Explore realistic, contemporary examples of how computational methods apply to physics research. In this first module, you will analyze LIGO data, detect a gravitational wave signal, and fit this signal within a physical model, among other objectives, using Jupyter notebooks.
Explore realistic, contemporary examples of how computational methods apply to physics research. In this first module, you will analyze LIGO data, detect a gravitational wave signal, and fit this signal within a physical model, among other objectives, using Jupyter notebooks.
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.
Probability distributions, error propagation, data fitting, uncertainty, likelihood, Fourier analysis, confidence, correlations, covariance, matched filtering, working with Jupyter notebooks.
