Computational Data Science in Physics I

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 Data Science in Physics I

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.

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This course is currently archived. You can view most of the course materials, such as lectures and readings, and many assignments. However, you cannot complete assignments for a grade; you cannot upgrade or earn a certificate; the discussion forums may not be open or actively monitored; and some assignments or links may no longer work.

Note: This is course is being offered with experimental features.


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 course will teach you how to critically apply data science tools to physics data analysis, using Jupyter notebooks. You will recreate Nobel Prize discoveries and perform 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.

In this course, you will learn the core statistical tools needed to analyze data and extract physics parameters from the data. Furthermore, you 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 Laser Interferometer Gravitational-Wave Observatory (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 presents real-world, Noble Prize-winning physics data, allowing you to recreate and learn the physics and data science tools behind these discoveries. Learners within the fields of physics and data science can benefit from this class. Moreover, those interested in simply understanding the data analysis toolkit used in modern physics would benefit from this. This class is a stepping stone towards the rapidly developing cross-disciplinary fields of data science, artificial intelligence 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.

Prerequisites

  • Basic understanding of Python
  • Understanding of Classical Mechanics including Kepler’s laws
  • Basic understanding of Statistics and Probability

Who can take this course?

Because of U.S. Office of Foreign Assets Control (OFAC) restrictions and other U.S. federal regulations, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba, Syria, North Korea and the Crimea, Donetsk People's Republic and Luhansk People's Republic regions of Ukraine.