Data Science Using Python
Overview
This unit aims to develop students’ conceptual and practical understanding of the field of data analytics in the contexts of real-world applications. The students will gain the understanding of how to identify and define data science relevant tasks in practical scenarios, and acquire the ability to apply given techniques and tools using Python to resolve given data analytics relevant tasks.
Requisites
15-June-2025
07-September-2025
30-November-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Design machine learning models to inform organisational decision-making
- Apply relevant algorithms to analyse data and make predictions using Python
- Test, evaluate, improve and deploy an effective machine learning model using Python
- Justify the approach taken to create machine learning models within different organisational contexts
- Examine ethical considerations within specific data science contexts
Teaching methods
Swinburne Online
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Online Directed Online Learning and Independent Learning | 15.00 | 10 weeks | 150 |
| TOTAL | 150 |
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Live Online Class | 1.50 | 8 weeks | 12 |
| Online Learning activities | 1.50 | 8 weeks | 12 |
| On-campus Workshop | 3.00 | 8 weeks | 24 |
| Unspecified Activities Independent Learning | 12.75 | 8 weeks | 102 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment 1 | Individual/Group | 40 - 60% | 1 |
| Assignment 2 | Individual | 40 - 60% | 2,3,4,5 |
Hurdle
As the minimum requirements of assessment to pass the unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:
(i) An aggregate mark of 50% or more, and
(ii) Complete both assignments.
Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 45% as the total mark for the unit.
Content
- Introduction to data science
- Machine learning models
- Predictive analytics
- Advanced analytics
- Biases
- Communicating insights
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.