Unit rationale, description and aim
Big Data Analytics focuses on extracting value from extremely large, complex, and fast-moving datasets that exceed the capabilities of traditional data-management and analytic approaches. Such datasets often combine structured, semi-structured, and unstructured data from multiple sources at scale, requiring distributed storage, high-performance processing, and specialised analytical methods. As big data technologies continue to evolve rapidly, employers increasingly seek graduates who can design scalable data pipelines and apply advanced analytics in real-world contexts.
This unit provides students with an in-depth understanding of the methods and technologies used to address the three defining characteristics of big data: volume, variety, and velocity. Students will learn about scalable cloud and high-performance computing infrastructures for large-scale data storage and processing, and will apply analytics techniques such as data integration, statistical modelling, stream processing, and machine learning to heterogeneous big-data environments.
The unit aims to develop students’ technical competence in big-data analytics and their hands-on experience in designing and implementing scalable solutions. It also builds students’ ability to critically evaluate the organisational and societal consequences of big-data use, including ethical, sustainability, privacy, and equity considerations.
Campus offering
No unit offerings are currently available for this unit.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.
Explain the characteristics of big data and the ar...
Learning Outcome 01
Apply scalable data integration, statistical analy...
Learning Outcome 02
Design and implement an end-to-end big-data analyt...
Learning Outcome 03
Critically evaluate big-data analytics solutions w...
Learning Outcome 04
Content
Topics will include:
- Core characteristics of big data: volume, variety, velocity
- Contemporary big-data ecosystems and architectures
- Distributed storage systems and scalable data management
- Data lakes and lakehouse approaches for heterogeneous data
- NoSQL models for high-scale and flexible access patterns
- Cloud and high-performance computing foundations
- Batch processing paradigms and distributed frameworks
- Large-scale data integration and pipeline design
- Scalable statistical analysis and machine-learning techniques
- Streaming data concepts and real-time analytics workflows
- Visualisation and communication of big-data insights
- Governance, privacy, ethics, sustainability, and societal impacts
Assessment strategy and rationale
A range of assessment procedures will be used to address the unit learning outcomes and develop graduate capabilities in alignment with university assessment requirements. Assessment 1 is an individual, scaffolded case-study analysis that develops foundational understanding of big-data characteristics, architectures, and decision contexts, and introduces ethical and societal considerations. It also incorporates integrity safeguards through personalised case materials, process evidence, and brief oral verification. Assessment 2 is an individual scalable analytics portfolio in which students design and execute distributed data-integration and analytics tasks on a large dataset, evaluate results, and interpret them for decision support. Assessment 3 is a group project requiring students to design, implement, and present an end-to-end big-data analytics workflow using cloud or high-performance computing tools, including a performance and ethics/sustainability evaluation. Together, these assessments build knowledge progressively from conceptual understanding to applied technical skills and critical evaluation. To pass the unit, students must demonstrate achievement of every unit learning outcome, pass hurdle tasks, and obtain a minimum mark of 50% in graded units
Overview of assessments
Assessment 1: Case Study Analysis – Big Data in P...
Assessment 1: Case Study Analysis – Big Data in Practice
Students analyse a unit-provided case study on big-data adoption in an organisation and submit a concise report explaining the big-data challenges (3Vs), the chosen platform/architecture, and the analytics value delivered, alongside risks and limitations. To ensure academic integrity security the assessment requires process evidence (annotated reasoning linked to case exhibits), and is followed by a brief in-class or live oral verification where students justify their key interpretations..
Submission Type: Individual
Assessment Method: Practical task + Presentation
Artefact: Documentation + Live / Recorded with face-overlay Presentation (5 minutes)
25%
Assessment Task 2: Big Data Analytics Project – S...
Assessment Task 2: Big Data Analytics Project – Stage 1
Students build a reproducible big-data analytics portfolio using a large real-world dataset. Deliverables include a distributed data-processing workflow (e.g., batch pipeline), applied statistical/ML analysis, performance discussion (scalability, latency, cost), and a short stakeholder-oriented interpretation. The portfolio is submitted as a notebook plus a concise report.
Submission Type: Group assignment
Assessment Method: Research & Data Analysis
Artefact: Report & Live / Recorded with face-overlay group Presentation (8 minutes) + Online Viva
30%
Assessment Task 2: Big Data Analytics Project – S...
Assessment Task 2: Big Data Analytics Project – Stage 2
Teams design, implement, and demonstrate an end-to-end big-data workflow addressing a defined business or societal problem. The submission includes a project report, technical artefacts (pipeline, code, dashboard), and a presentation covering design decisions, evaluation of performance, and ethical/sustainability considerations.
Submission Type: Group assignment
Assessment Method: Research & Data Analysis
Artefact: Report + Live / Recorded with face-overlay Presentation (10 minutes) + Online Viva
45%
Learning and teaching strategy and rationale
This unit is delivered through Attendance and Online modes using a single, integrated learning and teaching strategy designed to ensure equivalent learning outcomes and a comparable learning experience for all students, while supporting diverse learning needs and maximising access.
Across both modes, learning activities are intentionally aligned to the unit learning outcomes and assessment tasks, and are underpinned by active learning, guided engagement with disciplinary knowledge, opportunities for peer interaction, and regular, timely feedback. While the mode of delivery shapes how students participate, the pedagogical intent, expectations and standards remain consistent.
In Attendance mode, students engage in weekly face-to-face classes at designated locations, supported by preparatory activities prior to workshops and opportunities for consolidation following classes. Online learning platforms are used to complement face-to-face teaching through additional resources and learning activities.
In Online mode, students engage with the same core content and learning outcomes through a combination of synchronous and asynchronous activities, including structured discussions and applied learning tasks that support learning in professional contexts.
Across both delivery modes, students should plan to commit approximately 150 hours to this unit over the semester, including participation in learning activities, independent study, readings and assessment preparation.