Unit rationale, description and aim

The explosion in data and digital technologies has opened new ways of obtaining data-driven insights. Organisations increasingly require professionals who can work with modern data ecosystems, streaming data sources, and visualisation tools to extract, analyse, and communicate insights.

This unit introduces advanced techniques in data analytics, including big data processing and visualisation using industry-standard and open-source tools. Students will use platforms such as Power BI, Google Looker Studio, Tableau, and Python-based libraries and big data technologies (e.g., Hadoop, Spark, cloud-native platforms) to transform raw data into meaningful insights. Students will also explore data ethics, geospatial analysis, and advanced dashboarding techniques.

The aim of this unit is to equip students with job-ready skills in applied analytics, big data techniques, data storytelling, and dynamic reporting—enabling them to deliver strategic, ethical, and environmentally responsible decisions in complex real-world scenarios.

2026 10

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  • Term Mode
  • Semester 1Campus Attendance

Prerequisites

ITEC617 Modern Database Management AND ITEC610 Introduction to Data Science with Python

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Evaluate data analytics, big data and visualisatio...

Learning Outcome 01

Evaluate data analytics, big data and visualisation tools, techniques and technologies for solving real world problems.
Relevant Graduate Capabilities: GC1, GC10

Apply appropriate tools and techniques to the coll...

Learning Outcome 02

Apply appropriate tools and techniques to the collect, clean, transform and manage both structured and unstructured data at scale.
Relevant Graduate Capabilities: GC2, GC10

Analyse transformed data using statistical and vis...

Learning Outcome 03

Analyse transformed data using statistical and visualisation techniques to uncover actionable insights.
Relevant Graduate Capabilities: GC8, GC10

Justify data analytics solutions aligned with orga...

Learning Outcome 04

Justify data analytics solutions aligned with organisational, societal, and environmental goals.
Relevant Graduate Capabilities: GC6, GC11

Content

Foundations

·       Introduction to Data Analytics, Big Data and Visualisation

·       Introduction to Descriptive, Diagnostic, Predictive, and Prescriptive Analytics

·       Big Data Fundamentals: Volume, Variety, Velocity, and Veracity

Tools and Processing

·       Data Transformation and Modelling with Power Query and Python

·       Big Data technologies (Hadoop, Spark, and Cloud-based Data Processing)

·       Integrating Multiple Data Sources (APIs, Spreadsheets, JSON)

Visualisation & Communication

·       Best Practices in Data Visualisation

·       Interactive Dashboard Design with Filters, Drilldowns, and Animations

·       Data Analytics and visualisation tools (e.g. PowerBI, Tableau, Google Looker Studio)

·       Visualising Time-Series, Geo-spatial, and Big Data

·       Communicating Insights and Data Narratives

Ethics & Impact

  •   Social, Environmental and Ethical issues in Big Data Analytics and Visualisation

Assessment strategy and rationale

To pass this unit, students must achieve an aggregate mark of at least 50%.

Assessment tasks are designed to reflect authentic, industry-relevant scenarios and scaffold students’ skill development across applied analytics, visualisation, communication, and ethical practice.

Assessment 1 consists of a series of practical exercises involving applied analytics and dashboarding excercises. It provides foundational exposure to hands-on tools and prepares students for the subsequent assessments.

Assessment 2 is focused on data visualisation in the context of a real world case study.

Assessment 3 is a capstone big data analytics project. Students will complete a real-world data project involving the full lifecycle considering the ethical, social, and environmental issues.

These assessments collectively ensure the development of technical fluency, strategic thinking, and responsible data practices aligned with the expectations of industry and postgraduate-level study. Use of generative AI tools is permitted only where explicitly authorised within individual assessment instructions. Misuse will be treated in line with the university’s academic integrity policy.

All tasks will be assessed using detailed rubrics to ensure consistency, transparency, and alignment with the AQF Level 9 expectations.

Overview of assessments

Assessment 1: Preparatory Exercises This assessm...

Assessment 1: Preparatory Exercises

This assessment consists of a series of exercises, including data analytics and visualisation using the Microsoft PowerBI, Power Apps, Power Automate and Power Virtual Agents..

The feedback from this assessment will help students to be ready to apply the concepts in the next assessments.

Submission Type: Individual

Assessment Method: Practical task

Artefact: Program Files

Weighting

30%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC1, GC2, GC8, GC10

Assessment Task 2: Data Visualisation Case Study ...

Assessment Task 2: Data Visualisation Case Study

Students design and develop a visualisation project to address a real-world issue. Includes report, embedded dashboards, and a short recorded walkthrough presentation.

Submission Type: Individual

Assessment Method: Practical Task

Artefact: Dashboard + Recorded Presentation (5-7 minutes)

Weighting

30%

Learning Outcomes LO1, LO3
Graduate Capabilities GC2, GC8, GC10

Assessment Task 3: Big Data Analytics Project S...

Assessment Task 3: Big Data Analytics Project

Students will undertake a project that simulates a real-world big data analytics and visualisation challenge. Using large, complex datasets from domains such as climate science, digital health, smart cities, or financial systems, students will complete the full data lifecycle — from data acquisition and wrangling to analysis, visualisation, and storytelling.

Students will (1) Ingest large structured and/or unstructured datasets from open data platforms, APIs, or cloud-based sources (2) Clean, transform, and model data using scalable tools and techniques

(3) Apply advanced analytics to generate insights

(4) Design dynamic dashboards and visual narratives that support data-driven decisions for social, environmental, or organisational impact

And (5) Address data ethics, bias, and transparency in their solution

Submission Type: Individual

Assessment Method: Practical task

Artefact: Project Report (1500 words), Dashboard, and Presentation (7-8 minutes)

Weighting

40%

Learning Outcomes LO1, LO2, LO3, LO4
Graduate Capabilities GC1, GC2, GC6, GC8, GC10, GC11

Learning and teaching strategy and rationale

This unit is delivered in multiple modes. Students should expect to commit approximately 150 hours over a 12-week semester, including class activities, readings, discussions, and assessments.

Blended Mode includes two-hour weekly workshops and two-hour fortnightly labs. Workshops use active learning strategies to explore key concepts through discussion, collaborative exercises, and applied problem-solving. Labs adopt an experiential learning approach, providing hands-on opportunities to design, test, and troubleshoot data mining workflows using industry tools. Digital platforms support preparation, practice, and consolidation of learning between sessions.

Online and ACU Online Modes adopt an active, student-centred learning approach. Students engage with e-modules, pre-recorded lectures, guided readings, and asynchronous discussions. Weekly activities are designed to build progressively from foundational concepts to applied analysis, encouraging peer interaction and real-world application. Regular, timely feedback supports individual learning progression.

All delivery modes aim to scaffold knowledge development, enhance engagement, and foster reflective, ethical, and professional data practices through active and problem-based learning.

Representative texts and references

Representative texts and references

Obembe, F., & Engel, O. 2024, A Hands-on Introduction to Big Data Analytics, SAGE Publications.

Camm Jeffrey D., Cochran James J, Fry Michael J and Ohlmann Jeffrey W.  2024, Business analytics: descriptive, predictive, prescriptive (International Edition), 5th ed, Cengage Learning Inc.

Camm Jeffrey D., Cochran James J, Fry Michael J and Ohlmann Jeffrey W.  2025, Data Visualization: Exploring and Explaining with Data, 2nd ed, Cengage Learning Inc.

Albright, S.C. & Winston, W.L. 2025, Business Analytics: Data Analysis and Decision Making, 8th ed., Cengage Learning Inc.

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