Advanced Data Science

    Unit code: NIT3251 | 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)


    Advanced data science unit introduces deep understanding on data exploration, data visualization, statistical analysis, advanced machine learning and deep learning techniques. This unit allows students to gain hands-on experience with Python, Jupyter Notebooks, and open-ended investigations of real-world practical dataset and novel data science problems. Students will gain better understanding of the characteristics of different algorithms, tools, frameworks, and technologies and know how to solve authentic analytical problems and measure model performance. This unit will enable the students to build portfolio worthy projects and to explore the essential ethical, and social issues in data science.

    Learning Outcomes

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

    1. Experiment and evaluate various advanced machine learning and deep learning algorithms to analyse real-world problems;
    2. Critically reflect on advanced data analytics methods and their importance to support decision making;
    3. Conceptualise critical thinking and problem solving on novel data science problems; and
    4. Analyse real-world business problems by applying appropriate advanced data science methods and techniques with considerations of data privacy, human factors and professional ethics.


    For Melbourne campuses

    Assessment type: Test
    Grade: 20%
    Open book online test
    Assessment type: Project
    Grade: 40%
    Applied Learning project on real-world problem - code, and report (individual report)
    Assessment type: Project
    Grade: 40%
    Case study and algorithm implementation - code, and report (group report)

    Required reading

    Required 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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