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Predictive Analytics (BISY90016)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Year of offer2019
Subject levelGraduate coursework
Subject codeBISY90016
Availability(Quotas apply)
FeesSubject EFTSL, Level, Discipline & Census Date

Predicting key business variables has become increasingly important, as it drives both objective decision-making and improved profitability within organisations. This subject covers the main methods used to predict business variables, based on historical data. These include traditional regression, time series analysis, forecasting models, survival analysis, data mining, support vector machines and sentiment analysis. Throughout the subject, the focus will be on understanding how these methods are applied in various business problems, and identifying which predictive approach is the most appropriate to use, given a specific context. The importance of benchmarking different methodologies, as well as the use of prediction in decision-making frameworks, will also be emphasised.

Eligibility and requirements


Enrolled in one of the Master of Business Administration courses and completion of 112.5 credit points of core subjects in the courses and 70% or above in the subject Data Analysis



Non-allowed subjects


Core participation requirements

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home


Additional details

Individual assignment (1,000 words)Week 520%Syndicate assignment (equivalent to individual 1,000 words assessment) Week 830%Final examination (3 hours, hurdle requirement) End of term 50%

Quotas apply to this subject

Dates & times

  • November
    Mode of deliveryOn Campus — Parkville
    Contact hours30 hours
    Total time commitment150 hours
    Pre teaching start date10 November 2019
    Pre teaching requirementsStudents are required to complete approximately 10 hours of reading materials to prepare for the subject during pre-teaching period
    Teaching period17 November 2019 to 23 November 2019
    Last self-enrol date11 November 2019
    Census date18 November 2019
    Last date to withdraw without fail22 November 2019
    Assessment period ends 1 December 2019

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 30 August 2019