Essentials of Financial Data Analytics and Visualization with Python and Generative AI

Deniz Erdemlioglu and Paolo Mazza
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Deniz Erdemlioglu: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Paolo Mazza: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique

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Abstract: This case places learners in a fast-paced, real-world role-play scenario where they are urgently contacted to prepare a financial data analytics and visualization workshop using Python and generative AI assistance. The objective is to develop comprehensive and visually engaging financial analysis under tight time constraints. Unlike traditional case studies, this case explicitly integrates AI-supported workflows as a core competency, enabling learners to leverage large language models to accelerate Python coding, visualization, and statistical analysis. The role-play is activated through an email sent by a senior manager (the instructor) that outlines detailed tasks for workshop preparation. The case is designed for individual work but encourages students to consult open-access sources and interact with AI tools to facilitate their problem-solving process. Instructors provide on-demand feedback and support as students make progress through the tasks. This dynamic learning experience fosters technical autonomy, critical thinking, and the ability to navigate Python environments efficiently with AI assistance while developing essential skills in financial data storytelling and time-sensitive project delivery.

Date: 2025-07-19
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Published in 2025

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05362749

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