Data Science in Physics [SPHYM151]

Learning outcomes

Apply data science tools to situations encountered in physics.

Objectives

Among the many classes of situations to which Data Sciences apply, we will see some examples encountered in physics. In addition to the implementation of tools developed in other courses, the course will develop various theoretical methods useful for the analysis, representation or classification of data in physics.

Content

The course will be based on one or more situations encountered in physics whose resolution or analysis requires the use of "Data Science" methods. As an illustration, the public website Kaggle.com provides a wide variety of datasets associated with challenges related to physics problems (e.g. the detection of gravitational waves). Data from experiments conducted at UNamur will also be processed. The necessary theoretical and methodological contents, such as neural networks and deep learning, will be taught. The basics of working in Python and using libraries such as Numpy, Matplotlib, TensorFlow and Keras will be presented. One or more seminars on other aspects of the use of data sciences in physics will be offered. The student will have to solve the proposed challenge practically.