Apply data science tools to situations encountered in physics.
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.
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.
The assessment will cover the year's work (main course and practical exercises). The student will have to show that he/she has succeeded in applying the methods taught in the course and that he/she has mastered the lessons learned in the ex-cathedra or online courses. The methodology developed and the results obtained for the challenge will be taken into account.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Master in Physics, Professional focus in Physics and Data | Standard | 0 | 3 | |
| Master in Physics, Professional focus in Physics and Data | Standard | 1 | 3 |