About this course
Computational methods are a critical component of many fields of physics research. With the rise of deep learning and the development of large-scale computational facilities, the impact of computation has become increasingly important. Physics research in a broad range of fields has been rapidly accelerated due to emerging numerical techniques that have allowed for more comprehensive data analysis and increased computational complexity of physical phenomena. Much of the recent work in physics underpins the emerging field of Data Science and has helped to cultivate critical problems with solutions that cross-cut many areas of research.
This class presents a course on how to critically apply data science tools to physics data analysis, using Jupyter notebooks. You will recreate Nobel prize discoveries and perform current modern physics data analysis with research grade data. Additionally, you will understand the core data science toolkit required to be a physicist in the modern era.
For this class, the learner will learn the core statistical tools needed to analyze data and extract physics parameters from the data. Furthermore, the learner will learn when it is critical to apply the data science toolkit or the physics toolkit to obtain high quality physics results. The class is designed around research “modules,” where learners work on each module to gain experience with a specific scientific challenge. The first module is related to analysis of LIGO data. Additionally, the content of this course will be accessible through Jupyter notebooks, which learners are encouraged to edit and run, in order to advance through computational problems and projects.
This course provides real world, noble prize-winning physics data and allows learners to recreate these Nobel prizes and learn physics and data science tools behind these discoveries. Learners within the field of physics, data science can benefit from this class. Moreover, people just interested in understanding the modern data analysis toolkit used in physics would benefit from this. This class is a stepping stone towards the rapidly develop cross-disciplinary field of data science, AI and Physics.
What you’ll learn
Probability distributions, error propagation, data fitting, uncertainty, likelihood, Fourier analysis, confidence, correlations, covariance, matched filtering, working with Jupyter notebooks.