Channel: PyData

Category: Science & Technology

Tags: pythonlearn to codeeducationsoftwarepydatalearncodinghow to programjuliaopensourcescientific programmingnumfocuspython 3tutorial

Description: Abstract: Bayesian modeling is currently undergoing a Renaissance. Better and more user-friendly tools, as well as algorithms, allow this technique to be used by more people on larger and more complicated problems. While academia has already applied these powerful tools for research for a while, more and more businesses, frustrated by the empty promises of uninterpretable machine learning, are realizing the impact these more transparent methods can have. In this session, I will give a state of the art of probabilistic programming followed by a Q&A session. Bio: Thomas Wiecki is an author of the PyMC library and founder of PyMC Labs, a Bayesian consultancy solving advanced data science problems. He did his PhD at Brown University building computational models of the brain. PyData Global 2021 Website: pydata.org/global2021 LinkedIn: linkedin.com/company/pydata-global Twitter: twitter.com/PyData pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: github.com/numfocus/YouTubeVideoTimestamps