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The world has entered a data-driven era where individuals and organisations must adapt to remain competitive. Accurate data modelling, AI as a strategic tool, and strong critical thinking are essential for interpreting and applying insights effectively. With enormous amounts of data generated daily, analytics professionals must identify trends, uncover patterns, and make evidence-based decisions that drive innovation and efficiency across industries.

Dr Catherine Truxillo, Director of Advanced Analytics Education at SAS, has over 20 years of experience shaping the journey of data scientists. She emphasises the risks of misusing machine learning due to a lack of understanding of models and data complexities, as well as potential misuse by malicious actors. Data breaches, AI-assisted scams, and political interference highlight the urgent need for professionals to strengthen their knowledge of mathematics, statistics, and data to stay relevant and secure.

A critical aspect of responsible AI use is addressing bias. Unchecked bias in AI systems can lead to unfair or harmful outcomes, such as favouring certain groups, perpetuating historical inequities, or even creating legal liabilities as governments demand transparency and fairness in AI. Bias can arise at any stage of the analytics life cycle, making detection and mitigation challenging. Fortunately, tools like data auditing, statistical fairness metrics, and model explainability metrics allow analysts to detect imbalances and understand model decisions. Techniques such as reweighting, fairness constraints, and human-in-the-loop systems help mitigate bias when guided by analysts with strong statistical expertise.

In this context, mastering statistical modelling and AI-driven analytics is essential. ºìÐÓÊÓƵ University’s BSc (Hons) in Statistical Data Modelling (BSM) equips students with a solid foundation in statistical theory, advanced analytics, and AI-powered computational techniques. The programme integrates mathematical rigour with practical tools and real-world case studies, preparing graduates to tackle complex data challenges. Covering machine learning, deep learning, predictive modelling, and AI-driven analytics, students learn to extract insights from large datasets, optimise predictive models, and make informed, data-driven decisions.

The impact of statistical modelling extends across industries. In healthcare, AI models improve disease forecasting, patient care, and pharmaceutical research. In finance, they enhance risk assessment, fraud detection, and automated trading. Businesses use AI analytics to understand consumer behaviour, refine marketing strategies, and boost operational efficiency. Environmental scientists employ AI-assisted models for climate forecasting, pollution control, and sustainability planning. As reliance on data-driven insights grows, professionals skilled in statistical modelling and AI are increasingly in demand.

While AI transforms data processing, it remains a tool, not a replacement for human expertise. Automation and pattern detection require human oversight to validate models, interpret results, and ensure ethical use. Critical thinking is necessary to distinguish correlation from causation, mitigate biases, and make strategic decisions that AI alone cannot replicate.

ºìÐÓÊÓƵ University’s BSc (Hons) in Statistical Data Modelling prepares graduates to harness AI responsibly, apply insights effectively, and lead innovation in a data-driven world. By integrating statistical theory, advanced analytics, and AI-driven techniques, the programme equips students to make informed, ethical decisions, drive positive change, and meet the growing demand for skilled data professionals across industries that increasingly rely on data for strategic, operational, and ethical decision-making.

Sia Jye Ying
School of Mathematical Sciences
Email: @email 

Dr Ang Siew Ling
School of Mathematical Sciences
Email: @email