Jake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data.
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- Your host as usual is Tobias Macey and today I’m interviewing Jake Vanderplas about data science best practices, and applying them to academic sciences
- How did you get introduced to Python?
- How has your astronomy background informed and influenced your current work?
- In your work at the University of Washington, what are some of the most common difficulties that students face when learning data science?
- How does that list differ for professional scientists who are learning how to apply data science to their work?
- Where is the tooling still lacking in terms of enabling consistent and repeatable workflows?
- One of the projects that you are spending time on now is Altair, which is a library for generating visualizations from Pandas dataframes. How does that work factor into your teaching?
- What are some of the most novel applications of data science that you have been involved with?
- What are some of the trends in data analysis that you are most excited for?
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