Using big data in a master of applied statistics unit

Ayse Aysin Bilgin*, Peter Howley

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


    There have been various discussions in the academic community, especially by Statistics Educators about Big Data in various fora. These include online discussion forums, round table discussions (such as World Statistics Congress), and during conference presentations (such as IASE satellite(s), IASE round table(s) and OZCOTS). Is it statistics or computing or both? However one defines it, Big Data is valued by industry, governments, and NGOs alike all around the world. Recent job advertisements are increasingly desiring skills in Big Data analysis, instead of statistical skills. Therefore, it is important to prepare graduates of our courses for their future work by providing opportunities to learn how to deal with big, complicated and complex data sets. This chapter provides a case study on the inclusion of Big Data into a Master of Applied Statistics unit, called data mining. The short history of the unit, topics of study, learning outcomes, assessment tasks, and how students were included in decision making for their projects is described before presenting two case studies from students' projects.
    Original languageEnglish
    Title of host publicationBig data in education
    Subtitle of host publicationpedagogy and research
    EditorsTheodosia Prodromou
    Place of PublicationCham
    PublisherSpringer, Springer Nature
    Number of pages23
    ISBN (Electronic)9783030768416
    ISBN (Print)9783030768409, 9783030768430
    Publication statusPublished - 2021

    Publication series

    NamePolicy Implications of Research in Education
    ISSN (Print)2543-0289
    ISSN (Electronic)2543-0297


    • Statistics education research
    • Big data
    • Authentic assessments


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