About the course

The purpose of the course is to provide the student with an understanding of how digital methods support business development and social innovation in finding patterns and insights from a large amount of data. Based on concrete cases, the student will learn basic concepts of how to select and apply adequate digital methods for the data mining, pre-processing, classification, segmentation, grouping, modelling, visualization, evaluation and analyzation of digital data. The student will work with cases that focus on Social Innovation in order to find solutions and improvements to social problems and needs in society.

Course content

The course is comprised of the following elements:

- Basic concepts and orientation regarding digital methods and applications, with a focus on data extraction and analysis.
- The study of common types of digital extraction tasks and technologies within business development.
- The connection between data-driven business development and social innovation.
- Orientation on research design with support from digital methods.
- Programming (Python, Java, R) and the use of data visualization applications for working with data extraction and analysis.

Syllabus and course literature

You can find a list of literature in the syllabus, along with other details about the course.

Entry requirements and selection

Entry requirements

30 credits Informatics of which at least 7.5 credits in programming, or 30 credits Computer and Information Science of which at least 7.5 credits in programming, and English 6.


University credits completed 100%

Course evaluation

At the end of the course, all students will be offered the opportunity to submit written comments on the course. A compilation of these comments and any remarks from the course coordinator will be discussed with students/course representatives at a course evaluation/programme committee meeting. The compilation will be made available on the department network. (HF 1:14)


For more information about the education: