LEO5314 Machine learning and data analytics
Faculty of Business and Economics
LEO5314 Machine learning and data analytics is a level 5, 6-credit-point, postgraduate unit from the Faculty of Business and Economics. It isn't offered in 2026. It needs LEO5310.
- Credit points
- 6
- Offered in 2026
- Other periods
- Monash Online
- Assessment
- No exam
- 2 tasks
- Workload
- 144 hours
- per semester
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before LEO5314
Prerequisites
Pass these before you enrol.
After LEO5314
No unit lists LEO5314 as a prerequisite in the 2026 handbook.
Enrolment rules
You must be enrolled in course B6028 or B6059, or be granted permission by the Chief Examiner to undertake this unit.
Overview
Machine learning enables organisations to unlock insights and make informed predictions by leveraging predictive, diagnostic and prescriptive analysis. This unit covers the fundamentals of machine learning algorithms to analyse complex data with a specific focus on the use of R programming. The unit equips you with practical skills in supervised and unsupervised learning, including regression models, kNN, decision trees, random forests and clustering analysis. Examples of contemporary approaches to machine learning will be included through case across business disciplines.
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Teaching period 3 | Monash Online | Mo |
Assessment
- Written50%
- Written50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
understand the principles of machine learning methods for diverse datasets
- 2
demonstrate ability in the use of machine learning algorithms for a range of business purposes
- 3
apply supervised learning for predictive analysis to support decision making
- 4
apply unsupervised learning for diagnostic analysis and prescriptive analysis to identify patterns and trends to derive actionable insights.
Workload and teaching
- Teaching approachCase-based teaching
- Teaching approachResearch activities
- Teaching approachActive learning
- Teaching approachProblem-based learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit includes case-based teaching, where you apply your knowledge and engage in analytical and reflective thinking to solve complex contextual scenarios. Activities are often designed so that there is not one clear answer, but students need to work together to examine, analyse and make decisions to resolve the situation.
This unit allows you to develop their research skills by engaging in structured inquiry using a systematic approach and discipline-specific methodologies.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
Contacts
- Chief Examiners
- Hao Chen
Common questions
What are the prerequisites for LEO5314?
You need LEO5310 before you enrol. Enrolment rules also apply.
When is LEO5314 offered?
LEO5314 has no offerings listed in the 2026 handbook.
How much work is LEO5314?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does LEO5314 have an exam?
No. LEO5314 has 2 assessment tasks and no exam.