Data scientists are tasked with answering challenging questions using data that is often messy and incomplete. Anaconda is on a mission to make the lives of data professionals more manageable through creation and maintenance of high quality libraries and frameworks, the distribution of an easy to use Python distribution and package ecosystem, and high quality training material. In this episode Kevin Goldsmith, CTO of Anaconda, discusses the technical and social challenges faced by data scientists, the ways that the Python ecosystem has evolved to help address those difficulties, and how Anaconda is engaging with the community to provide high quality tools and education for this constantly changing practice.
- Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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- Your host as usual is Tobias Macey and today I’m interviewing Kevin Goldsmith about Anaconda’s contributions to the Python ecosystem for data science
- How did you get introduced to Python?
- Can you start by describing what Anaconda focuses on solving for?
- What was your path into the CTO position?
- From your perspective as the CTO of Anaconda, what are the biggest challenges facing data scientists today?
- What is the breakdown between technical and organizational sources for those difficulties?
- How is the Anaconda product suite architected to help address some of those problems?
- Where are you spending your focus to allow Anaconda to address the current and future needs of data scientists?
- Python has been a dominant force in the data and analytics ecosystem for several years now. What do you see as the future of the space? (e.g. monoglot vs. polyglot workflows)
- What are the most interesting, innovative, or unexpected ways that you have seen the Anaconda platform used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anaconda and data science tooling?
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