ANA404 – Data Mining and Machine Learning

Unit code & Title	ANA404 - Data Mining and Machine Learning
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	ULO1 Develop a clear understanding of machine learning and data mining principles, including supervised and unsupervised learning techniques, through the application of SAS tools and methodologies. ULO2 Apply data cleaning, transformation, and feature engineering techniques to prepare large datasets for analysis, ensuring data quality and integrity. ULO3 Identify, analyse, and interpret patterns within data using SAS data mining procedures, to uncover insights and trends for informed decision-making. ULO4 Create data visualisations in SAS to effectively communicate findings and support decision-making for both technical and non-technical audiences. ULO5 Gain practical experience in implementing state-of-the-art machine learning algorithms in SAS to address diverse data analysis tasks. ULO6 Evaluate and compare the performance of different models using SAS analytics tools and determine the most suitable approaches for specific data challenges. ULO7 Demonstrate an understanding of ethical considerations in machine learning and data mining, including issues related to data privacy, fairness, and responsible AI use. ULO8 Synthesise data-driven insights into practical applications using SAS, demonstrating how machine learning and data mining techniques can address real-world problems across various industries.

Overview

Data mining, a key component of knowledge discovery, enables the exploration and analysis of large data quantities through automatic and semi-automatic techniques.

This unit focuses on leveraging SAS as a powerful tool for data mining and machine learning applications. Machine learning focuses on the automated development of mathematical models to explain and generalise datasets, blending statistical methods and algorithmic approaches.

Students will gain hands-on experience with SAS programming and analytics covering core topics, such as pattern discovery, classification, regression, feature extraction, and data visualisation, alongside the analysis, comparison, and application of various machine learning and statistical techniques.

This unit provides a comprehensive introduction to machine learning and data mining using SAS, emphasising both practical applications and technical understanding.

By the end of this unit, students will be equipped with essential skills in SAS programming for uncovering insights, identifying patterns, and making predictive decisions through advanced data analysis.

The unit is aligned with the following course learning outcome (CLO):

• CLO1: Demonstrate a deep understanding of fundamental concepts and principles of business analytics, including data analysis techniques, modelling, and interpretation.

Unit Fee Information

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

Important Dates

Refer to Academic Calendar