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    The techniques of data science used to produce predictive and adaptive decision support techniques with particular emphasis on prediction, optimisation and search methods.

    All businesses make decisions based on an understanding of their historical, current and predicted environments. Adaptive Business Intelligence introduces methods for supporting robust decision making in a business context, leading to graduates with the confidence to visualise, understand and predict relevant aspects of a business process. Given the future of improving business processes is to understand what has happened and what will happen, INFO 424 delivers some fundamental skills that all business graduates need.

    About this paper

    Paper title Adaptive Business Intelligence
    Subject Information Science
    EFTS 0.1667
    Points 20 points
    Teaching period(s) Semester 1 (Distance learning)
    Semester 1 (On campus)
    Domestic Tuition Fees ( NZD ) $1,448.79
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    BSNS 102 or STAT 110
    INFO 304, INFO 324
    Limited to
    MA, MBus, MCom, MSc, MAppSc, MBusDataSc, BA(Hons), BAppSc(Hons), BCom(Hons), BSc(Hons), PGDipAppSc , PGDipArts, PGDipCom, PGDipSci, PGCertAppSc
    Teaching staff
    Convenor and Lecturer: Associate Professor Peter Whigham
    Paper Structure
    The paper covers three main themes:
    • Visualisation of data
    • Model building for prediction and analyses
    • Business applications

    "An Introduction to Statistical Learning", by G.James, D. Witten, T. Hastie & R. Tibshirani. This textbook is available online through the library.

    Course outline
    View the most recent Course Outline
    Graduate Attributes Emphasised
    Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy, Self-motivation.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    Students who successfully complete this paper will:

    • Have the ability to identify the activities of prediction, optimisation and adaption that exist within a decision-making context
    • Be confident in applying methods to understand and critically assess these activities
    • Have the technical skills to handle data processing, visualisation, modelling and interpretation


    Semester 1

    Teaching method
    This paper is taught through Distance Learning
    Learning management system

    Semester 1

    Teaching method
    This paper is taught On Campus
    Learning management system

    Computer Lab

    Stream Days Times Weeks
    A1 Tuesday 11:00-12:50 9-13, 15-22


    Stream Days Times Weeks
    A1 Monday 12:00-12:50 9-13, 15-22
    Tuesday 09:00-09:50 9-13, 15-22


    Stream Days Times Weeks
    A1 Wednesday 15:00-15:50 9-13, 15-22
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