Equips students with the tools to build contemporary Big Data processing and analysis systems. Students 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 datasets including natural language datasets.
Topic 1: Introduction to big data and data pre-processing
Topic 2: Data wrangling and dimensionality reduction
Topic 3: Managing time-series data
Topic 4: Data visualisation techniques
Topic 5: Introduction to natural language processing and text mining
Topic 6: Text classification algorithms
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||identify, manipulate and apply Big Data storage and processing technologies|
|2||acquire and clean large data sets|
|3||extract features and model large data sets|
|4||design algorithms and architectures for processing large data sets to extract patterns and information|
|5||develop natural language processing algorithms for text mining|
On completion of this unit, students should be able to:
- identify, manipulate and apply Big Data storage and processing technologies
- acquire and clean large data sets
- extract features and model large data sets
- design algorithms and architectures for processing large data sets to extract patterns and information
- develop natural language processing algorithms for text mining
- Prescribed text information is not currently available.
Teaching and assessment
Commonwealth Supported courses
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