Big Data Analytics

Unit Outline (Higher Education)

Level of Unit in Course

AQF Level(s) of Course

Level of Unit in Course

Introductory

Intermediate

Effective Term:

2026/05

Institute / School :

Institute of Innovation, Science & Sustainability

Unit Title:

Big Data Analytics

Unit ID:

GPSIT1103

Credit Points:

15.00

Prerequisite(s):

Nil

Co-requisite(s):

Nil

Exclusion(s):

Nil

ASCED:

020303

Other Change:

Brief description of the Unit

This unit provides fundamental concepts related to big data and analytics and will explore the theory and applications of big data and demonstrate the process from data to decisions. Students will examine the complete process from data to decision-making, learning to work with big data in various formats, data processing platforms, and analytics tools. They will develop skills to transform, visualise, model, and communicate insights hidden within data; providing end users with timely, actionable knowledge to support informed decisions. This unit will explain the challenges organisations are facing in managing big data. To support student success, additional learning hours are provided to support the development of students’ academic and study skills.

Grade Scheme:

Graded (HD, D, C, P, MF, F, XF)

Work Experience Indicator:

No work experience

Placement Component:

Supplementary Assessment:Yes

Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment

Course Level:

Learning Outcomes:

Knowledge:

K1.	Describe the stages involved in designing and implementing modern data solutions, including relational and big data systems.

K2.	Identify and classify different types of data (e.g. structured, semi-structured, unstructured) and their sources.

K3.	Outline common data analytics techniques, tools, and applications.

K4.	Recognise the key stages of the data analytics lifecycle and the challenges organisations face in managing data.

Skills:

S1.	Apply basic techniques to design and build a data-centric application.

S2.	Use analytical tools to explore and interpret real-world datasets.

S3.	Identify current trends and challenges in big data and analytics.

S4.	Develop the appropriate English language and academic skills to successfully study at an undergraduate level.

Application of knowledge and skills:

A1.	Communicate the outcomes of a data analytics process in a clear and structured manner.

A2.	Apply basic big data analytics tools to a real-world dataset.

Unit Content:

Topics may include:

1. Different types of data (e.g. structured, semi-structured, unstructured)

2. Sources of data (e.g. sensors, medical, business, social data)

3. Database life cycle (e.g., requirements analysis, design, implementation, maintenance)

4. Database design principles: normalisation, entity-relationship modelling.

5. Relational database concepts: tables, rows, columns, keys, relationships.

6. SQL (Structured Query Language): basic queries, data manipulation, data definition, joins.

7. Definition, scope and components of Information Systems.

8. Representation and use of data in Information System (Master Data, Transactional Data)

9. Components of an Information System (e.g., hardware, software, data, procedures, and people)

10. Types of Information Systems: transaction processing systems, management information systems, decision support systems, executive support systems, etc.

Graduate Attributes:

	Learning Outcomes Assessed	Assessment Tasks	Assessment Type	Weighting
1.	K1, K2, K3, K4, S1, S2, S3, S4, A2	Students will describe different types of data and their sources, demonstrating foundational understanding of data classification and origin.	Tutorial task(s)	10-30%
2.	K2, S1, S2, S4, A1, A2	Students will select and/or implement an appropriate data management solution for a specific problem. They will describe the components of the solution and respond to theoretical questions that provide context and encourage reflection on the analytical tasks undertaken.	Practical Assignment(s)	30 - 60%
3.	K1, K2, K3, K4, S1, S3, S4, A1	Students will select an appropriate information systems solution for a specific problem and describe its components. The task includes theoretical questions to support contextual understanding and reflection on the analytical process.	Oral Presentation(s)	30-60%

Adopted Reference Style:

APA ()

Professional Standards / Competencies:

Standard / Competency