Big Data
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
NIT1201 - Introduction to Database Systems
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
In this unit, you will explore the fundamental concepts, challenges, and opportunities of Big Data, focusing on its five key characteristics: Volume, Velocity, Variety, Veracity, and Value. You will learn about data processing architectures, distributed computing models, and open-source technologies used to build scalable solutions. You will develop practical skills in handling large-scale datasets using modern tools and frameworks, and you are encouraged to engage in critical discussions on data ethics, privacy, and governance. By the end of the unit, you will be equipped with the knowledge and skills needed to operate confidently in data-driven environments.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Analyse the big data challenges and opportunities in various industries;
- Explain the impact of big data features through real-world case studies;
- Apply architectural components and programming models of big data frameworks;
- Collaborate effectively to develop technological solutions using open-source big data tools;
- Evaluate ethical, privacy, and governance considerations related to big data applications.
Assessment
For Melbourne campuses
Assessment type: Test | Grade: 20%
Online test
Assessment type: Assignment | Grade: 40%
Big data system design and implementation (Group) with Q&A verification.
Assessment type: Assignment | Grade: 40%
Scenario-based solution and presentation (Group) with Q&A verification.
Assignment 1: Includes structured milestone reviews and in-class progress checks. Individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.
Assignment 2: Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.
Other locations
Assessment type: Laboratory Work | Grade: 25%
Practical lab work
Assessment type: Case Study | Grade: 25%
Big data system design and implementation (Group) with Q&A verification.
Assessment type: Examination | Grade: 50%
Invigilated final written examination
Case Study: Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding. Examination: Invigilated, time-bound exam includes unseen data sets or problem types. Students must demonstrate deep understanding by explaining reasoning, performing analysis, and applying concepts to new contexts.
Required reading
Selected readings are provided on VU Collaborate.
As part of a course
This unit is studied as part of the following course(s):