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Announce the launch of Skolar for the scikit-learn community #217

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94 changes: 94 additions & 0 deletions _posts/2025-06-30-probabl-skolar.md
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---

#### Post Information ####
title: "Skolar: an open-source initiative to democratize open data science"
date: June 30, 2025

#### Post Category and Tags ####
# Format in titlecase without dashes (Ex. "Open Source" instead of "open-source")
categories:
- Updates
tags:
- Sponsor
- Open Source
- Machine Learning

#### Featured Image ####
featured-image: BSD_watermark.svg
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Is this the right image to use? It sounds like it is some default thing?

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To be honest I have no idea, it seems like a default setting but let me know if you think some other image would be more appropriate? Thanks

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I think you need to know what image you want in your post. From the name BSD_watermark.svg it sounds like this image is about the BSD license or some such, which is probably not a good feature image for this post.

What does the rendered blog post look like? Does an image appear or maybe an <img> tag that points to a non-existing image?


#### Author Info ####
# Can accomodate multiple authors
# Add SQUARE Author Image to /assets/images/author_images/ folder
postauthors:
- name: Skolar
website: https://skolar.probabl.ai/
image: "skolar-logo.png"
- name: Pénélope Gittos
website: https://www.linkedin.com/in/gittospenelope-data-analyst-growth-bilingual/
image: "penelope_gittos.jpeg"
---
<div>
<img src="/assets/images/posts_images/{{ page.featured-image }}" alt="">
{% include postauthor.html %}
</div>

<span style="color:red">*This blog post has been submitted by Probabl, a sponsor of scikit-learn.* </span>
The scikit-learn project values educational efforts that build and nurture a
strong vibrant open-source community. The goal of this is straightforward: give
everyone, everywhere, the tools they need to easily grasp, engage with, and
meaningfully contribute to data science using open-source software. This mission
is shared and actively supported by [Probabl](https://probabl.ai/), a company
that helps maintain scikit-learn by employing many of its core contributors and
investing in its long-term sustainability. With Probabl's support and a deep
commitment from the community, the scikit-learn ecosystem continues building bridges between research,
software, and education.

When the [Inria scikit-learn MOOC](https://inria.github.io/scikit-learn-mooc/)
(Massive Open Online Course) first went live, the community got a front-row seat
to the amazing impact of practical, accessible and open learning. Created by
several core developers and maintainers of scikit-learn—now working at
Probabl—the MOOC has reached over 40,000 learners worldwide, clearly
highlighting the demand for organized, hands-on resources that blend theory with
real-world practice.

Today, Probabl is excited to introduce
[Skolar](https://app.arcade.software/share/vCN6ik9dR22zD35XP5a7), a new, fully
open-source educational initiative, built directly from your feedback and all
the lessons we've learned along the way. Developed and extended by those same
core developers of scikit-learn, Skolar is designed specifically for data
science practitioners, offering hands-on, high-quality learning resources
grounded in real-world applications and open-source values.

Skolar exists to boost our shared values: openness, teamwork, and practicality.
It offers clear, interactive tutorials and structured courses carefully designed
to match industry challenges and specialized use-cases. But even more
importantly, it captures the true spirit of open source: encouraging
collaboration, peer-to-peer learning, and guidance from experts.

Right now, we’re just at the beginning. Today, you can dive into our
Scikit-learn Associate Practitioner online course, adapted from the popular
Inria MOOC but enhanced with new material on unsupervised learning, especially
clustering.

The next stages, professional and expert levels, will be released soon. We'll
also add more courses covering other open-source libraries such as
[skrub](https://skrub-data.org) (for data wrangling),
[hazardous](https://soda-inria.github.io/hazardous/) (for survival analysis),
and [fairlearn](https://fairlearn.org/) (for fairness).
Additionally, our scikit-learn team is planning to create industry-specific
modules tackling real-world needs in fields like healthcare, finance, medicine,
and beyond.

At its core, Skolar is about empowering people through education, driven
entirely by our passion for openness and collaboration. We firmly believe that
true open data science begins with community-built learning resources. We warmly
welcome you, whether you're a contributor, learner, teacher, or just someone
curious, to join us. Help shape Skolar’s future and support open-source
education in data science.

Create your account on Skolar today: https://skolar.probabl.ai

Contribute to the [scikit-learn course
contents](https://github.com/probabl-ai/scikit-learn-course), or contribute to
the learning platform's [backend](https://github.com/France-ioi/AlgoreaBackend)
or [frontend](https://github.com/France-ioi/AlgoreaFrontend).
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