Summary of content

This module introduces students to prescriptive analytics which represents the pinnacle of the analytics journey, focusing on recommending optimal courses of action to achieve desired outcomes. It involves analysing data, predictive models, and decision variables to generate actionable recommendations that guide decision-making processes. Prescriptive analytics helps businesses answer the question "What should we do?" by providing insights into the best actions to take given specific objectives, constraints, and uncertainties. It enables businesses to optimise decision-making processes, improve operational efficiency, and maximise value creation.

The syllabus offers a comprehensive and progressively structured exploration of prescriptive analytics, equipping students with the theoretical understanding and practical skills required to make data-driven, optimised business decisions. Beginning with foundational concepts, it guides students through essential optimisation methods (linear, integer, nonlinear, and multi-objective programming) before advancing to specialised areas including simulation modelling, game theory, and reinforcement learning. Emphasis is placed on both model formulation and practical implementation using industry-relevant tools, ensuring students are prepared to apply prescriptive analytics techniques in real-world scenarios.

Throughout the module, students engage with structured decision-making frameworks, ethical considerations, and strategic communication techniques, aligning with key learning outcomes such as problem-solving using optimisation, effective data preparation, and communicating analytical insights to stakeholders. Case studies and content focused on application ensure students can evaluate, select, and implement appropriate methodologies to solve complex business challenges, while also developing critical awareness of the limitations and societal implications of their work.

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