Unit of Study COMP6004 Big Data Analysis and Tools (2024)

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Unit Snapshot

Unit type

PG Coursework Unit
Credit points

12
Faculty & College

Faculty of Science and Engineering
Placement

No

Pre-requisites

COMP6001 - Computational Intelligence and Machine Learning

Unit description

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.

Unit content

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

Availabilities

Location	Domestic	International
Gold Coast	Term2	Term2
Melbourne	Term2	Term2
Online	Term2	Term2
Perth	Term2	Term2
Sydney	Term2	Term2

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:

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.

Teaching and assessment

Teaching method

Workshop 1 hour (weekly)

Tutorial 2 hours (weekly)

Assessment
Project	30%
Project	60%
Short written response	10%

Teaching method

Workshop 1 hour (weekly)

Tutorial 2 hours (weekly)

Assessment
Project	30%
Project	60%
Short written response	10%

Teaching method

Workshop 1 hour (weekly)

Tutorial 2 hours (weekly)

Assessment
Project	30%
Project	60%
Short written response	10%

Teaching method

Workshop 1 hour (weekly)

Tutorial 2 hours (weekly)

Assessment
Project	30%
Project	60%
Short written response	10%

Teaching method

Workshop 1 hour (weekly)

Tutorial 2 hours (weekly)

Assessment
Project	30%
Project	60%
Short written response	10%

Prescribed learning resources

Prescribed Texts

No prescribed texts.

Prescribed Resources/Equipment

Python (https://www.python.org/)

Prescribed Learning Resources may change in future Teaching Periods.

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.

Find out more for domestic fees