Growing And Supporting The Data Science Community At Anaconda

Summary 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. Announcements Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science. When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! 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 Interview Introductions 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? Keep In Touch LinkedIn @KevinGoldsmith on Twitter Website Picks Tobias Perdido Street Station The Scar Iron Council Kevin Lego Typewriter Closing Announcements Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at pythonpodcast.com/chat Links Anaconda Spotify Lisp Scheme C# Anaconda Nucleus PyData AnacondaCon Grid Computing PyTorch Podcast Episode Tensorflow Pyston Podcast Episode Dask Podcast Episode Numba Panel dashboard framework Datashader Jupyter R Julia AstroPy Podcast Episode Arrow Data Teams by Jesse Anderson Podcast Episode The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA