Availabilities:

LocationDomesticInternational
Gold Coast
Online

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

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:
1apply appropriate data collection methods to collect real-world data.
2visualise and communicate data, results and insights to stakeholders.
3develop appropriate algorithms to prepare data for big data analytics.
4design and develop machine learning algorithms for processing big data to extract patterns and insights.

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.

Prescribed Learning Resources

Prescribed Texts
  • Prescribed text information is not currently available.
Prescribed Resources/Equipment
  • Prescribed resources/equipment information is not currently available.
Prescribed Learning Resources may change in future Teaching Periods

Teaching and assessment

Teaching method
Workshop 1 hour (weekly)
Tutorial 2 hours (weekly)
Assessment
Project30%
Project60%
Short written response10%

Teaching method
Workshop 1 hour (weekly)
Tutorial 2 hours (weekly)
Assessment
Project30%
Project60%
Short written response10%
Notice

Intensive offerings may or may not be scheduled in every teaching period. Please refer to the timetable for further details.

Southern Cross University employs different teaching methods within units to provide students with the flexibility to choose the mode of learning that best suits them. SCU academics strive to use the latest approaches and, as a result, the learning modes and materials may change. The most current information regarding a unit will be provided to enrolled students at the beginning of the teaching period.

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.

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