Master's degree (2 years)

Leads to degree Master of Data Science (datavitenskap)

Full-/Part-time Full-time

Duration 4 semesters

Undergraduate No

Language of instruction English

A master's degree in Data Science makes you eligible for the most demanding and interesting work 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.

Objectives, content, and organisation of the study programme

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 two-year master's degree in Data Science comprises 120 ECTS.

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 dataset, 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.

Details on teaching and learning methods, required literature, evaluation methods, and assessment criteria are provided in each course description.

The University of Stavanger 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. After admission to the programme, students can apply to take the programme part-time.

Learning outcome

A graduate should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

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

K2: Specialised insightinto 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

A degree in Data Science provides a basis for work with:

  • data analysis and development of data processing systems for the whole data lifecycle
  • advanced statistics
  • data mining
  • machine learning and processing of large data volumes
Graduates can qualify for admission to the PhD programme in Science and Technology with relevant specialisation at the University of Stavanger.

Course assessment

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

Studyplan with courses

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Student exchange

Schedule for the exchange3rd semester

Students can go on a study abroad experience during the 3rd semester of the master's programme in Data Science. Abroad, you must choose courses that provide an equivalent specialisation in your field of study, and these must be approved before you leave. It is also important that the courses you are going to take abroad do not overlap with courses you have already taken or will take later in your studies. It is recommended to think about your specialisation and/or your field of interest. You must choose at least one non-science/technological course equivalent to 5-10 ECTS (e.g. economics, languages, ethics, project management, green transition or similar).

More opportunities

In addition to the recommended universities listed below, UiS has a number of agreements with universities outside Europe that are applicable to all students at UiS, provided that they find a relevant subject offering. Within the Nordic region, all students can use the Nordlys and Nordtek networks.

Find out more

Contact your study adviser at the faculty if you have questions about guidance and pre-approval of courses: Sheryl Josdal

General questions about exchange:

Go to the exchange guide in the Digital student service desk

Contact information

Faculty of Science and Technology, tel 51 83 17 00, E-mail: post-tn@uis.no.

Study Adviser: Sheryl Josdal.