Students

BUSA8001 – Applied Predictive Analytics

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
George Milunovich
george.milunovich@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
(BUSA6004 and BUSA8000) or (Admission to MActPrac or GradCertResMQBS or GradDipResMQBS)
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This advanced-level unit builds on prior training in data analytics and quantitative methods. Students will prepare and analyse structured datasets using Python and its associated open-source libraries, apply data visualisation techniques for exploratory analysis, and develop predictive models for various business applications including credit risk, housing prices, and asset valuation. The unit further introduces unsupervised learning methods such as clustering, and text analytics techniques for sentiment and topic modelling.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

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

  • ULO1: Critically evaluate data requirements to generate predictive models that support decision-making.
  • ULO2: Critically evaluate and apply advanced predictive analytics techniques to solve complex business problems.
  • ULO3: Devise and implement optimised computer algorithms to develop predictive analytics with efficiency and accuracy.
  • ULO4: Demonstrate an advanced understanding of contemporary analytical techniques and their application in solving problems.
  • ULO5: Collaborate effectively in diverse teams to develop predictive models for real-world business applications.

General Assessment Information

Late Submission Penalties

If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0.

Example 1 (out of 100):

  • If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

Example 2 (out of 30):

  • If you score 27/30 but submit 20 hours late, you will lose 1.5 marks and receive 25.5/30.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Professional practice: Modelling analysis 2 30% No Week 13 Group No Open AI
Formal examination 40% No University Examination Period Individual No Observed
Professional practice: Modelling analysis 1 30% No 02/04/2026 Individual Yes Open AI

Professional practice: Modelling analysis 2

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 25 hours
Due: Week 13
Weighting: 30%
Groupwork/Individual: Group
Short extension 3: No
AI Approach: Open AI

The purpose of this assessment is for you work with your peers to develop and apply your skills in addressing complex analytics tasks using Python, aligned with industry standards.

You will solve a business problem through data-driven analysis developing predictive or analytical models, implementing solutions using Python and presentation recommendations and rationale.

Skills in focus:

  • Digital skills
  • Critical thinking and problem-solving
  • Discipline knowledge
  • Collaboration skills
  • Work readiness 

Deliverable(s): Modelling task

Group assessment
On successful completion you will be able to:
  • Critically evaluate data requirements to generate predictive models that support decision-making.
  • Critically evaluate and apply advanced predictive analytics techniques to solve complex business problems.
  • Devise and implement optimised computer algorithms to develop predictive analytics with efficiency and accuracy.
  • Demonstrate an advanced understanding of contemporary analytical techniques and their application in solving problems.
  • Collaborate effectively in diverse teams to develop predictive models for real-world business applications.

Formal examination

Assessment Type 1: Examination
Indicative Time on Task 2: 30 hours
Due: University Examination Period
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to formally demonstrate the expertise you have gained in this unit.

You will participate in a 2-hour exam with 10 minutes reading time, held during the University Examination period. Important information about the exam will be made available on the unit iLearn page. You should also review the MQ Exams website for general tips.

Deliverable(s): Formal exam

Individual assessment
On successful completion you will be able to:
  • Critically evaluate data requirements to generate predictive models that support decision-making.
  • Critically evaluate and apply advanced predictive analytics techniques to solve complex business problems.
  • Devise and implement optimised computer algorithms to develop predictive analytics with efficiency and accuracy.
  • Demonstrate an advanced understanding of contemporary analytical techniques and their application in solving problems.

Professional practice: Modelling analysis 1

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 15 hours
Due: 02/04/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open AI

The purpose of this assessment is for you to develop and demonstrate your ability to solve complex predictive modelling problems using Python, aligned with industry practices.

You will work with a messy dataset to clean the data, build and implement predictive models using Python, and generate accurate forecasts. You will also interpret and discuss your results.

Skills in focus:

  • Discipline knowledge
  • Digital skills
  • Critical thinking and problem solving
  • Work readiness

Deliverable(s): Modelling task

Individual assessment
On successful completion you will be able to:
  • Critically evaluate data requirements to generate predictive models that support decision-making.
  • Critically evaluate and apply advanced predictive analytics techniques to solve complex business problems.
  • Devise and implement optimised computer algorithms to develop predictive analytics with efficiency and accuracy.
  • Demonstrate an advanced understanding of contemporary analytical techniques and their application in solving problems.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Classes

  • Number and length of classes: 3 hours of face-to-face teaching per week, consisting of:

    • One 1.5-hour lecture

    • One 1.5-hour computer lab/tutorial

Recommended Textbook

  • Python Machine Learning (Third Edition) by Sebastian Raschka and Vahid Mirjalili

Technology Used and Required

  • You will need a decent-quality laptop. A tablet is not sufficient.

  • Students will use Python and JupyterLab during lectures and lab sessions.

Unit Schedule

See iLearn for details.

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Unit information based on version 2026.04 of the Handbook