Data Science - Master of Science Degree Programme

A master's degree in Applied 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 digitization.

Programme content, structure and composition

After the student is admitted to the 2-years master programme in Applied Data Science, the student must take a test in programing and system administration. If the test is not passed, University of Stavanger offers and encourages the student to take a preparatory summer course in programing and system administration. A project report has to be delivered after the summer course is finished. The purpose of the summer course is to prepare the student for the master's programme in best possible way.

The preparatory course takes place in the summer before the start of the first semester and includes both online and on-campus activities. Details of the course, including schedule, will be available on the university website in January 2019.

The University of Stavanger does not consider it necessary to offer the preparatory summer course for students who already have passed the following courses at University of Stavanger:

• 10 ECTS in programming and a minimum of 5 ECTS in operating systems

All teaching is conducted in English. The study programme consists of practical courses (that elaborate and extend mathematical- and natural science- foundation from the bachelor's program), technical specialization courses, elective course and a master thesis. The two-year master's programme in Applied Data Science comprises 120 ECTS points.

The study programme has practical courses that build on mathematics, statistics and fundamental programming from the bachelor's program in engineering or science. The study programme consists of advanced statistic and algorithm courses, machine learning and databases. Students can further specialize in information retrieval, data mining and theoretical statistics.

Learning outcome

A candidate with a completed 2-year Master's degree in Applied Data Science shall have the following total learning outcomes defined in terms of knowledge, skills and general competences:

Knowledge

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

K2: Specialized 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 comprehensive independent 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.

Career prospects

Researchers and developers in Data Science are indispensable in almost all industries. Some examples of businesses where they find employment are: consulting companies, telecommunications companies, oil-related businesses, hospitals and other public agencies. We encounter digital technology everywhere, and researchers and developers in Data Science are crucial in making information society a reality.

Completed master's degree in Applied Data Science provides the basis for admission to PhD studies within information technology, mathematics and physics.

Course assessment

All courses in this study programme will be evaluated according to the Faculty's guidelines.