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    Topics include an overview of genetics and molecular biology; genetic, genomic, and proteomic technologies; analysis of large data sets; incorporation of biological information into the statistical analysis process.

    The analysis of large data sets is becoming increasingly important in many areas. The techniques covered in this paper will be applicable to a wide range of data types, including non-biological data. Exposure to other disciplines (in this case biomedical science) is a must for any applied statistician. Interacting with students from other fields is stimulating and will help you appreciate your statistical skills.

    About this paper

    Paper title Data Analysis for Bioinformatics
    Subject Statistics
    EFTS 0.1667
    Points 20 points
    Teaching period Semester 1 (On campus)
    Domestic Tuition Fees ( NZD ) $1,240.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    This paper is open to fourth-year students from the Biological and Medical Sciences, Mathematics and Statistics, and Computer Science. As long as you have skills in one of these areas, any remaining gaps will be filled in during the paper. Experience with R will certainly help.

    Enrolments for this paper require departmental permission. View more information about departmental permission.

    Teaching staff

    Professor Mik Black (Department of Biochemistry)

    Paper Structure
    Main topics:
    • Overview of genetics and molecular biology
    • Introduction to genomics technologies
    • Methods for the statistical analysis of large data sets
    • Application of standard statistical methods
    • Introduction to new purpose-built methods
    • Incorporation of biological information into the statistical analysis process
    Teaching Arrangements
    One 2-hour session per week.
    Textbooks are not required for this paper.
    Graduate Attributes Emphasised
    Communication, Critical thinking.
    View more information about Otago's graduate attributes.
    Learning Outcomes
    Students who successfully complete the paper will demonstrate in-depth understanding of the central concepts and theories.


    Semester 1

    Teaching method
    This paper is taught On Campus
    Learning management system
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