Our IT Course in Artificial Intelligence

Students will gain an understanding of business analysis and systems analysis and design – both essential for creating software solutions for businesses. Students will explore current issues related to systems analysis and design, engage in practical exercises to analyse and design solutions for business problems, and grasp the significance of business analysis in facilitating the development of solutions for business growth. Topics covered include analysing strategic plans and ICT systems and proposing changes, evaluating the effect of changes, planning the implementation of changes, business solution requirements, analysing the impact of technical solutions, planning the implementation of business solutions, validating and finalising business solutions, reviewing and confirming system requirements, resolving conflicts and inconsistencies, building and testing the model and finalising the solution.

In this subject, students will gain an understanding of data extraction, normalisation, and security methodologies, alongside industry-standard data analysis techniques. They will explore automated tasks and processes within organisations, including email campaigns, chatbots, recruitment, and customer support. The course covers key machine learning (ML) principles, techniques, and algorithms for handling structured, unstructured, labelled, and unlabeled data. Students will learn to operate various algorithms, understand the logic behind them, and compare expected versus actual ML outputs. The subject also delves into secure ML model development, deployment requirements, and relevant organisational policies, including data governance, ethics, and Australia’s Artificial Intelligence Ethics Framework. Additionally, students will familiarise themselves with programming languages for algorithms and methodologies such as CRISP-DM and software development, ensuring they can apply ML principles to improve productivity in real-world contexts.

This subject aims to provide an understanding of various data-related tasks and skills. It covers the analysis, planning, and effective communication of data extraction and gathering activities. Proficiency in gathering and preparing data for analysis, with a special emphasis on ensuring data completeness, is a key focus. The course also explores the essential steps in data analysis and reporting, with particular attention to data verification and finalisation. Additionally, it delves into the realm of intellectual property (IP) requirements in organisations, addressing the review, determination, and updating of IP, ethics, and privacy policies. It also involves an examination of the establishment and maintenance of privacy policies and ethical guidelinesIt also provides instruction and expertise in recognising, creating, and validating data models while making required adaptations, ultimately improving proficiency in this area.

This subject aims to equip learners with the skills and knowledge required to critically assess and document software requirements and validate AI software design against given specifications. Students must provide evidence of their ability to interpret software specifications and validate software designs. The first part of the course includes developing at least two high-level system solutions according to client requirements. For the latter part, students must validate software design, source code, and testing requirements. All evidence should include well-documented processes and outcomes.