PhD in Recommender Systems Queensland University of Technology (QUT)
Postdoctoral Researcher
QUT × Department of Agriculture, Water and the Environment
Data Science & Artificial Intelligence
AI Specialist · Telstra Applied machine learning at scale in enterprise telecommunications
4 years of university teaching QUT · Kaplan Business School · Central Queensland University · James Cook University
This subject teaches you to design, program, and apply AI models to real-world business data. You will source, store, and prepare data to build AI applications that uncover insights of strategic value — working with financial, social media, text, image, and speech data.
From predictive modelling and causal inference through to generative AI and agentic frameworks, you will build a full spectrum of AI capabilities grounded in Python and applied business contexts.
"AI is no longer a specialist skill — it is the foundation of competitive advantage. The analysts who thrive will be those who can build, deploy, and critique AI systems."
From customer churn prediction to marketing automation, businesses need graduates who can move beyond dashboards — building AI applications that directly drive decisions and measurable outcomes.
This subject goes further than most analytics courses. You will learn causal machine learning — enabling you to evaluate whether an intervention actually caused an outcome, not just whether it correlated with one.
You will use Large Language Models, prompt engineering, and AI-assisted coding as integral parts of your analytics workflow — and learn to evaluate their limitations, biases, and ethical implications.
The course culminates in agentic AI frameworks — autonomous systems that plan and act across multi-step workflows. Understanding these positions you at the cutting edge of how AI is transforming enterprise operations.
Run a provided Colab notebook for a SaaS customer retention scenario. Use LightGBM, TFT forecasting, SHAP, and EconML causal analysis — then write a professional business report with your findings.
Analyse a social media dataset, conduct external research, and use generative AI to produce a marketing strategy complete with visual, video, and audio creative assets. Present live in class.
Apply the full AI analytics workflow to a real-world business problem of your choosing. Demonstrate mastery across all four learning outcomes — from data sourcing through to advanced business insights.
All analysis runs in browser-based Jupyter notebooks — no local installation required. Pre-built notebooks are provided for each assessment, allowing you to focus on interpretation and business insight rather than setup.
LightGBM for high-performance churn prediction; EconML for causal inference and treatment effect estimation (ATE & CATE); SHAP for explainable AI — understanding why the model made each prediction.
State-of-the-art sequence models for multi-horizon business forecasting. TFT handles complex temporal dependencies across multiple variables — used in Assessment 1 for revenue forecasting in a SaaS context.
Hands-on experience with large language model APIs for content generation and prompt engineering. ChromaDB and RAG pipelines for knowledge-grounded AI. Agentic frameworks for multi-step autonomous workflows.
Over-reliance on AI-generated content will be penalised across all assessments. Uploading the wrong file format incurs a project management penalty. Late submissions: 5% per day (1–9 days), 50% deduction (10–14 days), zero after 14 days.