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 organizers@pypgh.org.
Upcoming events (1)
See all- September Event: Decoding Spatial Biology with PythonSwartz Center for Entrepreneurship, Pittsburgh, PA
PyData Pittsburgh is excited to host our September event: Decoding Spatial Biology with Python: Multi-Modal Insights into Breast Cancer Progression. Join us on Tuesday, September 30th, as Alex C. Chang, CMU-Pitt (Graduate Student PhD, Computational Biology) and Brent Schlegel, University of Pittsburgh School of Medicine (Graduate Student PhD, Integrative Systems Biology), present their novel tool, CITEgeist, which harnesses Python’s capabilities for multi-modal spatial transcriptomics.
Python has rapidly become a cornerstone of scientific computing, computational biology, and bioinformatics due to its ease of use and scalability for handling large datasets—qualities that are critical in today’s “big data” era of clinical and translational research. As computational resources and data collection methods continue to expand, we are now empowered to ask larger and more clinically relevant questions that enable us to dissect complex biological systems with unprecedented detail.
However, this surge in data complexity brings new challenges, from the integration of diverse data modalities to the need for sophisticated analytical methods capable of untangling intricate biological signals from background noise.
About the talk:
In this talk, Alex and Brent describe how Python not only meets these challenges but also drives innovation through the development of novel bioinformatics tools like CITEgeist. Biological datasets often face challenges of high sparsity and noise. CITEgeist harnesses Python’s robust ecosystem to provide an efficient, scalable pipeline that deconvolutes messy spatial signals into actionable, clinically relevant features.
Time:
5:30pm – Doors Open
6:00pm - 7:30pm – Talk and Q&A, Decoding Spatial Biology with Python: Multi-Modal Insights into Breast Cancer ProgressionGetting to the event:
The Swartz Center for Entrepreneurship is located within the Tepper School of Business on the Carnegie Mellon University campus. After you arrive at the Tepper Quad, enter the main doors of the Tepper School building (on floor 2). The Swartz Center for Entrepreneurship is located on the third level (on floor 3) in Suite 3700.
Parking is available in the East Campus Garage on Forbes and Beeler Streets.
For more information about directions and parking, please see here.
About the speakers:
Alex C. Chang
Alexander Chih-Chieh Chang is a fourth-year MSTP student in the CMU-Pitt Computational Biology Ph.D. Program, mentored by Drs. Lee and Oesterreich. He earned a BS/BA in Chemical and Biomolecular Engineering/Sociology from Johns Hopkins University in 2021. Previously, during his undergraduate research in the lab of Rong Li, Ph.D., he conducted large-scale genomic screens to study proteomic dysregulation and spent a gap year in the lab of Manish Aghi, MD. PhD., studying breast cancer metastasis to the brain. Currently, as a computational biologist and medical student, he coordinates the Hope for OTHERS tissue donation program in the Lee-Oesterreich Lab and computational research projects in breast cancer metastasis and genomic evolution.Brent Schlegel
Brent Schlegel is a first-year PhD student in Integrative Systems Biology at the University of Pittsburgh School of Medicine, co-mentored by Drs. Adrian Lee and Steffi Oesterreich. He earned his AS in Mathematics and Sciences from CCAC (2019) and a BS in Computational Biology from Pitt (2021). Most recently, he worked as a Bioinformatics Analyst at the UPMC Children’s Hospital of Pittsburgh, where he specialized in the integrative analysis of large, complex biomedical datasets. Now, Brent combines data science, computational modeling, and multi-omic integration to tackle the systems biology of invasive lobular breast cancer, using patient-derived organoid models and leveraging “big data” to uncover hidden patterns and drive innovation in diagnosis and treatment.✨✅ Take the PyData Pittsburgh Member Survey!
Thanks for being part of our community! This quick 5-minute survey will help shape future PyData Pittsburgh events. Take the survey HERE.
