Neuro-insights: Data Science Approaches in Neuroscience

Master's course - Continuing education

This course will introduce you to data science and big data applied to neuroscience research.

  • Semester start

    It is not decided when this course will run again.

    Information about semester start and other information for new students.

  • Admission requirements

    To apply for this programme you must 

    It is recommended  that you have knowledge in algebra, linear algebra, statistics, calculus, neurophysiology, and any programming language, especially Python.

  • How to apply

    You apply via Søknadsweb ( with study code 215 3434.

    Application deadlines:

    More information about admission (in Norwegian).

  • Content

    OsloMet recommend this course if you are a PhD or a Master student involved in neuroscience research projects.

    Because most young researchers in life and health sciences do not have a solid quantitative background, they often face difficulties when analysing data independently. This represents a major drawback in research. 

    This course provides you with a comprehensive introduction to data science and big data applied to neuroscience research.

    The course will train you in state-of-the-art techniques in data analysis and machine learning. This will enable you to interact independently with the data and draw insights from them. 

    The modules in the course are organized so that you have the opportunity to learn how to handle the most common data types (e.g. EEG, calcium imaging). 

    The course gives special attention to field-tested data management protocols, as they are critical for a fast transition from data acquisition to knowledge generation. 

    Read more about what learning outcomes you can expect after taking this course in the course plan for Neuro-insights: Data Science Approaches in Neuroscience 10 ECTS (

  • Teaching methods

    In this hands-on course you will

    • learn from implementing the analysis with close supervision
    • focus on case studies using data from real experiments
    • develop understanding through constant presentation of your work and dialectical reflection over your choices, results, and interpretations

    Bloom's taxonomy of educational goals, namely, recollection, understanding, application, analysis, evaluation, and creativity has oriented the teaching methodology.

    To promote recollection, understanding and application, the course will consist of 

    • seminars taught by the teaching staff of OsloMet and other experts in neuroscience or data science fields
    • coding workshops
    • problem solving oriented projects. 

    You will actively participate by implementing the full data processing pipeline from extracting the raw data to building visualizations. 

    You will consolidate pipeline and good habits through repetition in different modules, contexts, and data types, which is known to promote generalization of the knowledge. 

    Organized in pairs, you and another student will have the opportunity to recollect, explain the content to each other, and justify your work, as well as provide feedback to your partner. 

    With the intent to prepare you to go beyond the methods taught in the course, once per module, you can read relevant papers in neuroscience, and discuss how to implement your analysis. 

    The course has approximately 20 workshops and seminars.

  • Course description

  • Costs

  • Exam and assessment

    If you take the course with 10 credits:

    • An individual oral presentation, which counts for 40% of the final mark for the 10 ECTS.
    • An individual final 3.000 to 5.000 words report, which counts for 60% of the final mark.

Academic coordinator

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Questions about this course?

You can contact us by e-mail if you have questions about this course.