Hands-On Data Science and Machine Learning in Undergraduate Education
Category
Published on
Abstract
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
- Data handling
- Predictive modeling
- 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
Sponsored by
Cite this work
Researchers should cite this work as follows:
Tags
Hands-on Learning Modules on Data Science and Machine Learning in Engineering
This resource belongs to the Hands-on Learning Modules on Data Science and Machine Learning in Engineering group.