Examination arrangement

Course content

Data science comprises a significant variety of methods and technologies for mining, aggregating and analzying data. The aim of most courses in AI is understanding the finer details of the methodological aspects. This course, however, is aimed at developing knowledge of, skills in and competance of the most used methods. The course exploits the fact that very many business-relevant, practical problems applications of data science do not require the most sophisticated methods. Many practical problems from private and public organizations may be tackled with known methods readily available in commodified technologies in the form of open source.

Learning outcome

Knowledge: The candidate will establish deep knowledge about the variety of data science methods, technologies and algorithms. Skills: The candidate will gain solid skills in setting up and configuring data science tools. The candidate will develop good skills in identifying what methods are appropriate for what type of problems. The candidate will also develop good skills in preparing and pre-processing data for input. Competence: The candidate will estabslish a compentence in the conditions for the application of selected data science methods to address business and strategica challenges.

Learning methods and activities

The course consists of lectures and project work. The students need to complete a group-based project that is to be presented as well as an individual assignment. In the group project the students go through a realistic, problem-oriented analytics of the data. The group project is develop practical skills in configuring the relevant tools/ technologies, pre-processing of data and conducting the analytics. The individual assignment discusses the group project in light of relevant literature from the courses curriculum. The group-based project counts for the final grade. The presentation and the individual assignment are mandatory but do not count towards the final grade of the evaluation.

The course may be lectured in English if there are students not knowledgeable in Norwegian.

Compulsory assignments

• Individual report
• Presentation of group work

Further on evaluation

The group report counts 100% and a presentation is mandatory in order to pass the course.

An individual report is mandatory and needs to be passed in order to hand in the group report.

There will be no re-sit.

Recommended previous knowledge

None

Required previous knowledge

None

Course materials

Provided at beginning of semester