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vepadulano
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Sep 21, 2026
| 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 Python Course](https://github.com/root-project/root-cpp-course/tree/26.09). This course is fully in C++ and covers aspects such as sing 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 in with RooFit, as well as Parallelism and performance considerations. |
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| * Advanced: [ROOT Advanced Python Course](https://github.com/root-project/root-cpp-course/tree/26.09). This course is fully in C++ and covers aspects such as sing 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 in with RooFit, as well as Parallelism and performance considerations. | |
| * Advanced: [ROOT Advanced Python 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. |
vepadulano
reviewed
Sep 21, 2026
| 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 Python Course](https://github.com/root-project/root-cpp-course/tree/26.09). This course is fully in C++ and covers aspects such as sing 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 in with RooFit, as well as Parallelism and performance considerations. |
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| * Advanced: [ROOT Advanced Python Course](https://github.com/root-project/root-cpp-course/tree/26.09). This course is fully in C++ and covers aspects such as sing 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 in with RooFit, as well as Parallelism and performance considerations. | |
| * Advanced: [ROOT C++ Course](https://github.com/root-project/root-cpp-course/tree/26.09). This course is fully in C++ and covers aspects such as sing 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 in with RooFit, as well as Parallelism and performance considerations. |
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