What we’re about
PyData Pittsburgh is a community for data scientists, machine learning practitioners, and all professionals, students, researchers, and enthusiasts working with Python and data in Pittsburgh. Pittsburgh is an emerging tech hub, with world-class research universities, outposts of major technology companies, a dynamic ecosystem of homegrown startups, and a burgeoning robotics sector. Let's connect these dots to share ideas, learn from each other, and grow the local technology community.
Our members include researchers and tech professionals with decades of experience, novices who have yet to write their first line of code, and everyone in between. If you're interested in learning more about amazing, cutting-edge work happening with Python, data, and related technologies in Pittsburgh, you're in the right place, and you'll find a welcoming, supportive community of like-minded folks.
Have an idea for a future PyData Pittsburgh event? Fill out our Call for Proposals form and a member of our organizing team will get back to you!
Meetup is the primary place we post our events, but you can also find and connect with us on:
- Email & Web: https://news.pypgh.org
- Mastodon: https://pypgh.org/mastodon
- X/Twitter: https://pypgh.org/twitter
- LinkedIn: https://pypgh.org/linkedin
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PyData Pittsburgh is also a node in the larger PyData network. 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.
The PyData Code of Conduct governs this meetup. To discuss any issues or concerns relating to the code of conduct or the behavior of anyone at a PyData meetup, please contact the local group organizers (message us on the meetup page). Please also submit a report of any potential Code of Conduct violation directly to NumFOCUS. Thank you for helping us to maintain a welcoming and friendly PyData community!
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Need to get in touch with the PyData Pittsburgh organizing team? You can reach us at [email protected].
Upcoming events (1)
See all- Data science for mental health crisesCode & Supply Coworking, Pittsburgh, PA
Please join PyData Pittsburgh for the talk Data Science for Mental Health Crises by Pim Welle, Chief Data Scientist at the Allegheny County Department of Human Services! We'll be gathering in person at the Code & Supply Workspace.
Please note that the date for this event has changed. The event will now take place on Wednesday, November 20, not Wednesday, November 13.
About the talk
In this talk, you'll hear how cutting-edge data science techniques can be used in government settings. By making use of machine learning / predictive analytics, survival modeling, and non-experimental causal inference, the Allegheny County Department of Human Services (DHS) did a complete profile of its mental health system, including a deep dive into involuntary commitments (sometimes called 302s).
The results of the analysis were staggering. This work led to a report and upcoming academic publication, which found that (1) 302s are very common, affecting 350 per 100k individuals (roughly in line with prison sentencing), (2) folks have very poor outcomes post 302, with 20% of the population passing away in 5 years and (3) the 302 population is very expensive - we spend 25% of our Medicaid behavioral health funding on 2% of the population. Moreover, using machine learning, we can detect the individuals who are likely to have poor outcomes from the moment they step foot in an inpatient unit. Lastly, the upcoming academic work highlights non-experimental causal inference techniques to show whether the commitment itself is helping or hurting downstream outcomes.
The resulting analysis is a use case that demonstrates how statistics, machine learning, and causal inference can all come together to help understand how our public systems are working.
How to find us
Attendees are welcome to use the parking lot associated with the Code & Supply building off St. Clair Street. The front door on Friendship Avenue will be open but is stairs-only. There's an elevator by the parking lot entrance. Head to the third floor and look for signs pointing to the presentation room, where the event will be held. All doors should be unlocked and open, so you're welcome to come right in!
Learn more about PyData Pittsburgh and subscribe to announcements and event updates at https://news.pypgh.org.
