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

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 has been admitted to the master's programme in Applied Data Science, part-time over 4 years, the student must take a test in programming and system administration. If the student does not pass the test, UiS will offer and encourage the student to complete a preparatory summer course in programming and system administration. The purpose of the course is that the students should be best prepared for the master's programme. The course takes place in early August before the regular semester starts.

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

• 10 ECTS in programming and at least 5 ECTS in operating systems

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. The master's programme in Applied Data Science, part-time over 4 years, comprises 120 ECTS.

This part-time study can be taken in combination with work or other activities for those who live in the region and can follow the educational plan provided. Courses are taken together with full-time students who complete the degree in two years, but as a part-time student you will have fewer courses per semester, distributed over four years. The part-time study takes place during the day and most of the courses are based on laboratory work and project work in groups with compulsory attendance. Lectures are usually not streamed, but books and other materials cover the syllabus. You will usually need 1-2 days per week (depending on the semester) to follow mandatory activities.

Learning outcomes

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 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.

Career prospects

With a Master in Applied 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.

Course assessment

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