In data science, speed matters as much as accuracy, especially when users expect quick results. This talk explores simple yet effective techniques to boost performance, using a real-life case of accelerating a Panel app. While some strategies are case-specific, most apply broadly to data-driven projects.
Pandas, Data Science, Panel
Performance is critical in data science—accuracy alone isn’t enough if applications are slow. Users expect both correct and fast results, and delays can lead to frustration, decreased productivity, and reduced trust in the tools. Whether in web apps, dashboards, or data pipelines, efficient processing is essential for user satisfaction and business success.
This talk explores simple yet effective techniques to optimize performance, focusing on faster data processing and analysis. Using a real-world case of improving a Panel app, we demonstrate strategies such as algorithm optimization and parallel processing to reduce bottlenecks and enhance speed.
While some methods are case-specific, most are broadly applicable across data science projects. These scalable solutions improve data handling and computation, enabling faster insights and real-time decision-making. By implementing these strategies, teams can significantly reduce latency and enhance user experience without compromising accuracy. Whether working on web apps, internal tools, or large-scale pipelines, these approaches help tackle performance challenges effectively.
Maximilian holds a Master's degree in Computer Science, earned in 2023, with a focus on data analytics. He has been an active participant in data analytics communities since 2020 and works as a software engineer at UL Solutions, applying his expertise to drive business outcomes. To share his practical knowledge, he also develops and leads data analytics training programs.