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Learner Reviews & Feedback for Materials Data Sciences and Informatics by Georgia Institute of Technology

232 ratings
68 reviews

About the Course

This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (including advanced statistics, dimensionality reduction, and formulation of metamodels) and innovative cyberinfrastructure tools (including integration platforms, databases, and customized tools for enhancement of collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges....

Top reviews


Jul 28, 2020

It's a great course that can give you a wide view of how to accelerate the development of material using computational resources. I'm a Metallurgical Engineer and I totally recommend this course.


Sep 23, 2018

Machine learning part and its application to material science was interesting but informative contents like material dev eco system and whole week 1 was more informative than logical

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26 - 50 of 67 Reviews for Materials Data Sciences and Informatics

By pradeep s c

Jun 01, 2020

It includes ausam information in structured manner to learn the subject easily.

By Li J Y

Aug 24, 2020

Interesting content! Liked the explanation of principle component analysis.

By Madhuri C D

May 22, 2019

Best way to learn newly developed system using material data science.


May 18, 2020

Skills on the Data Sciences can be applied to other areas of studies

By Youxing C

Oct 04, 2020

It exactly fits my needs for this area. Highly recommended!

By Santosh B M

Apr 08, 2020

Good to know about the basics of materials data.

By Anshuman S

Aug 09, 2016

Brilliant lectures on a very interesting topic!

By Naveen s

Jun 06, 2020

Very good experience and have learnt allot

By Herbaut J P M

Jul 15, 2019

Great expérience !

Herbaut Julien / Yale

By Jorge A A L

May 29, 2020

It is a intermediate/advanced course

By Marcin F

Aug 11, 2020

Excellent presentation and content

By Prakhar C T

Jun 23, 2020

Very useful course

By Siva S

Jan 12, 2020

Excellent course!

By mansi g

Jul 17, 2018

its easy to do it

By Sheikh N

Oct 10, 2018

Very nice course

By Dr. K R

Mar 17, 2019

Awesome Course!

By Salim A

Dec 18, 2016

very beneficial


Jun 24, 2020

Great course!

By Mona A A

Jul 10, 2020


By Dr. C K

Jun 14, 2020


By kavuri v

Apr 18, 2020


By Gilbert L

Jan 17, 2020


By James M

Oct 25, 2020


By Sumit B

Jun 08, 2020

Pretty advaced stuff! The starters must have a solid grip on statistics, Linera algebra(Eigenvalue, Eigenvector, SVD), ,Intergral transforms (Fourier and Laplace), ICT, Computer programming (especially Python) and Introductory materials science. A tensor analysis and Perturbation theory background is helpful.

A lot of new formalism and a good link or repositories have been provided. The n-point statistics and specially the mathematics of Localization are extremely complicated, and poorly presented (localization-homogenization, specially Capital Gamma function and numerical solution to integral equations) of having rich assemblage of knowledge.

The first two weeks and specially the first week could have been arranged in mor pedagogically suitable manner. Still I am Giving it 4 instead of e stars for profound knowledge embedded into the course.

By Yeshar H

Sep 21, 2016

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.