STKD6810 Neuro-insights: Data Science Approaches in Neuroscience II Emneplan

Emnet tar utgangspunkt i det vitenskapelige, samfunnsmessige og humanistiske grunnlaget for velfungerende helsetjenester til nytte både for pasienten og for samfunnet. Et spesielt fokus rettes mot kunnskap og ferdigheter som kan fremme respekt, empati, refleksjon, samarbeid og god kommunikasjon. I tillegg er praktisk trening i livreddende førstehjelp en del av emnet. Dette er grunnleggende ferdigheter i prehospitalt arbeid og er velegnet for utvikling av relasjonell kompetanse.

To sensorer, hvorav minst én ekstern, vurderer alle besvarelser.

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

;

Knowledge

Upon successful completion of this course, the student knows:

• theoretical and practical aspects of data management and data processing for different data types (electrophysiology, imaging, and behavior), so they can independently engage with the state-of-the art literature.
• how statistics, dimensionality reduction, signal processing, calculus, and supervised and unsupervised learning may be applied to data analysis in neuroscience research.
• 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

Upon successful completion of this course, the student can:

• apply data analysis techniques to formulate a hypothesis, collect data, preprocess it, analyze it, and reach conclusions about the data.
• plan, design and implement data analysis pipelines for the most common types of data in neuroscience.
• 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.
• identify new opportunities for organizational change including process improvements, cost reduction, or efficiency improvements.

General competence

Upon successful completion of this course the student can apply:

• data analysis principles to neuroscientific data in the context of its own research.
• methods and tools for data analysis and visualization.
• heuristics and strategies commonly used in the research field to solve data analysis problems.

The teaching methodology is oriented by Bloom's taxonomy of educational goals, namely, recollection, understanding, application, analysis, evaluation, and creativity. To promote recollection, understanding, and application, the course will consist of seminars taught by the teaching staff of OsloMet and other guests (experts in neuroscience or data science fields), coding workshops and problem solving oriented projects. Students will actively participate by implementing the full data processing pipeline from extracting the raw data to building visualizations. The pipeline and good habits will be consolidated through repetition in different modules, contexts, and data types, which is known to promote generalization of the knowledge. Organized in pairs, the students will constantly have the opportunity to recollect, explain the content to each other, and justify their work, as well as to provide feedback to their partners. With the intent to prepare the students to go beyond the methods taught in the course, once per module, the participants will read relevant papers in neuroscience, and discuss how to implement their analysis.

The students will have an assignment at the end of each module (4 in total) to consolidate the material learned in that week. All assignments need to be submitted in order to have access to the exam and to pass the course.

Final examination:

• An individual oral presentation, which counts for 40% of the final mark
• An individual project described in a final 3,000 to 5,000 word report.which counts for 60% of the final mark.

Each exam must be assessed to E or better for the course as a whole to be given a final grade.

The oral presentation cannot be appealed.

Muntlig eksamen i gruppe, inntil 40 minutter. Gruppestørrelse: inntil syv studenter. Eksamen består av fremlegg/presentasjon (ca. 20-30 min.) med påfølgende eksaminering (ca. 10-20 min.) Studentene får utdelt tema for fremlegg én uke før eksamen.

Alle.

Bestått/ ikke bestått.

The course has 5 ECTS of overlapping content towards "Neuro-insights: Data Science Approaches in Neuroscience I" (STKD6800).