This repository will contain a series of hands-on tutorials to get started with some of the basics of data handling and data analysis in ATLAS and at CERN.
These are intended for summer students joining our group, as well as group members coming from different fields, and people just getting started with programming.
Before you explore the material, it is necessary to get acquainted with some basic topics like Unix, Git/Github, Python and Machine Learning. Here are some links to free online courses, video-lectures and material you should look at if you're not familiar with some of these topics. These are my personal recommendations, but feel free to edit this document with other great online tutorials that you've found or used before.
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20 min video with command line examples: http://openclassroom.stanford.edu/MainFolder/VideoPage.php?course=PracticalUnix&video=intro-shell&speed=100 Later on, if you want, feel free to look at other videos in this series, such as the ones called "Find", "Grep and Regular Expressions", "scp - Secure Copy" and the last 4 about Redirecting Output.
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Interactive CodeAcademy course. It should take you 1-2 hours: https://www.codecademy.com/learn/learn-the-command-line
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Udacity video course. It should take you 1-2 hours. Lecture 1 is about setting up a Virtual Machine, which you don't have to do, so feel free to skip it: https://classroom.udacity.com/courses/ud595/lessons/4597278561/concepts/46968695970923
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Set of video lectures with lots of good material (only look at Lectures 3 and 4A, they should take you 1-2 hours): https://class.coursera.org/startup-001/lecture
- Quick 15 minute interactive course: http://try.github.io/
- Awesome (!!) Udacity video course. It should take you 1 or 2 days: https://www.udacity.com/course/viewer#!/c-ud775/l-2980038599/m-2960778924
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Nice and thorough course on Coursera. It comes with 7 video lectures that should take you 30 minutes to 1 hour each; they are split up in shorter videos of a few minutes each. It also comes with assignments and exams if you want to get extra practice: https://class.coursera.org/programming1-002/lecture
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Google Developers intro course. It comes with written instructions and material, as well as video lectures and exercises (see menu on the left): https://developers.google.com/edu/python/
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Code Acedemy interactive course. Again, no videos, just super quick instructions and interactive shell. They estimate it should take 13 hours to complete. 2.5 million people took this course!: https://www.codecademy.com/learn/python
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Here is a list of longer video courses on Coursera. They might take you too long though, so I would say only consider them if you plan to keep on watching a lecture a day for a few weeks. Mostly, I just wanted to leave these here for reference: https://www.coursera.org/courses?languages=en&query=python&start=0
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The best intro course out there taught by Andrew Ng. It should take you weeks to complete it, but it's totally worth it: https://www.coursera.org/learn/machine-learning
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Deep Learning Glossary with simple, brief definitions and links to more in-depth explanations http://www.wildml.com/deep-learning-glossary/
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Best papers to read to learn more about Deep Learning: https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
- Google Style Guide for C++ : https://google.github.io/styleguide/cppguide.html
For a more comprehensive list of resources and tutorials in all sorts of languages and frameworks, check out this list: https://github.com/WomenWhoCode/guidelines-resources/blob/master/learn_to_program.md
- Getting started with RooFit in ipython: Vince Croft's intro tutorial
- Basic example with RooFit in ipython: Kyle's blog post
- Signal & bkg composite models in ipython: Vince Croft's in depth tutorial
- Another RooFit basics ipython notebook: here
- CERN academic training on likelihoods: Statistics for Particle Physics Analyses: Introduction to Computing Examples
- RooFit introduction: slides
- Advanced and very helpful RooFit tutorial: slides
- 200-page RooFit presentation: here
- RooStats (and HistFactory) introduction: slides
- Quick paper on RooStats: paper
- Higgs analysis in ATLAS using RooStats (and more): twiki with C++ examples
- HistFactory - A tool for creating statistical models for use with RooFit and RooStats: paper
- Kyle's HistFactory video lecture: video