| Unit code & Title | ANA204 - Predictive 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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Predictive Analytics is the use of data, statistical algorithms and machine learning techniques to produce actionable knowledge from historical data to facilitate decision-making in organisations. This unit aims to equip students with the fundamental knowledge and skills of performing appropriate predictive analytics methods targeting business needs. Topics include fundamental business analysis concepts, processes, essential tools and methods for predictive analytics, data pre-processing, principal component analysis, clustering, regression and association analysis. Besides the theoretical knowledge, students will learn to apply the practical skills through real-life study cases and gain hands-on experiences with SAS, one of the most widely adopted software and services for Analytics, Artificial Intelligence, Data Management and Visualization.
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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