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Course Outlines

MATHS 7027OL - Mathematical Foundations of Data Science

Online - Online Teaching 5 - 2024

This course introduces fundamental mathematical concepts relevant to computer science and provides a basis for further postgraduate study in data science, statistical machine learning, and cybersecurity. Topics covered are probability: sets, counting, probability axioms, Bayes theorem; optimisation and calculus: differentiation, integration, functions of several variables, series approximations; linear algebra: vector and matrices, matrix algebra, vector spaces; discrete mathematics and statistics: linear regression, linear least squares, regularisation. Applications of the theory to data science and machine learning will be developed.

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  • General Course Information
    Course Details
    Course Code MATHS 7027OL
    Course Mathematical Foundations of Data Science
    Coordinating Unit Mathematical Sciences
    Term Online Teaching 5
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites Carousel 1 Courses: COMP SCI 7212OL, COMP SCI 7210OL, DATA 7201OL and DATA 7202OL or MATHS 7203OL
    Assumed Knowledge SACE Stage 2 Mathematical Methods
    Restrictions Graduate Diploma in Data Science (Applied) OL OR Master of Data Science (Applied) OL Only
    Assessment Exam, ongoing assessment
    Course Staff

    Course Coordinator: Dr Daniel Stevenson

    Course Timetable

    The full timetable of all activities for this course can be accessed from .

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    		 1

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    		 3

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    		 2,4
  • Learning Resources
    Online Learning
    All resources for the course are contained within the MyUni Canvas pages.
  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Online courses have an expectation of 25+ hours work per week.
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    1: Quiz component: Due Start of weeks 2,3,4,5,6 Tuesday 11:59pm; 20%
    2: Assignment: Due End of Week 4; 30%
    3: Timed Assignment: Due End of Week 6; 50%


    Assessment Detail

    No information currently available.

    Submission
    All assessments are to be submitted through MyUni.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49