This repository contains data science educational materials for physics courses developed by Data Science Education Community of Practice (DSECOP) Fellows. To learn more about the DSECOP project, please visit https://dsecop.org/.
- DSECOP 102 The Professor's Module by Julie Butler (2023)
- DSECOP 160 Outlier Analysis by Ashley Dale (2023)
- DSECOP 170 Introduction to Error Reduction Techniques by Richard Harry (2023)
- DSECOP 236 Gaussian Processes and the Schrödinger Equation by Karan Shah (2023)
- DSECOP 250 Automatic Differentiation by Connor Robertson (2023)
- DSECOP 101 Introduction to Data Science Libraries by Julie Butler (2023)
- DSECOP 240 Symbolic Regression by Joseph Dominicus Lap (2023)
- DSECOP 320 Connecting Monte Carlo to Modern AI by Ashley Dale (2023)
- DSECOP 290 Time Series Analysis and Forecasting by Connor Robertson (2023)
- DSECOP 150 Intro to Classification Algorithms by Richard Harry (2023)
- DSECOP 280 Automated Object Detection by Karan Shah (2023)
- DSECOP 110 Intro to Data Processing with Histograms by Radha Mastandrea (2022)
- DSECOP 201 Intro to Deep Learning by Fatima Bagheri (2022)
- DSECOP 235 Learning the Schrodinger Equation by Karan Shah (2022)
- DSECOP 340 NMR Deep Learning by Sebastian Atalla (2022)
- DSECOP 230 Solving Differential Equations with NNs by Julie Butler (2022)
- DSECOP 220 Spectral Clustering by Cunwei Fan (2022)
- DSECOP 120 Exploratory Data Analysis by Radha Mastandrea (2022)
- DSECOP 210 Intro to Random Forest by Fatima Bagheri (2022)
- DSECOP 330 Singular Value Decomposition by Sebastian Atalla (2022)
- DSECOP 140 Machine Learning Workflow by Julie Butler (2022)
Please use the Bug Reporting Form found here to let us know about any problems with the notebooks.
You can also email dsecop@aps.org with feedback.
All content in this repository is distributed under Creative Commons CC0 License. Please submit issues or pull requests to this GitHub repo if you wish to improve the materials.
