Visualizing data, particularly in sports analytics, provides valuable insights for informed decision-making, reveals underlying patterns in the data, and enhances communication among stakeholders. Leveraging Python’s versatility and powerful data visualization libraries enables the creation of well-crafted visual narratives across diverse domains. Matplotlib, Python's most popular visualization package, offers extensive customization options and precise control, making it a preferred tool for crafting detailed and impactful visualizations. This workshop will introduce Matplotlib’s robust plotting capabilities, showcase practical examples of data visualizations in baseball and basketball, and equip participants with versatile techniques applicable across any domain.

Prerequisites
Familiarity with Python and Jupyter Notebooks. Recommended: familiarity with Pandas. Please visit GitHub repository for more information.

Training Materials
On GitHub: https://github.com/ram200010/CSAS_2025_Data_Visualization

Rahul Manna is a junior pursuing a dual degree in Statistical Data Science and Mechanical Engineering. He is currently working as a research assistant in the Laboratory for Advanced Manufacturing Reliability (KKim Lab), where he tests materials for implantable bioelectronics and uses Python and Matplotlib to analyze and visualize data. Outside the classroom and workspace, Rahul enjoys Formula One, where the fusion of cutting-edge engineering, advanced statistics, data science, analytics, and human ingenuity drives both the on-track performances and the strategic decisions behind the scenes.