Big Data
Duration
- One Semester or equivalent
Contact hours
- 48 hours face to face + blended
On-campus unit delivery combines face-to-face and digital learning.
2023 teaching periods
Hawthorn Higher Ed. Semester 2 |
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Dates: Results: Last self enrolment: Census: Last withdraw without fail: |
Prerequisites
Postgraduate:
Entry to MA-ITPC1 Master of Information Technology (Professional Computing)
or
and
Undergraduate:
and
Aims and objectives
This unit enables students to understand the nature of Big Data, and to evaluate and apply solutions to the problems of storing, integrating and analysing heterogeneous data sets with varying degrees of structure from diverse sources using the appropriate tools in order to extract meaningful information.
Unit Learning Outcomes (ULO)
On successful completion of this unit students will be able to:
1 Discuss the challenges and opportunities of Big Data in the context of its potential for organisations
2 Propose efficient solutions for the storage and management of large data sets
3 Use efficient tools to query diverse forms of data storage, such as NoSQL data sources
4 Compare and apply technologies and algorithms to extract and integrate information from data sets with varying degrees of structure
5 Appraise the quality and fitness for purpose of the information extracted, using suitable statistical methods
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
On Campus Lecture | 2 | 12 | 24 |
On Campus Class in Computer Lab | 2 | 12 | 24 |
Unspecified Activities Independent Learning | 8.5 | 12 | 102 |
TOTAL | 150 hours |
Assessment
Types | Individual/Group Role | Weighting | Unit Learning Outcomes (ULOs) |
Portfolio | Individual/Group | 100% | 1,2,3,4,5 |
Content
- Big Data, its nature, challenges and opportunities
- Storage and management technologies
- Query tools
- Tools and techniques for extraction of information from structured and unstructured data
- Integration of data/information from diverse sources
- Statistical methods for information extraction and quality evaluation
Study resources
- Reading materials.
Reading materials
A list of reading materials will be made available in the Unit Outline.