| Unit code & Title | ANA603 - Big Data Analytics |
| Pre-requisite | Not Applicable |
| Delivery modes | On-campus; Online; |
| Credit points | 10 |
| Study commitment | Average of 150 hours of teaching, learning and assessment over the trimester. |
| Scheduled learning (On-campus) | 2 × 3 hours on-campus seminar and tutorial weekly (for block mode) |
| Scheduled learning (AIA Online) | Recorded contents + 2 × 3 hour online seminar and tutorial weekly |
| Learning Outcomes |
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This unit is designed to equip students with advanced knowledge and practical skills in leveraging SAS (Statistical Analysis System) for the effective management, analysis, and visualisation of Big Data. As industries increasingly adopt data-driven decision-making processes, understanding how to harness the power of SAS tools for handling large-scale datasets becomes critical.
The course delves into a variety of SAS applications and tools, including SAS Viya, SAS Visual Analytics, and SAS Event Stream Processing, emphasizing their roles in Big Data ecosystems. Students will gain insights into the architecture and functionality of SAS as it integrates with modern Big Data frameworks like Hadoop and Spark. They will explore techniques for ingesting, cleaning, and transforming massive datasets to prepare them for analytics and modelling.
A key focus of this unit is on advanced analytics and real-time decision-making. Through hands-on labs and case studies, students will learn how to build and deploy statistical models, machine learning algorithms, and predictive analytics using SAS. They will also explore real-time event-driven architectures to tackle scenarios such as fraud detection, dynamic pricing, and operational monitoring.
Visualisation plays a pivotal role in Big Data analytics. This unit emphasises using SAS Visual Analytics to create compelling dashboards and visualisations, enabling students to communicate insights effectively to stakeholders. Additionally, students will learn to design Big Data solutions that adhere to governance, security, and ethical standards.
Through a blend of theoretical concepts and practical applications, this course bridges the gap between academic understanding of Big Data and its real-world implementation. By the end of the unit, students will be prepared to use SAS to address complex Big Data challenges across various industries, making them valuable assets in the field of data analytics and decision sciences.
The unit is aligned with the following course learning outcome (CLO):
• CLO6: Utilise business intelligence tools and techniques to generate insights from data and create meaningful reports for decision-making purposes.
• CLO7: Employ advanced data visualisation techniques to communicate complex analytical findings effectively to stakeholders.
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit Current student page
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