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THIS IS PAID EVENT. PRE-REGISTRATION IS REQUIRED. RSVP here - https://lu.ma/lqgq5hi2 Use code CommunityEast2025 for an extra discount.

On its 10th anniversary, ODSC East will feature 250+ speakers, instructors, and practitioners, 2,500 of your fellow data and AI practitioners, and hands-on instruction for world-class experts in:

AI Agents \| Generative AI \| Large Language Models \| AI Engineering \| Machine Learning \| Deep Learning \| MLOps and LLMOps \| AI for Biotech\, Pharma\, and Life Sciences \| Data Engineering \| Data Visualization and Analysis \| and much more.

More details about networking, learnings and schedule you may find here - https://hubs.li/Q02YK3hT0

First confirmed speakers: - Ryan Harter\, Senior Staff Data Scientist at Shopify - Allen Downey\, PhD\, Curriculum Designer \| Professor Emeritus at Brilliant.org \| Olin College - Dr. Jon Krohn\, Chief Data Scientist at Nebula.io - Suman Debnath\, Principal AI/ML Advocate at Amazon Web Services - Andras Zsom\, PhD\, Assistant Professor of the Practice, Director of Graduate Studies at Data Science Institute, Brown University - Rajiv Shah\, PhD\, Machine Learning Engineer at Contextual AI - Peter Henstock\, PhD\, Senior Machine Learning Engineer at Incyte - Noah Giansiracusa\, PhD\, Associate Professor of Mathematics and Data Science at Bentley University - Stella Biderman\, Executive Director\, AI and NLP Researcher \| Lead Scientist - Mathematician at Eleuther AI \| Booz Allen Hamilton - Edward Donner\, Co-founder and CTO at Nebula.io - Chen Wang\, Lead Alliance Solution Architect at AI21 Labs - Sinan Ozdemir\, AI & LLM Expert \| Author \| Founder + CTO at LoopGenius ***- and more - ***https://hubs.li/Q02YK3hT0

🔴 Hands-On Learning: No fluff, no superficial talks – just in-depth workshops, training sessions, and expert-led discussions. 🔴 In-Person Connections: Meet peers, network with industry leaders, and gain actionable insights face-to-face. 🔴 NEW World-Class Venue: Experience it all at the Boston Convention & Exhibition Center, in the heart of the Seaport District.

• “ODSC remains the go-to event for AI and data science practitioners. Complete advanced-level training, engaging and relevant workshops, and exclusive perspectives from thought leaders in the fields.”

– Mansi More \| AI Engineer \| OurDate

• “Open Data Science Conference (ODSC) West 2024 in San Francisco was amazing! So much talent and leadership on display, and really, just a lot of fun.”

– John Ryan Kivela\, MA \| Director of Healthcare Analytics \| Northern Arizona Regional Behavioral Health Alliance ODSC Links:

Useful links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/

THIS IS PAID EVENT. PRE-REGISTRATION IS REQUIRED. RSVP here - https://lu.ma/lqgq5hi2 Use code CommunityEast2025 for an extra discount.

On its 10th anniversary, ODSC East will feature 250+ speakers, instructors, and practitioners, 2,500 of your fellow data and AI practitioners, and hands-on instruction for world-class experts in:

AI Agents \| Generative AI \| Large Language Models \| AI Engineering \| Machine Learning \| Deep Learning \| MLOps and LLMOps \| AI for Biotech\, Pharma\, and Life Sciences \| Data Engineering \| Data Visualization and Analysis \| and much more.

More details about networking, learnings and schedule you may find here - https://hubs.li/Q02YK3hT0

First confirmed speakers: - Ryan Harter\, Senior Staff Data Scientist at Shopify - Allen Downey\, PhD\, Curriculum Designer \| Professor Emeritus at Brilliant.org \| Olin College - Dr. Jon Krohn\, Chief Data Scientist at Nebula.io - Suman Debnath\, Principal AI/ML Advocate at Amazon Web Services - Andras Zsom\, PhD\, Assistant Professor of the Practice, Director of Graduate Studies at Data Science Institute, Brown University - Rajiv Shah\, PhD\, Machine Learning Engineer at Contextual AI - Peter Henstock\, PhD\, Senior Machine Learning Engineer at Incyte - Noah Giansiracusa\, PhD\, Associate Professor of Mathematics and Data Science at Bentley University - Stella Biderman\, Executive Director\, AI and NLP Researcher \| Lead Scientist - Mathematician at Eleuther AI \| Booz Allen Hamilton - Edward Donner\, Co-founder and CTO at Nebula.io - Chen Wang\, Lead Alliance Solution Architect at AI21 Labs - Sinan Ozdemir\, AI & LLM Expert \| Author \| Founder + CTO at LoopGenius ***- and more - ***https://hubs.li/Q02YK3hT0

🔴 Hands-On Learning: No fluff, no superficial talks – just in-depth workshops, training sessions, and expert-led discussions. 🔴 In-Person Connections: Meet peers, network with industry leaders, and gain actionable insights face-to-face. 🔴 NEW World-Class Venue: Experience it all at the Boston Convention & Exhibition Center, in the heart of the Seaport District.

• “ODSC remains the go-to event for AI and data science practitioners. Complete advanced-level training, engaging and relevant workshops, and exclusive perspectives from thought leaders in the fields.”

– Mansi More \| AI Engineer \| OurDate

• “Open Data Science Conference (ODSC) West 2024 in San Francisco was amazing! So much talent and leadership on display, and really, just a lot of fun.”

– John Ryan Kivela\, MA \| Director of Healthcare Analytics \| Northern Arizona Regional Behavioral Health Alliance ODSC Links:

Useful links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02ZkDV90 • Code of conduct: https://odsc.com/code-of-conduct/

🎙️ Speaker: Allen B. Downey \| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin

If you commute by subway, you might have noticed that you can use the number of waiting passengers to predict the time until the next train. If there are fewer passengers than usual, you just missed a train and might have to wait longer. If there are more than usual, it's been a while since the last train, and you expect one soon. But if there are many more than usual, there might be a disruption of service and a long wait!

In this case study, we'll use PyMC to model this scenario. Starting simple, we'll demonstrate a process for developing and testing models incrementally, present some less commonly used PyMC features, and show how a Bayesian model can replicate Bayesian thinking.

Resources

We will assume that webinar participants are familiar with basic PyMC models and distributions like Normal, Poisson, and Gamma.

If you are not familiar with PyMC, you can start with this chapter from Think Bayes, especially the World Cup Problem: https://allendowney.github.io/ThinkBayes2/chap19.html

Or you can run that chapter on Colab https://colab.research.google.com/github/AllenDowney/ThinkBayes2/blob/master/notebooks/chap19_v3.ipynb

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Allen B. Downey Allen Downey is a Principal Data Scientist at PyMC Labs, professor emeritus at Olin College and the author of several books -- including Think Python, Think Bayes, and Probably Overthinking It -- and a blog about programming and data science. He received a Ph.D. in computer science from the University of California, Berkeley, and Bachelor's and Masters degrees from MIT.

🔗 Connect with Allen B. Downey: 👉 Linkedin: https://www.linkedin.com/in/allendowney/ 👉 Blog: https://www.allendowney.com/blog/ 👉 X: https://twitter.com/AllenDowney

💼 About the Host:

1. Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/

🎙️ Speaker: Allen Downey\, Ricardo Vieira \| ⏰ Time: 3 pm UTC / 7 am PT / 10 am ET / 4 pm Berlin

In many large surveys, not every respondent is asked every question, and not every respondent answers the questions they are asked. So how can we compare people who answer different sets of questions? One solution is to use item response theory (IRT) to impute missing responses -- and nothing goes better with IRT than Bayesian methods!

In this talk, we report the results of a friendly competition -- a bake-off -- between two approaches to this problem, one using grid algorithms and a simplified model, the other using PyMC and a more detailed model. We'll discuss the implementations, compare the results, and outline their pros and cons.

Content: 🎙️ Async Talk: https://www.youtube.com/watch?v=uyznG61myy0 📽️ Interview video: https://www.youtube.com/watch?v=rvIStfmP1NU 📝 Slides: https://tinyurl.com/bbo1123 👉 Discourse Post for more details and discussion: https://discourse.pymc.io/t/13403

📢 Note: This session is exclusively for Q&A. We kindly request you to watch the recording before joining the event. For additional information and further discussion, please refer to this Discourse post.

💼 About the Speaker:

1. Allen Downey Allen is a curriculum designer at Brilliant and Professor Emeritus at Olin College, and the author of Think Python, Think Bayes, Think Stats and other books related to computer science and data science. He writes a blog about Bayesian statistics and related topics called Probably Overthinking It. And he is working on a book, also called Probably Overthinking It, that will be published by University of Chicago Press in 2023. If you would like to get an occasional update about the book, please join my mailing list. Dr. Mahmood is a neuroscientist with a PhD from Brandeis University, where he investigated the neural coordination of taste. His research, initially using electrophysiology to probe brain region interactions, hints at a complex network processing flavors. His forthcoming studies aim to unravel this network further, exploring the directional flow of neural information and the impact of feedback mechanisms in taste perception.

🔗 Connect with Allen: 👉 Website: https://allendowney.substack.com/ 👉 LinkedIn: https://www.linkedin.com/in/allendowney/ 👉 Twitter: https://twitter.com/AllenDowney 👉 Mastodon: @[email protected]

1. Ricardo Vieira Ricardo Vieira is a PyMC developer and data scientist at PyMC Labs. He spent several years teaching himself Statistics and Computer Science at the expense of his official degrees in Psychology and Neuroscience.

🔗 Connect with Ricardo: 👉 Website: https://ricardov94.github.io/posts/ 👉 GitHub: https://github.com/ricardoV94

🤝 Sponsor We thank our sponsors for supporting PyMC and the PyMCon Web Series. If you would like to sponsor us, contact us for more information.

Adia Lab is an independent, Abu Dhabi-based laboratory dedicated to basic and applied research in data and computational sciences. ADIA Lab focuses on societally-important topics such as climate change and energy transition, blockchain technology, financial inclusion and investing, decision making, automation, cybersecurity, health sciences, education, telecommunications, and space, by conducting cutting-edge research in Data Science, Artificial Intelligence, Machine Learning, and High-Performance Computing.

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🌐 Connecting with PyMC 📺 PyMCon Web Series: https://pymcon.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc/ 🐦 Twitter: https://twitter.com/pymc_devs 🎥 YouTube: https://www.youtube.com/@pymc-devs 🤝 Meetup: https://www.meetup.com/pymc-online-meetup/ 🐘 Mastodon: https://bayes.club/@pymc

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems