Students
BUSA8001 – Applied Predictive Analytics
2025 – Session 2, In person-scheduled-weekday, North Ryde
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Nizar Hoblos
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
(BUSA6004 and BUSA8030) or (Admission to MActPrac or GradCertResBus or GradDipResBus)
|
| Corequisites |
Corequisites
|
| Co-badged status |
Co-badged status
|
| Unit description |
Unit description
This unit introduces modern machine learning methodology which is used in solving many business problems in the modern world. Topics will be chosen from a wide set of possible areas including data analytics principles such as training and test data and the bias-variance tradeoff, modern approaches to regression including shrinkage techniques, tree based models and neural networks, methods for classification and the predictive analytics workflow. Emphasis throughout the unit will be on business applications drawn from a variety of fields. |
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: Assess data requirements needed to generate good predictions.
- ULO2: Apply a number of predictive analytics techniques to a range of business problems.
- ULO3: Devise computer code required to implement predictive analytics.
- ULO4: Analyse business problems using data science methods.
- ULO5: Successfully work in teams to achieve group and organizational objectives
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Professional Practice: Modelling Analysis 1 | 30% | No | 07/09/2025 |
| Formal and Observed Learning: Exam | 40% | No | During University Exam Period |
| Professional Practice: Modelling Analysis 2 | 30% | No | 07/11/2025 |
Professional Practice: Modelling Analysis 1
Assessment Type 1: Modelling task
Indicative Time on Task 2: 15 hours
Due: 07/09/2025
Weighting: 30%
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
Deliverable: Modelling task.
Individual assessment
On successful completion you will be able to:
- Assess data requirements needed to generate good predictions.
- Apply a number of predictive analytics techniques to a range of business problems.
- Devise computer code required to implement predictive analytics.
- Analyse business problems using data science methods.
Formal and Observed Learning: Exam
Assessment Type 1: Examination
Indicative Time on Task 2: 30 hours
Due: During University Exam Period
Weighting: 40%
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: Formal exam
Individual assessment
On successful completion you will be able to:
- Assess data requirements needed to generate good predictions.
- Apply a number of predictive analytics techniques to a range of business problems.
- Devise computer code required to implement predictive analytics.
- Analyse business problems using data science methods.
Professional Practice: Modelling Analysis 2
Assessment Type 1: Modelling task
Indicative Time on Task 2: 25 hours
Due: 07/11/2025
Weighting: 30%
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
Deliverable: Modelling task.
Group assessment
On successful completion you will be able to:
- Assess data requirements needed to generate good predictions.
- Apply a number of predictive analytics techniques to a range of business problems.
- Devise computer code required to implement predictive analytics.
- Analyse business problems using data science methods.
- Successfully work in teams to achieve group and organizational objectives
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
- the Writing Centre 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
Delivery and Resources
Classes
- Number and length of classes: 3 hours face-to-face teaching per week, consisting of one 1.5 hour lecture and one 1.5 hour computer lab/tutorial.
Recommended Textbook
- Python Machine Learning (Third Edition) by Raschka and Mirjalili
Technology Used and Required
- You will need a decent quality laptop (a tablet is not sufficient)
- Students will use Python and Jupyter Lab
Unit Schedule
Will be avialable on iLearn
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:
- Academic Appeals Policy
- Academic Integrity Policy
- Academic Progression Policy
- Assessment Policy
- Fitness to Practice Procedure
- Assessment Procedure
- Complaints Resolution Procedure for Students and Members of the Public
- Special Consideration Policy
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.
- Workshops
- Chat with a WriteWISE peer writing leader
- Access StudyWISE
- Upload an assignment to Studiosity
- Complete the Academic Integrity Module
The Library provides online and face to face support to help you find and use relevant information resources.
Student Services and Support
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- IT Support
- Accessibility and disability support with study
- Mental health support
- Safety support to respond to bullying, harassment, sexual harassment and sexual assault
- Social support including information about finances, tenancy and legal issues
- Student Advocacy provides independent advice on MQ policies, procedures, and processes
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 2025.05 of the Handbook