Podcast appearances and mentions of jake vanderplas

Hugo speaks with Dr. Chelle Gentemann, Open Science Program Scientist for NASA's Office of the Chief Science Data Officer, about NASA's ambitious efforts to integrate AI across the research lifecycle. In this episode, we'll dive deeper into how AI is transforming NASA's approach to science, making data more accessible and advancing open science practices. We explore Measuring the Impact of Open Science: How NASA is developing new metrics to evaluate the effectiveness of open science, moving beyond traditional publication-based assessments. The Process of Scientific Discovery: Insights into the collaborative nature of research and how breakthroughs are achieved at NASA. ** AI Applications in NASA's Science:** From rats in space to exploring the origins of the universe, we cover how AI is being applied across NASA's divisions to improve data accessibility and analysis. Addressing Challenges in Open Science: The complexities of implementing open science within government agencies and research environments. Reforming Incentive Systems: How NASA is reconsidering traditional metrics like publications and citations, and starting to recognize contributions such as software development and data sharing. The Future of Open Science: How open science is shaping the future of research, fostering interdisciplinary collaboration, and increasing accessibility. This conversation offers valuable insights for researchers, data scientists, and those interested in the practical applications of AI and open science. Join us as we discuss how NASA is working to make science more collaborative, reproducible, and impactful. LINKS The livestream on YouTube (https://youtube.com/live/VJDg3ZbkNOE?feature=share) NASA's Open Science 101 course

At PyCon 2017, Jake Vanderplas gave a great keynote where he said, "Python is a mosaic." He described how Python is stronger and growing because it's being adopted and used by people with diverse technical backgrounds. In this episode, we're adding to that mosaic by diving into how Python is being used in the architecture, engineering, and construction industry. Our guest, Gui Talarico, has worked as an architect who help automate that world by bringing Python to solve problems others were just doing by point-and-click tooling. I think you'll enjoy this look into that world. We also touch on his project pyairtable near the end as well. Links from the show Pyninsula Python in Architecture Talk: youtube.com Using technology to scale building design processes at WeWork talk: youtube.com Revit software: autodesk.com Creating a command in pyRevit: notion.so IronPython: ironpython.net Python.NET: github.com revitpythonwrapper: readthedocs.io aec.works site: aec.works Speckle: speckle.systems Ladybug Tools: ladybug.tools Airtable: airtable.com PyAirtable: pyairtable.readthedocs.io PyAirtable ORM: pyairtable.readthedocs.io Revitron: github.com WeWork: wework.com Article: Using Airtable as a Content Backend: medium.com Python is a Mosaic Talk: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training

Twitter: https://twitter.com/pgbovineSupport with PayPal, Patreon, credit/debit: http://pgbovine.net/support.htmhttp://pgbovine.net/PG-Podcast-49-Srini-Kadamati.htm- [Dataquest: Learn Data Science](https://www.dataquest.io/)- [Concord Consortium](https://concord.org/)- [Older Adults Learning Computer Programming: Motivations, Frustrations, and Design Opportunities](http://pgbovine.net/older-adults-learning-programming-paper.htm)- [Non-Native English Speakers Learning Computer Programming: Barriers, Desires, and Design Opportunities](http://pgbovine.net/non-native-english-speakers-learning-programming-paper.htm)- [Explorable Explanations](https://explorabl.es/)- [Observable](https://observablehq.com/)- [idyll: a toolkit for creating data-driven stories and explorable explanations](https://idyll-lang.org/)- [Explorable Multiverse Analyses](https://explorablemultiverse.github.io/)- [Crowd Research Initiative](http://crowdresearchinitiative.stanford.edu/)- [Vineet Pandey's research](https://vineetp13.github.io/)- [Installing Python Packages from a Jupyter Notebook](https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/) by Jake VanderPlas- [eBird - Discover a new world of birding...](https://ebird.org/home)- [Nadia Eghbal | The independent researcher](https://nadiaeghbal.com/independent-research)- [Data Stories 134 | Visualizing Uncertainty with Jessica Hullman and Matthew Kay](http://datastori.es/134-visualizing-uncertainty-with-jessica-hullman-and-matthew-kay/)- [Video Digests: A Browsable, Skimmable Format for Informational Lecture Videos](http://vis.berkeley.edu/papers/videodigests/)- [Future of Coding Podcast - Learning Programming At Scale: Philip Guo](https://futureofcoding.org/episodes/022)- [Reading Entire Conference Proceedings](http://pgbovine.net/reading-conference-proceedings.htm)- [DS.js: Turn Any Webpage into an Example-Centric Live Programming Environment for Learning Data Science](http://www.pgbovine.net/dsjs-paper.htm)Recorded: 2019-05-09 (2)

Jake VanderPlas, a data science fellow at the University of Washington's eScience Institute, astronomer, open source beast and renowned Pythonista, joins Hugo to speak about data science, astronomy, the open source development world and the importance of interdisciplinary conversations to data science.

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.

Support these videos: http://pgbovine.net/support.htmhttp://pgbovine.net/PG-Podcast-23-Jake-VanderPlas.htmRecorded: 2017-02-13

Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for The post Python Data Visualization with Jake VanderPlas appeared first on Software Engineering Daily.

Our guest is Jake VanderPlas, who is a real Data Science Fellow at the UW eScience Institute, and is the author of the recently published Python Data Science Handbook.  Topics discussed include BiCapitalization bridging the astronomy-astrology divide whether the e in eScience is the same as the e in eBay Myers-Briggs why Jake keeps saying “transcriptable” like it's a real word whether newsletters have replaced blogs how to talk to people at parties what it’s like being a data scientist in academia whether students still hook up in the library why your zodiac sign isn’t what you think it is whether Pluto is a planet and why people care teaching statistics using simulation how to pronounce "numpy" using deep learning to identify new constellations building an AI that chooses the right data visualization This week's theme music has a cool Vegas swanky lounge vibe.  Please listen to it.

There's an old adage which says you cannot fit a model which has more parameters than you have data. While this is often the case, it's not a universal truth. Today's guest Jake VanderPlas explains this topic in detail and provides some excellent examples of when it holds and doesn't. Some excellent visuals articulating the points can be found on Jake's blog Pythonic Perambulations, specifically on his post The Model Complexity Myth. We also touch on Jake's work as an astronomer, his noteworthy open source contributions, and forthcoming book (currently available in an Early Edition) Python Data Science Handbook.

There's an old adage which says you cannot fit a model which has more parameters than you have data. While this is often the case, it's not a universal truth. Today's guest Jake VanderPlas explains this topic in detail and provides some excellent examples of when it holds and doesn't. Some excellent visuals articulating the points can be found on Jake's blog Pythonic Perambulations, specifically on his post The Model Complexity Myth. We also touch on Jake's work as an astronomer, his noteworthy open source contributions, and forthcoming book (currently available in an Early Edition) Python Data Science Handbook.