About the tutorial
Data Science is playing an ever increasing role in physics. While some departments have offered courses, many of the examples are in the context of social science and other disciplines. In this tutorial, we will introduce data science in the physics context. We will start by introducing Jupyter notebooks and how to explore and visualize data. We will then introduce unsupervised learning techniques including clustering, random forests, etc. We will conclude with an introduction to neural networks and object tracking.
Graduate students, post-docs, and other scientists interested in learning how to apply data science to their research should attend this tutorial. The lectures will provide an introduction to data science and its applications in physics. We assume that participants will have some experience with Python, Numpy, and Matplotlib at the level of a software carpentry course and we will provide a link to learning materials before the tutorial.
Topics covered:
- Data visualization and exploratory data analysis
- Unsupervised learning
- Convolutional neural networks
Organizers
- William Ratcliff, National Institute of Standards and Technology
- Talat Rahman, University of Central Florida
Presenters
- Julie Butler, University of Mount Union
- Karan Shah, Technische Universität Dresden
- William Ratcliff, National Institute of Standards and Technology