Below are some of the software tools that you are encouraged to use and explore. This course will teach both the statistical and machine learning techniques of data science that are applicable to experimental physics, in addition to modern computational tools. Curriculumįor the content of this course please see SYLLABUS.md Software and Tools The assignment for this week is Module X. Each module is designed to be utilized for one ~90-minute period or one-and-a-half ~60-minute periods. The modules are arranged in increasing order of complexity, from "Basic Statistics and Probability" up to "Machine Learning for HEP". The interdisciplinary curriculum helps students develop a comprehensive understanding of computer science, statistics, strategic decision-making, ethics and data visualization. This can be done by the instructor or by requiring a Python programming course first. The Master of Science in Data Science program at Southern Methodist University is designed to prepare and develop professionals in the ever-evolving data science field. The modules expect some familiarity already with Python, so it is important before utilizing any materials here that students already have a pre-requisite of some education in Python as a programming language. This is implemented at SMU where the course is instructed by the lead author of this repository ( Stephen Sekula).
This repository is home to Jupyter/Python notebook and other resources, arranged into modules, that can be used to conduct a data science portion of a HEP/Particle Physics course. Modules for a data science program in a HEP/particle physics class at SMU