This textbook aims to provide computing skills to analyze data collected by means of several types of experiments. Generally speaking, the analysis of data is complementary to experimental and/or theoretical activities, but, as a matter of fact, a fundamental part of the training process in scientific degree courses (such as physics, mathematics, chemistry, biology, and engineering) consists in different laboratory activities, collecting data and then analyzing and interpreting them. Different analysis tools are available for this purpose, including commercial and open-sources ones. Some of them allow to analyze data in a user-friendly manner, and it can be helpful for the first approach for a student to a data analysis problem, but, at the same time, it represents a limit on the real possibility that students can achieve by using the computation potentiality offered by a good knowledge of programming languages. For this reason, at least a computing course is generally present in scientific degree courses, as well as in experimental laboratories in which part of the training time is devoted to analyze and visualize data.

Part of the book is devoted to furnishing the rudiments of programming in C++, Python, and other open-source languages. In the second part, Root, a powerful and open-source data analysis framework, and the universe of the Python libraries will be introduced as a complete tool for data analysis, as well as their application and examples regarding physics experiments performed in laboratories.