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Computational Data Science in Physics I

This course provides realistic, contemporary examples of how computational methods apply to physics research. In this first module, learners will analyze LIGO data, detect a gravitational wave signal, and fit this signal with a physical model, among other objectives, using Jupyter notebooks.

Computational Data Science in Physics I
start date
6 weeks
10-15 Hours/Week

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.


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

Meet your instructors

  • Philip Harris

    Assistant Professor of Physics, MIT

    Philip Harris joined the MIT faculty in 2017. Since joining MIT, Philip has helped found the Fast Machine Learning group aimed at deploying processor accelerated machine learning algorithms for real-time and high throughput scientific applications, including the LHC. Additionally, Philip leads the real-time particle reconstruction group on the CMS experiment. He is the deputy director of the new NSF institute A3D3(Accelerated AI Algorithms for Data-Driven Discovery). Born in Sao Paulo, he received his B.S in Physics from Caltech in 2005 and his Ph.D. from MIT in 2011 on research performed at CERN with the CMS experiment. From 2011-to 2013, Philip was a CERN fellow working on the Higgs Boson discovery. From 2014-to 2017, he was a CERN staff scientist leading the effort on dark matter searches at the CMS experiment.

  • Isaac Chuang

    Professor of Electrical Engineering and Computer Science, and Professor of Physics, MIT

    Isaac Chuang is a pioneer in the field of quantum information science. His experimental realization of two, three, five, and seven quantum bit quantum computers using nuclear spins in molecules provided the first laboratory demonstrations of many important quantum algorithms, including Shor’s quantum factoring algorithm. He is also passionate about education, and has served as senior associate dean for digital learning at MIT for the past decade.

  • Jesse Thaler

    Professor of Physics, MIT

    Jesse Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider through new theoretical frameworks and novel data analysis techniques. Prof. Thaler joined the MIT Physics Department in 2010, and is currently a Professor in the Center for Theoretical Physics. In 2020, Prof. Thaler became the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions.

  • Alex Shvonski

    Lecturer, MIT Department of Physics, & Digital Learning Lab Scientist

    Alex Shvonski is a Lecturer in the Department of Physics at MIT and a Scientist in the Digital Learning Lab, currently working on developing online classes. He also works with introductory physics courses at MIT, most recently creating and running take-home experiments for remote learning. Alex is interested in physics education research and developing effective ways for students to interactively engage with content, for instance through hands-on experiments or simulations. He received his Ph.D. in Physics from Boston College working on plasmonics.

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.