The purpose of this module is to increase the competency and comfort of learners in managing and leveraging data and quantitative operations to inform business decision-making practices. This module supports a structured and accessible transition from spreadsheet-based analysis to industry-leading data analysis tools. Learners will be introduced to advanced concepts in data inspection, manipulation, and visualisation through practical, real-world datasets linked to contemporary global business events. Learners will develop skills in AI techniques that specialise in the analysis of different data types in business analysis contexts. Regression modelling will be introduced for learners interested in making business decisions using quantitative insights, while clustering and textual analysis will be introduced for students interested in making business decisions using qualitative insights. A learn-by-doing approach will be implemented, allowing learners to develop technical and programming skills alongside theoretical and computational skills. The module schedule will allow for appropriate learner reflection and independent practice sessions to foster intra-class assurance of learning. Learners will have demonstrative evidence of furthering their expertise in business decision-making using statistical analysis and AI modelling in Excel and Python contexts. Learners will be able to navigate and utilise financial database systems for data sourcing and analysis and be able to leverage Excel and Python for data manipulation, data inspection, and data analysis.

Translating business and economic problems into analytical frameworks. Critical understanding of business and economic data using advanced data visualisation and data insights, Introduction to Python programming in an AI-supported learning environment, in parallel with Excel. Introduce business contexts that benefit from the use of advanced analysis tools. Introduce real-world business problems that require actionable insight using data analysis techniques. Understand how key business decisions are formed, assessed, and decided upon using data analysis tools, while appreciating the business case and business environment. Be introduced to solutions being addressed by Generative AI and Agentic modelling, and contemporary issues that remain in these systems. Understand ethical challenges with the use of these systems, and apply strategies and ‘critical review’ practices to maintain learner integrity. Introduce how business challenges can be translated to an AI context, and how data-driven insights can be derived from AI modelling. Leverage AI-assisted coding platforms (e.g. Gemini) to aid in the development of AI programming skills in Python. Apply Python for data visualisation and data analysis to develop data-driven business insights. Develop an appreciation of AI model reviews and troubleshooting in understanding the importance of outliers, pattern identification, and AI modelling limitations.

On successful completion of this module, students will be able to: Explain the role of data exploration and analysis in addressing global business analytics and societal challenges. Apply artificial intelligence (AI) modelling and advanced analytics techniques in appropriate business contexts. Analyse the role and ethical considerations of incorporating Generative AI into your data inspection and decision-making routine. Diagnose and frame limitations and abnormalities in the outputs of AI models typically used to inform business decision-making. Justify business-enhancing decisions developed using statistical techniques and data inspection software.

On successful completion of this module, students will be able to: Reflect on the role of data literacy and analytical skills in their personal, academic, and professional development.

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The purpose of this module is to increase the competency and comfort of learners in managing and leveraging data and quantitative operations to inform their business decision-making and analytical practices. A learn-by-doing approach will be implemented in this class, meaning learners will develop technical and programming skills as well as their conceptual, theoretical, and computational skills. Innovative teaching practices will be incorporated into the module schedule to allow for appropriate learner reflection and independent practice sessions to foster intra-class assurance of learning. Upon successful completion of the class, learners will have demonstrative evidence of furthering their expertise in business decision-making and AI modelling. Learners will be able to navigate and utilise financial database systems for data sourcing and analysis, and be able to leverage Excel and Python in appropriate data manipulation, data inspection, and data analysis contexts. This module will leverage skills developed in previous business analytics modules to introduce learners to industry-leading data analysis tools and techniques and develop data-driven learners who are investigative, ambitious, and curious.