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.

Progression level in relation to degree requirements

This is an elective course.

Course contents

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.

Learning outcomes

Knowledge and understanding

On completing the course, the student shall:

• demonstrate the ability to explain important concepts in the field of data science and data extraction;
• demonstrate the ability to describe important steps in digital methods for handling, processing and visualizing data; and
• demonstrate insight into the application of and the challenges and opportunities presented by data analysis in practical problem-solving situations related to business development and social innovation.

Competence and skills

On completing the course, the student shall:

• demonstrate the ability to apply digital methods to large amounts of data in order to identify and analyze relevant information based on a formulated problem, as well as to present the process and results obtained.

Judgment and approach

On completing the course, the student shall:

• demonstrate the ability to evaluate the choice of relevant digital methods for various problems related to business development and social innovation; and
• demonstrate the ability to assess how the quality, amount and type of data steers the choice of digital method.

Learning activities

Problem-based activities based on practical cases; supported by lectures, workshops. seminars and laboratory work. Practical cases should preferably be prepared in collaboration with social enterprises and described in an individual report to be presented orally.

Assessment

Laboratory work (mini-projects, exercises) 5 HE credits

Seminars, theoretical concepts, 2 HE credits

Seminars, practical applications, 2 HE credits

Final individual report, case study, 5 HE credits

Final presentation, case study, 1 HE credit

In order to achieve a passing grade for laboratory work and seminars, the student is required to participate actively in and achieve a passing grade in each individual course component

In order to achieve a passing grade for the entire course, the student is required to achieve a passing grade for all laboratory work and seminars and a minimum of an E grade for goal attainment in all of the course’s intended learning outcomes. Goal attainment for each outcome is assessed based on the individual report and oral presentation with the aid of an assessment matrix, which will be provided by the course coordinator.

Course literature

• Provost, F. & Fawcett, T. (2015). Data Science for Business: What You Need to Know About Data Mining and Data-analytic Thinking. (1st Edition) Sebastopol, Calif.: O'Reilly.
• Salganik, M. J. (2017). Bit by Bit: Social Research in the Digital Age. http://doi.org/10.1111/rssa.12375

Additionally, a collection of scientific articles will be used.

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)

Interim rules

If a course has been discontinued or its content has altered significantly, students shall, for a period of one year after such changes have been implemented, be offered two opportunities for retakes based on the course syllabus that applied at the time of registration.

Additional information

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format.