BV2500 Barnevern i velferdsstaten – juridiske og sosiologiske perspektiver på arbeid i institusjoner Emneplan

None.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student has knowledge of:

• Basic theoretical and practical aspects of data management and data processing for different data types.
• How statistics, dimensionality reduction, and supervised and unsupervised learning may be applied to data analysis in investigations.
• Commonly encountered problems in data science and the most trusted strategies to solve them.
• Basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

Skills

The student can:

• Apply data analysis techniques to collect data, pre-process it, analyse it, and reach conclusions about the data.
• Identify and define a problem and craft a solution using data analytics.
• Critically assess the results as well as justify and explain the methodological choice.

General competence

The student can apply:

• Data analysis principles to data in research.
• Methods and tools for data analysis and visualization.

Teaching and learning are centred around students’ participation in classes and group activities. The course is organized in digital and/or physical gatherings of intensive tuition, with lectures, workshops, group presentations and discussions.

The following coursework requirements must have been approved in order for the student to take the exam:

• Coursework: The students need to complete four online quizzes. One at the beginning of the course, then three quizzes during the course.

The coursework is required to secure the students’ progress through the course.

All required coursework must be completed and approved by the given deadline in order for the student to take the exam. If the coursework requirement has not been approved, the student will be given one opportunity to a new submission by the given deadline.

Only personal notes written in non-digital media will be allowed during the oral exam. All support materials are allowed in the term paper.

Grade scale A-F

The exam papers are assessed by one internal and one external examiner. At least 25% of the exam papers will be assessed by two examiners. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.

The oral presentation will be assessed by one internal and one external examiner.

The course has 7,5 ECTS of overlapping content towards STKD6810 Neuro-insights: Data Science Approaches in Neuroscience II.

Det benyttes intern og ekstern sensor til sensurering.

Cecilie Basberg Neumann

Merete;Havre