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    Details available from the Department of Mathematics and Statistics.

    This paper provides an overview of ideas and methods that are useful when analysing big data.

    About this paper

    Paper title Topic in Advanced Statistics
    Subject Statistics
    EFTS 0.1667
    Points 20 points
    Teaching period Not offered in 2024 (On campus)
    Domestic Tuition Fees ( NZD ) $1,240.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Students should have completed a first-year paper in statistics (STAT110, STAT115 or BSNS102) and two further papers at 200/300-level that include experience in quantitative research methods or applied statistics before enrolling in STAT442. Students should see the course co-ordinator for approval.

    Students should see the Course Co-ordinator for approval. The prerequisite conditions at second-year Statistics may not be compulsory for students majoring in Information Science because the paper content may complement the topics covered in such a major.

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


    Teaching staff

    The paper will be taught by academic staff from the University of Canterbury and the University of Otago.

    The course will be delivered by lectures using videoconferencing technology and in-person lectures on the Dunedin campus.

    Students have a local contact person/co-ordinator.

    Paper Structure


    • Sources and characteristics of big data
    • Challenges with big data
    • Data acquisition, storage and retrieval
    • Data management, cleaning and pre-processing
    • Data visualisation
    • Machine learning methods for high-dimensional data
    Teaching Arrangements

    Twelve 2-hour lectures.


    Textbooks are not required for this paper.

    Graduate Attributes Emphasised
    Communication, Information literacy, Research.
    View more information about Otago's graduate attributes.
    Learning Outcomes
    Students who successfully complete the paper will develop an ability to analyse a very large dataset and to communicate the information obtained from the analysis.


    Not offered in 2024

    Teaching method
    This paper is taught On Campus
    Learning management system
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