PHYS 370 - Computational Physics

This course introduces students to coding and computational methods, focusing on developing computation-based skills that are critical for practicing physicists. Students process experimental data using statistical tools, study the implications of realistic physical models using a toolbox of numerical methods, and visualize information in meaningful formats. The course starts with an introduction to coding (in Python) and a survey of data manipulation tools (e.g., reading, writing, analyzing statistically, etc.). The focus is on skills that help students in labs, research, and anywhere else they need to extract physical insight from data. After the introduction to data analysis, the course transitions into a survey of numerical techniques which facilitate quantitative analysis of theoretical models describing complex physical phenomena, thereby creating a bridge between experimental/observational and theoretical physics. The lab provides hands-on experience in the application of computational skills discussed in lecture to realistic physical models drawn from a wide range of physics subdisciplines. Projects in the lab are implemented using Jupyter notebooks, via the Google Colaboratory environment. Students write documentation in Latex and Markdown. No previous experience with any of these is assumed. Prerequisite(s): PHYS 331 Spring semester. (4 Credits)