Analyzing Rock Climbing Data in Python
Key Takeaways:- Learn how to perform exploratory data analysis using Polars and Plotly.
- Work through a real-world use case analyzing rock climbing data.
- Understand how to turn analysis results into insights and recommendations.
Description
Rock climbing offers a unique and data-rich environment for analysis, making it an ideal use case for learning modern data tools and techniques. By applying exploratory data analysis (EDA) with Python libraries like Polars and Plotly, data analysts can uncover patterns in performance, route difficulty, and progress over time—turning raw climbing data into meaningful insights.
In this hands-on code-along session, Arne Warnke, Head of Emerging Curriculum at DataCamp as well as a passionate rock climber,will guide you through a real-world project analyzing rock climbing data. You’ll learn how to use Polars for fast, efficient data manipulation and Plotly for interactive visualizations. The session will also show you how to derive insights and translate them into actionable advice. Perfect for data analysts looking to sharpen their skills, this session combines sports analytics with practical EDA techniques.
Presenter Bio
Arne heads the emerging curriculum team at DataCamp which focuses on new and emerging technologies and practices. Additionally, he is a guest lecturer at University of Heidelberg for data science and machine learning, he holds a PhD in economics & statistics and has climbed up to 7a+/5.12a outdoors.