Equips students with tools, techniques and algorithms to build contemporary Big Data processing and analysis systems. Students will learn how to create and develop each task in the machine learning pipeline from acquiring and cleaning data to analysing and visualising insights obtained from data.
Topic 1: Introduction to big data
Topic 2: Data visualisation techniques
Topic 3: Data pre-processing
Topic 4: Dimensionality reduction and feature selection
Topic 5: Data wrangling
Topic 6: Data modelling
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||apply appropriate data collection methods to collect real-world data.|
|2||visualise and communicate data, results and insights to stakeholders.|
|3||develop appropriate algorithms to prepare data for big data analytics.|
|4||design and develop machine learning algorithms for processing big data to extract patterns and insights.|
On completion of this unit, students should be able to:
- apply appropriate data collection methods to collect real-world data.
- visualise and communicate data, results and insights to stakeholders.
- develop appropriate algorithms to prepare data for big data analytics.
- design and develop machine learning algorithms for processing big data to extract patterns and insights.
Prescribed Learning Resources
- No prescribed texts.
Teaching and assessment
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Please check the international course and fee list to determine the relevant fees.