Big Data Concepts

Undergraduate | 2026

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Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
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Area/Catalogue
INFO 1038
Course ID icon
Course ID
207123
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Campus
Adelaide City Campus
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course owner
Course owner
Adelaide University
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Course level
1
Work Integrated Learning course
Work Integrated Learning course
No
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No

Course overview

In this course, students will explore Big Data concepts, including cloud and Big Data architectures, an overview of Big Data analytics, implementation of Big Data initiatives, and to apply these concepts using an industry standard non-relational database environment. Part A: Big Data Fundamentals - The Data explosion and the evolution of Big Data: definition, characteristics, sources and benefits of Big Data; Challenges with the data explosion: information management and strategy, privacy and security, data analytics; Skills, competencies and roles associated with Big Data. Part B: Big Data Technologies Roadmap - Cloud and Big Data architectures; Non-relational databases: techniques for storing and processing large volumes of structured and unstructured data; Hadoop, MapReduce and the Big Data ecosystem; Information quality and governance of Big Data. Part C: Big Data Analytics Overview - Reasoning, logic, dealing with uncertainty, search, indexing and memory, streams, information, language and sentiment, visualisation of Big Data. Part D: Execution and Implementation of Big Data Initiatives - The business case for Big Data Initiatives, analytics process management, innovation using Big Data. Part E: Future Directions in Big Data.

Course learning outcomes

  • Identify appropriate Big Data tools to manage and analyse structured and unstructured data.
  • Use the map-reduce approach in, for example, a Hadoop ecosystem.
  • Discuss common Big Data analytic techniques and their applications.
  • Describe common data storage architectures and determine when a non-relational database architecture should be used.
  • Identify, for a business objective, relevant organisational Big Data and develop a business case for effective use of this Big Data.

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

  • must not have completed INFS3084 UO Big Data Concepts at the University of South Australia OR must not have completed INFS4020 Big Data Concepts at the University of South Australia

Assumed knowledge

N/A

Textbook(s)

N/A

Inherent requirements

N/A

Other resources

N/A

Assessment

N/A

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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.