Data Science Principles
48 hours face to face + Blended
One Semester or equivalent
Hawthorn, Online
Available to incoming Study Abroad and Exchange students
Overview
This unit aims to introduce students to the key concepts and techniques in data science and Big Data analytics. Students will be introduced to different stages in data analytics and given the opportunity to plan and design data science projects that address business and organisational needs.
Requisites
Prerequisites
Assumed Knowledge
Students are expected to have basic programming skills
Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 1
Location
Hawthorn
Start and end dates
03-March-2025
01-June-2025
01-June-2025
Last self-enrolment date
16-March-2025
Census date
31-March-2025
Last withdraw without fail date
24-April-2025
Results released date
08-July-2025
Teaching Period 1
Location
Online
Start and end dates
10-March-2025
08-June-2025
08-June-2025
Last self-enrolment date
23-March-2025
Census date
04-April-2025
Last withdraw without fail date
02-May-2025
Results released date
01-July-2025
Teaching Period 3
Location
Online
Start and end dates
03-November-2025
08-February-2026
08-February-2026
Last self-enrolment date
16-November-2025
Census date
28-November-2025
Last withdraw without fail date
02-January-2026
Results released date
03-March-2026
Learning outcomes
Students who successfully complete this unit will be able to:
- Appreciate the roles of data science and Big Data analytics in organisational contexts
- Compare and analyse the key concepts, techniques and tools for discovering, analysing, visualising and presenting data
- Describe the processes within the Data Analytics Lifecycle
- Analyse organisational problems and formulate them into data science tasks
- Evaluate suitable techniques and tools for specific data science tasks
- Develop and execute an analytics plan for a given case study
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Live Online Lecture | 2.00 | 12 weeks | 24 |
| On-campus Class | 2.00 | 12 weeks | 24 |
| Online Directed Online Learning and Independent Learning | 1.00 | 12 weeks | 12 |
| Unspecified Activities Independent Learning | 7.50 | 12 weeks | 90 |
| TOTAL | 150 |
Sarawak
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Online Directed Online Learning and Independent Learning | 12.50 | 12 weeks | 150 |
| On-campus Lecture | 2.00 | 12 weeks | 24 |
| On-campus Class | 2.00 | 12 weeks | 24 |
| Online Directed Online Learning and Independent Learning | 1.00 | 12 weeks | 12 |
| Unspecified Activities Independent Learning | 7.50 | 12 weeks | 90 |
| TOTAL | 300 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment 1 | Individual/Group | 10 - 30% | 4,5,6 |
| Assignment 2 | Individual/Group | 10 - 30% | 1,2,3 |
| Online Tests | Individual | 5 - 15% | 4,5,6 |
| Online Tests | Individual | 5 - 15% | 2,3,4,5,6 |
| Online Tests | Individual | 30 - 50% | 1,2,3,4,5,6 |
Content
- Introduction to Data Science and Big Data Analytics
- Roles of Data Science for business
- Contemporary data structures and management
- Data Science tools, techniques and technologies
- The Data Analytics Lifecycle
- Analytical techniques and methods
- Analytics plan development
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.