diff --git a/blog/_posts/2026-09-21-modern-root-training.md b/blog/_posts/2026-09-21-modern-root-training.md new file mode 100644 index 00000000..237eaf87 --- /dev/null +++ b/blog/_posts/2026-09-21-modern-root-training.md @@ -0,0 +1,23 @@ +--- +title: "A successful ROOT advanced training in Bologna" +layout: archive +author: Danilo Piparo +--- + +Last week, September 16th-18th 2026, the INFN Sezione di Bologna (Dipartimento di Fisica e Astronomia) organized and hosted the first *"Modern ROOT workshop"* training event. The ROOT project was invited to give lectures and hands-on exercises to students, post-docs and scientists attending both in Bologna (Italy) and online. + +![][image1] + +Organized by INFN and the University of Bologna, this three-day hands-on ROOT course explored the latest paradigms in ROOT, focusing on high-performance data analysis as the community prepares for the High-Luminosity LHC (HL-LHC) and next-generation physics experiments. + +The event was organized in three main sessions. The [ROOT advanced Python course](https://github.com/root-project/ROOTAdvancedCourse) started the training, featuring core data analysis ROOT functionality in Python with an added deep-dive on statistical inference and AD (automatic differentiation). Later on, the participants saw for the first time the brand-new [ROOT C++ course](https://github.com/root-project/root-cpp-course). On the last day, there was a dedicated "inverted" session: participants took center stage to showcase their own ongoing projects, analysis pipelines, and custom software tools built with ROOT. + +During these interactive presentations, attendees received direct code reviews, performance tuning advice, and architecture suggestions from members of the ROOT core development team. The session sparked valuable discussions on real-world edge cases, feature requests, and workflow optimizations. + +👉[Access the training event agenda on Indico](https://agenda.infn.it/event/52834) + +### **Thank You\!** + +A warm thank you to our local chairs, **Carlo Battilana** and **Tommaso Diotalevi**, the INFN Bologna team, the instructors, and all participants—both on-site in Bologna and connected remotely—for making this event a success. + +[image1]: \ No newline at end of file diff --git a/learn/index.md b/learn/index.md index 4f5ab565..c66d2e12 100644 --- a/learn/index.md +++ b/learn/index.md @@ -9,9 +9,10 @@ Planning to learn more about ROOT? This is the right page! We offer ROOT courses and exercises, in Python based on Jupyter Notebooks. For these courses, you don't need to install ROOT on your machine. You can directly run all the examples and exercises on [SWAN](https://swan.cern.ch){:target="_blank"} (if you have a CERN computing account), or otherwise using GitHub Codespaces or Binder. -Two levels are available: -* Beginners: [ROOT introductory course](https://github.com/root-project/student-course){:target="_blank"}, which can be accompanied by [its the video version](https://videos.cern.ch/record/2301866). This course covers the ROOT basics. -* Intermediate: [ROOT Advanced Course](https://github.com/root-project/ROOTAdvancedCourse/tree/26.04), which can be accompanied by [its the video version](https://videos.cern.ch/record/3025341). This course is about RNTuple, UHI, RooFit, Pythonizations and some more advanced RDataFrame features +Three levels are available: +* Beginners: [ROOT Introductory Python Course](https://github.com/root-project/student-course){:target="_blank"}, which can be accompanied by [its the video version](https://videos.cern.ch/record/2301866). This course covers the ROOT basics. +* Intermediate: [ROOT Advanced Python Course](https://github.com/root-project/ROOTAdvancedCourse/tree/26.04), which can be accompanied by [its the video version](https://videos.cern.ch/record/3025341). This course is about RNTuple, UHI, RooFit, Pythonizations and some more advanced RDataFrame features +* Advanced: [ROOT Advanced C++ Course](https://github.com/root-project/root-cpp-course/tree/26.09). This course is fully in C++ and covers aspects such as using basic ROOT objects, dealing with RDataFrame using only compiled code, dataset schema with custom classes using ROOT dictionaries and writing it to TTree and RNTuple datasets, likelihood evaluation with RooFit, as well as parallelism and performance considerations. Would you like to install ROOT on your local system? Have a look to the [instructions]({{ '/install' | relative_url }}).