This document introduces basic concepts of data science and machine learning in the context of materials science applications. The focus is on hands-on activities where readers use open, online tools in nanoHUB to explore the concepts being introduced. Topics covered include querying data resources and data organization and preparation. Regression exercises, including neural networks, to predict materials properties from a set of descriptors and a classification exercise designed to predict the crystal structure of elemental metals. The examples are provided as fully contained Jupyter notebooks and state of the art, open software. They are designed to introduce to the main steps in data science workflows and readers can modify or extend them to solve additional problems.

Alejandro Strachan ,Research Group Page

Saaketh Desai, LinkedIn

Juan Carlos Verduzco, LinkedIn

Powered by Jupyter Notebooks, Keras, Pandas.

Network for Computational Nanotechnology

Researchers should cite this work as follows:

• Alejandro Strachan, Juan Carlos Verduzco Gastelum, Saaketh Desai (2019), "Data Science and Machine Learning for MSE Students: introduction and Hands-on Activities," https://nanohub.org/resources/31246.

  BibTex | EndNote

1. crystal structures
2. data science
3. Jupyter notebooks
4. Keras deep learning API
5. machine learning
6. materials science
7. Materials Science and Engineering Education
8. NCN Group - Machine Learning
9. Pandas