Introduction to Data Science
Overview
This unit aims to develop students’ conceptual and practical understanding of the field of data science in the contexts of real-world applications. The students will learn about basic concepts, key techniques and popular tools in various aspects of data science, following the lifecycle of a practical data science project which involves data collection, management, wrangling, analytics and visualisation. They 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 science relevant tasks.
Requisites
MA-ITPC1 Master of Information Technology (Professional Computing)
OR
COS60006 Introduction to Programming
OR
Concurrent Pre-requisite
COS60010 Technology Inquiry Project
Assumed Knowledge
Basic understanding of Database concepts
01-June-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Demonstrate knowledge of fundamental concepts, key techniques and popular tools in data science
- Demonstrate understanding of the lifecycle of a practical data science project
- Demonstrate how to identify and define data science relevant tasks in practical scenarios
- Critically analyse, evaluate and apply given techniques and tools to solve given data science relevant problems
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Online Lecture |
1.00 | 12 weejk | 12 |
| On-campus Lecture |
1.00 | 12 weeks | 12 |
| On-campus Lab |
2.00 | 12 weeks | 24 |
| Unspecified Activities Independent Learning |
8.50 | 12 weeks | 102 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment | Individual | 30 - 50% | 1,2,3 |
| Project | Individual/Group | 40 - 60% | 1,2,3,4 |
| Test | Individual | 10 - 30% | 1,2,3,4 |
Content
- Foundations of data science
- Data science in real world
- Lifecycle of practical data science projects
- Data collection: basic concepts, techniques and tools
- Data management: basic concepts, techniques and tools
- Data wrangling: basic concepts, techniques and tools
- Data analytics: basic concepts, techniques and tools
- Data visualisation: basic concepts, techniques and tools
- Applications of given data science relevant techniques and tools to deal with specific data science relevant tasks
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.