This series of modules introduce key concepts in data science in the context of application in materials science and engineering. The end to end modules include:

• A recorded lecture that introduces each topic and provides background material,
• A hands-on tutorial with step-by-step instructions to perform interactive online activities and run interactive code,
• A homework assignment designed to help users explore the concepts using online models and simulations and adopt the code to problems of their interest.

The modules are self-contained and modular, they are designed for easy incorporation into existing courses or for those interested in self-study.

All interactive computing is performed using cloud computing in nanoHUB, there is no need to download or install any software. All resources are open and free.

Knowledge and Skills

1. Data handling
   • Data collection, completeness, & provenance – See Module 1 and Module 2
   • Data storage and sharing – See Module 1 and Module 2
   • Data querying, organization, and filtering – See Module 2
2. Predictive modeling
   • Data visualization – See Module 2 and Module 3
   • Digital representation and descriptors for materials – See Module 3
   • Simple regression models – See Module 4
   • Machine learning models for regression and classification - See Module 5
   • Random forests and decision trees – See Module 7
3. Decision making
   • Uncertainty quantification – See Module 6
   • Active learning for design of experiments – See Module 7

Pre-requisites

The interactive computing is performed using python through Jupyter notebooks. Basic programing skills are required. An introductory tutorial on Jupyter, python and plotting is available at: https://nanohub.org/resources/33266

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Alejandro Strachan, Saaketh Desai, Juan Carlos Verduzco Gastelum, Michael N Sakano, Zachary D McClure, Joseph M. Cychosz, Jared Gray West (2020), "Hands-On Data Science and Machine Learning in Undergraduate Education," https://nanohub.org/resources/34285.

  BibTex | EndNote

1. classification
2. data science
3. decision trees
4. design of experiments
5. hands on
6. Jupyter notebooks
7. machine learning
8. NCN Group - Machine Learning
9. NCN Group - Machine Learning Modules
10. python
11. random forests
12. regression
13. simulation-based learning
14. uncertainty quantification (UQ)
15. visualization