DATA 7301OL - Applications of Data Science

Online - Online Teaching 4 - 2024

A practical introduction to data modelling, analysis and prediction using contemporary software packages. An overview of common techniques and their implementation in software libraries. Selection of tools and techniques that are appropriate for different types and scale of data. Validation and interpretation of process outputs.

  • General Course Information
    Course Details
    Course Code DATA 7301OL
    Course Applications of Data Science
    Coordinating Unit Computer Science
    Term Online Teaching 4
    Level Postgraduate Coursework
    Location/s Online
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange N
    Prerequisites Carousel 1 Courses: COMP SCI 7212OL, COMP SCI 7210OL, DATA 7201OL & DATA 7202OL or MATHS 7203OL
    Assumed Knowledge DATA 7201OL
    Restrictions Graduate Diploma in Data Science (Applied) OL OR Master of Data Science (Applied) OL Only
    Assessment Assignment and/or exam
    Course Staff

    Course Coordinator: Nordiana Shah

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    Upon completion of this course/subject, students will be able to:
    1. Generate unsupervised and supervised methods to gain Information to bring about solutions
    2. Formulate a range of regression methods as part of supervised learning
    3. Formulate a range of classification methods as part of supervised learning
    4. Construct tree methods to map non-linear relationships as part of supervised learning
    5. Investigate the role of machine learning method in support of all the other techniques
    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, 2, 3, 4, 5

    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.

    1, 2, 4

    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.

    4, 5

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    1, 2, 3, 4, 5

    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    All resources are provided through the MyUni platform.
    Recommended Resources
    All resources are provided through the MyUni platform.
    Online Learning
    All resources are provided through the MyUni platform.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course is an on-line course with weekly Zoom tutorials and regular discussions in the course discussion board system.

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

    Students are expected to allocate roughly 20-25 hours per week to go through content, attend activities, and undertake assignments.
    Learning Activities Summary
    This course provides a practical introduction to data modelling, analysis and prediction using contemporary software packages, presented through the lens of case studies. You will also be given an overview of common data science techniques and their implementation in software libraries.

    Students will have assigned readings, quizzes, assignments, videos, and guided discussion, as well as pieces of assessment. Each week will cover a different theme:
    1. Unsupervised Methods
    2. Supervised Methods
    3. Regression Methods
    4. Classification Methods
    5. Tree Methods
    6. Machine Learning
    Specific Course Requirements
    Not applicable.
  • 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
    Students will be required to under three different assessments. Assessment 1 is based on 5 weekly quizzes, Assessment 2 is a case study, and Assessment 3 is a longer report, due at the end of the course.
    Assessment Detail
    Assessment Name Due Weighting Course Learning Outcomes Related Weeks
    Assessment 1 - Quiz    Weekly
    30% (5% each) 1-5 Each Week
    Assessment 2 - Case Study Evaluation End of Week 3 30%  1 and 2 Weeks 1-3
    Assessment 3 – Case Study Report End of Week 6 40% 3, 4 and 5 Weeks 1-6
    All submission will be through assignment gateways on the MyUni system.
    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 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support

    Counselling for Fully Online Postgraduate Students

    Fully online students can access counselling services here:

    Phone: 1800 512 155 (24/7) 

    SMS service: 0439 449 876 (24/7) 


    Go to the Study Smart Hub to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)

  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.