Big Data in Biomedical Sciences

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Big Data in Biomedical Sciences

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Biomedical Sciences

  • Language Level

    Taught In English

  • Course Level Recommendations

    Lower

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    COURSE OBJECTIVE
    Students will learn how various types of biomedical data are acquired, how they can be used in fundamental and translational research, and in which format they are usually stored.
    Students will have knowledge of the FAIR principles (Findable, Accessible, Interoperable, Reusable) for data storage.
    Student will understand the value of accurate and understandable metadata and feel responsible for good 'data stewardship'.
    Students oversee the potential and current challenges of big data applications in personalized medicine, genetics, neuroscience,
    connectomics and metagenomics.
    Students have hands-on experience with programming algorithms for big data mining or other bioinformatics analyses.
    Students have sufficient insight into bioinformatics workflows, possibilities and limitations to effectively communicate with bioinformaticians.
    Students can independently collect up-to-date knowledge on the abovetopics ('metalearning'). This is important because bioinformatics is a
    fast-changing discipline.

    COURSE CONTENT
    This elective addresses important concepts in bioinformatics and big data mining, with powerful applications in biomedical sciences. Lectures and practical assignments provide theory and hands-on experience in fast moving fields of personalized medicine, genetics, neuroscience, connectomics and metagenomics.

    TEACHING METHODS
    Each week the course will offer lectures (35 h for the entire course) and a practical computer assignment (16 h).
    Expect to spend approximately 100 h on self-study.

    TYPE OF ASSESSMENT
    The knowledge in the lectures will be tested by a written exam with open questions held at the end of the course.
    Each practical assignment will be evaluated individually by the teachers. The criteria for grading will be made accessible in the form
    of 'Rubrics' in Canvas.
    The final grade will be calculated as 60% (final exam) and 40% (assignments).
    To pass the course, both the exam and assignments need to be graded 5.5 or higher.

     

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Courses and course hours of instruction are subject to change.

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