This ten-session experiential learning workshop is designed for students/staff/postdocs across all disciplines who aim to develop foundational and advanced competencies in data analysis using Python and Artificial Intelligence (AI) tools. In an era where data is pivotal to research and innovation, this series empowers participants to harness the capabilities of Python, a versatile and widely adopted programming language, for effective data manipulation, insightful visualization, and robust statistical analysis (McKinney, 2023; VanderPlas, 2016). The curriculum progressively introduces core data science libraries like Pandas, NumPy, Matplotlib, and Scikit-learn, ensuring a solid understanding of the entire data analysis workflow.
Beyond traditional methods, the workshop delves into the transformative potential of AI, demystifying machine learning concepts and providing hands-on experience with predictive modeling. A unique aspect of this series is the integration of modern AI tools, including an introduction to leveraging Large Language Models (LLMs) to augment analytical tasks, such as data cleaning, insight generation, and even assisting in code development.
The interdisciplinary nature of these skills is emphasized throughout, with examples and use cases drawn from diverse fields such as the natural and social sciences, engineering, humanities, and health sciences. Whether analyzing experimental results, textual corpora, survey data, or sensor outputs, participants will find the acquired skills directly applicable to their research. Furthermore, the workshop will touch upon scientific outreach opportunities, enabling students to better communicate their data-driven findings to broader audiences and contribute to open science initiatives. This practical, self-paced series aims to equip graduate students with the essential toolkit to confidently tackle complex data challenges and enhance their research impact.