Predictive Analysis

    Unit code: NIT3151 | Study level: Undergraduate
    (Generally, 1 credit = 10 hours of classes and independent study.)
    Footscray Park
    NIT2151 - Fundamentals of Data Science; and
    NIT2251 - Machine Learning and Data Mining
    (Or equivalent to be determined by unit coordinator)


    Predictive Analytics is a unit that encompasses the core body of knowledge in data science. Students will learn to analyse business needs and provide predictive solutions through team work and communication. Students will develop knowledge of the fundamental processes and methods in data science and their application in predictive analytics, including data exploration; classification and clustering; time series; model validation and deployment. Further, students will test and validate their models so that they can develop appropriate predictive methods to solve real-world problems.

    Learning Outcomes

    On successful completion of this unit, students will be able to:

    1. Apply theories and methods in predictive analytics;
    2. Analyse, explore, and process real-world data sets;
    3. Adapt modern ethical and professional practice in data analytics;
    4. Propose solutions in predictive analysis by utilising state-of-the-art software and frameworks; and
    5. Present visualisations of data that communicates results of analysis to a wider audience.


    For Melbourne campuses

    Assessment type: Test
    Grade: 20%
    Online test
    Assessment type: Project
    Grade: 40%
    Group project based assessment
    Assessment type: Test
    Grade: 20%
    Scenario-based in-class problem solving
    Assessment type: Presentation
    Grade: 20%
    Individual presentation on group project

    Required reading

    Required and recommended readings will be made available on VU Collaborate.

    As part of a course

    This unit is studied as part of the following course(s):

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