Data Science Using Python
24 hours face to face + blended
One Semester or equivalent
Online
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 to resolve given data analytics relevant tasks.
Requisites
Teaching Periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
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
- Test, evaluate, improve and deploy an effective machine learning model
- 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 | 25 - 35% | 1 |
Assignment 2 | Individual | 65 - 75% | 2,3,4,5 |
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.