Unit of Study ISYS2002 Data Wrangling and Advanced Analytics (2024)

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Unit Snapshot

Unit type

UG Coursework Unit
Credit points

12
Faculty & College

Faculty of Science and Engineering
Placement

No

Unit description

Introduces students to big data technologies. Students will gain the knowledge and skills to design and implement data analytics models using programming languages and contemporary data analytics tools to solve real-world problems and aid decision-making.

Unit content

Introduction to Big Data Analytics
Data Pre-processing
Data Wrangling
Introduction to Neural Networks
Neural Network based Data Modelling
Big Data Analytics Case study

Availabilities

Location	Domestic	International
Gold Coast	Term2	Term2
Melbourne	N/A	Term2
Online	Term2	Term2
Perth	N/A	Term2
Sydney	N/A	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:

use contemporary data analytics libraries and tools

develop and apply algorithms to prepare data for analysis

analyse and apply appropriate data modelling approaches

develop business intelligence systems using data analytics tools and programming libraries

Teaching and assessment

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Project	40%
Project	50%
Practical	10%

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Project	40%
Project	50%
Practical	10%

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Project	40%
Project	50%
Practical	10%

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Project	40%
Project	50%
Practical	10%

Teaching method

Workshop 1 hour (Weekly)

Tutorial 2 hours (Weekly)

Assessment
Project	40%
Project	50%
Practical	10%

Prescribed learning resources

Prescribed Texts

No prescribed texts.

Prescribed Resources/Equipment

No prescribed resources/equipment.

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.

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