Desenvolvido por:   Georgia Institute of Technology

  • Dr. Surya Kalidindi

    Ministrado por:    Dr. Surya Kalidindi, Professor

    The George W. Woodruff School of Mechanical Engineering
LevelIntermediate
Commitment5 semanas de estudo,2-3 horas/semana
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.2 stars
Average User Rating 4.2See what learners said
Programa

Perguntas frequentes
Como funciona
Trabalho
Trabalho

Cada curso é como um livro didático interativo, com vídeos pré-gravados, testes e projetos.

Ajuda dos seus colegas
Ajuda dos seus colegas

Conecte-se com milhares de outros aprendizes, debata ideias, discuta sobre os materiais do curso e obtenha ajuda para dominar conceitos.

Certificados
Certificados

Obtenha reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.

Desenvolvedores
Georgia Institute of Technology
The Georgia Institute of Technology is one of the nation's top research universities, distinguished by its commitment to improving the human condition through advanced science and technology. Georgia Tech's campus occupies 400 acres in the heart of the city of Atlanta, where more than 20,000 undergraduate and graduate students receive a focused, technologically based education.
Classificações e avaliações
Avaliado em 4.2 de 5 decorrente de 20 avaliações

This is a great starter course for materials informatics. It covers a good amount of topics and uses a nice case study to reinforce digital representation of data, spatial correlations, principal component analysis, and regression. I really liked the examples of pyMKS. My only suggestions is it would have been nice to have more hands-ons use of pyMKS and sci-kit learn. This could have been accomplished through a course project or homeworks.

A great introduction to this exciting field!

very beneficial

Great, fantastic information that made me see the importance of data sciences in materials science and engineering. My only request would be to potentially spend more time fleshing out PCA and the statistical tools around it; most of it went over my head without seeing a step-by-step application of it that showed the calculations. Maybe it could be optional so that those who are already strong in PCA can skip it.



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