Availabilities:
| Location | Domestic | International |
|---|---|---|
| Online | Term1 | Term1 |
Unit description
Introduces students to computing for data analytics and the role it plays in problem-solving. Students will learn to break down problems into steps that can be performed by machines to solve problems and accomplish goals.
Unit content
Topic 1: Introduction to data analytics
Topic 2: Four types of data analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
Topic 3: Understanding business data
Topic 4: Data preparation: value generation from raw data
Topic 5: Explorative analysis for model planning
Topic 6: Model building and communication in data analytics projects
Learning outcomes
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
| On completion of this unit, students should be able to: | |
|---|---|
| 1 | articulate and apply knowledge of frameworks, processes, and techniques of data analytics and incorporate with the principles and strategies of the business environment |
| 2 | apply analytical thinking on business needs assessment and analytical problem framing |
| 3 | apply practical knowledge and skills of Python programming to perform data preparation, exploration, and modelling tasks |
| 4 | effectively interpret and communicate insights from data analytics projects using appropriate data analytics techniques. |
On completion of this unit, students should be able to:
- articulate and apply knowledge of frameworks, processes, and techniques of data analytics and incorporate with the principles and strategies of the business environment
- apply analytical thinking on business needs assessment and analytical problem framing
- apply practical knowledge and skills of Python programming to perform data preparation, exploration, and modelling tasks
- effectively interpret and communicate insights from data analytics projects using appropriate data analytics techniques.
Prescribed Learning Resources
- No prescribed texts.
- No prescribed resources/equipment.
Teaching and assessment
Fee information
Domestic
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Fee paying courses
For postgraduate or undergraduate full fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees
International
Please check the international course and fee list to determine the relevant fees.