Bayesian Modeling and Computation in Python
Learn to do data science and Bayesian analysis with the open source python library PyMC.
Learn to do data science and Bayesian analysis with the open source python library PyMC.
Learn to set up Jekyll based website and blog and host it with GitHub Pages
Tiffany shares about her background in career coaching
Discussion with Mariatta on what makes a community
Lauren Burke shares her path on giving her first solo presentation and some tips for first-time speakers.
Tiffany shares about her background in career coaching
Learn to optimize pytorch model and deploy them into production
Rami Krispin - This talk focuses on a holistic approach for deploying data science projects into production (e.g., CI/CD) with the use of open-source and free tools such as R, bash, Docker, Github Actions, etc.
This post summarizes conversations surrounding these topics and provides resources to help you develop an impact strategy for your next event.
Use Python to code a linked list and use graphs for efficiency.
Learn to set up Jekyll based website and blog and host it with GitHub Pages
Discussion with Mariatta on what makes a community
Summary of the PyMC Open Source Working Session, March 2023
The event board project is looking for donors and contributors.
Summary of the PyMC Open Source Working Sessions, Jul-Aug 2022
PyMC & Data Umbrella share why and how the sprint was organized and outcomes.
PyCon DE & PyData Berlin 2022 Keynote
Sandra Meneses shares her experience at the Data Umbrella PyMC Sprint.
Maren Westermann shares her learnings from taking part in Data Umbrella scikit-learn sprints.
Learn the tips for making the online sprints a success.
Juan Martín Loyola shares his experience collaborating in open source.
In this example, learn to refactor code and add tests in scikit-learn.
Beryl Kanali shares her experience submitting a pull request to scikit-learn for the first time.
Nestor Navarro shares his experience in Data Umbrella 2021 Latin America scikit-learn sprint.
Reshama Shaikh - Summary of the Africa & Middle East Sprint, October 2021
Introduction to the Visual Studio Code extension Live Share which allows you to edit snippets collaboratively.
Sebastian Andres shares his experience submitting a pull request to scikit-learn for the first time.
Reshama Shaikh - Summary of the Latin America Sprint, June 2021
Reshama Shaikh - Summary of the Africa & Middle East, February 2021
Fortune Uwha shares her experience contributing to to scikit-learn for the first time.
Reshama Shaikh - Summary of the Global Online Sprint, June 2020
Maren Westerman from Germany shares her experience contributing to open source.
Cynthia Thinwa from Kenya shares her experience contributing to open source.
Jake Tae shares his experience contributing to open source.
Joe Lucas shares his experience contributing to scikit-learn for the first time.
Bruno Gonçalves - Explains a broad range of traditional machine learning (ML) and deep learning techniques to model and analyze time series datasets with an emphasis on practical applications.
CZI grant awarded to Data Umbrella to support diversity and inclusion in computational science.
Summary of the PyMC Open Source Working Session, March 2023
Summary of the PyMC Open Source Working Sessions, Jul-Aug 2022
PyMC & Data Umbrella share why and how the sprint was organized and outcomes.
Sandra Meneses shares her experience at the Data Umbrella PyMC Sprint.
Learn to do data science and Bayesian analysis with the open source python library PyMC.
Use Python to code a linked list and use graphs for efficiency.
Learn about GitHub Actions and how they can accelerate your application development workflows.
Learn how open source probabilistic programming makes Bayesian inference algorithms near the frontier of academic research accessible to a wide audience.
In this example, learn to refactor code and add tests in scikit-learn.
Introduction to the Visual Studio Code extension Live Share which allows you to edit snippets collaboratively.
Bruno Gonçalves - Explains a broad range of traditional machine learning (ML) and deep learning techniques to model and analyze time series datasets with an emphasis on practical applications.
Hugo Bowne-Anderson - Explains Bayesian data science through the lens of simulation.
Learn to optimize pytorch model and deploy them into production
What is the difference between Open Collective Foundation and Open Source Collective?
The event board project is looking for donors and contributors.
We provide a helpful resource to set key milestones and due dates leading up to an event.
Rami Krispin - This talk focuses on a holistic approach for deploying data science projects into production (e.g., CI/CD) with the use of open-source and free tools such as R, bash, Docker, Github Actions, etc.
An introduction to Rust Programming for complete beginners
PyCon DE & PyData Berlin 2022 Keynote
Learn the tips for making the online sprints a success.
In this example, learn to refactor code and add tests in scikit-learn.
Beryl Kanali shares her experience submitting a pull request to scikit-learn for the first time.
Nestor Navarro shares his experience in Data Umbrella 2021 Latin America scikit-learn sprint.
Sebastian Andres shares his experience submitting a pull request to scikit-learn for the first time.
Fortune Uwha shares her experience contributing to to scikit-learn for the first time.
Maren Westerman from Germany shares her experience contributing to open source.
Cynthia Thinwa from Kenya shares her experience contributing to open source.
Jake Tae shares his experience contributing to open source.
Joe Lucas shares his experience contributing to scikit-learn for the first time.
Summary of the PyMC Open Source Working Session, March 2023
Summary of the PyMC Open Source Working Sessions, Jul-Aug 2022
Reshama Shaikh - Summary of the Africa & Middle East Sprint, October 2021
Reshama Shaikh - Summary of the Latin America Sprint, June 2021
Reshama Shaikh - Summary of the Africa & Middle East, February 2021
Reshama Shaikh - Summary of the Global Online Sprint, June 2020
Bruno Gonçalves - Explains a broad range of traditional machine learning (ML) and deep learning techniques to model and analyze time series datasets with an emphasis on practical applications.
Introduction to the Visual Studio Code extension Live Share which allows you to edit snippets collaboratively.