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 equips you with the tools and frameworks to analyse time series data — data that evolves over time — and extract insights that drive strategic decisions. You will work with real-world datasets, apply industry-standard methods, and communicate findings like a practising Business Analyst.
From exponential smoothing to ARIMA and Meta's Prophet, you will build a portfolio of methods and the judgement to choose between them in context.
"Every strategic business decision is a bet on the future. Forecasting is how you make that bet an informed one."
Demand forecasting, financial planning, supply chain optimisation, and risk management all rely on time series thinking. As an MBA(Analytics) graduate, clients will expect you to deliver these capabilities.
This subject is not just about fitting models — it is about understanding what a forecast means for a business: when to invest, when to hedge, and when to act. That translation skill defines senior analysts.
You will use Prophet (Meta), SARIMA, and Tableau forecasting — the same tools deployed by companies like Amazon, Telstra, and government analytics teams for live planning decisions.
Retail demand, water security, financial markets, healthcare capacity, climate risk — time series analysis is the common language of all data-driven industries. Your domain will dictate the data; the methods remain yours.
Use GenAI to create a realistic synthetic dataset for an assigned company, validate it quantitatively, run a Tableau forecast, and present strategic insights.
Forecast water storage levels using Holt-Winters, Prophet, and SARIMA. Compare models, evaluate out-of-sample accuracy, and advise on water management policy.
Choose a real-world forecasting problem from your own workplace, public data, or financial markets. Present a 3-minute pitch and submit a 1,000-word analytical report.
Interactive dashboards and built-in time series forecasting. Used in Assessment 1 for presenting synthetic data forecasts. Industry standard for communicating analytical outputs to non-technical stakeholders.
A no-code interface for statistical analysis and time series modelling. Provides ACF/PACF plots, decompositions, and model diagnostics — ideal for understanding data structure before modelling.
Visual programming environment for machine learning workflows. Allows rapid experimentation with forecasting pipelines without writing code — valuable for business analysts who need results quickly.
Notebook-based Python environment for Prophet, SARIMA, and custom forecasting pipelines. Required for residual diagnostics (ACF/PACF) in Assessment 2. No local installation required.
Word limits are strictly enforced — marking stops at the 110% threshold. GenAI over-reliance is penalised across all assessments; original analysis and reasoning are required. Late submissions attract a 5% daily penalty.