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Отзывы учащихся о курсе Materials Data Sciences and Informatics от партнера Технологический институт Джорджии

Оценки: 296

О курсе

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....

Лучшие рецензии


27 июля 2020 г.

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.


22 сент. 2018 г.

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

Фильтр по:

51–75 из 77 отзывов о курсе Materials Data Sciences and Informatics

автор: kavuri v

18 апр. 2020 г.


автор: Gilbert L

17 янв. 2020 г.


автор: James M

24 окт. 2020 г.


автор: Sumit B

8 июня 2020 г.

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.

автор: Fariba T

17 февр. 2021 г.

I liked very much the fact that this course on "materials data science" gave me a general insight into what could look like the data science for material scientist. However, one should admit that it was too abrupt when it comes into informatics and modeling. The knowledgeable instructor seems to assume that all of us have a background in mathematics and statistics too. I suppose a way to improve the quality and effectiveness of the course is to give a bit more time on these aspects in correlation with materials science.

In addition, the week 5 on pyMKS was not updated based on the present information on the website of pyMKS.

Thank you for the generous sharing of your knowledge!

автор: Yeshar H

21 сент. 2016 г.

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.

автор: Lim J H

24 июня 2020 г.

Great concepts and descriptions, however, it can be surprisingly dry and not helping is the monotonous way the lessons are being carried out. The PyMKs helps to alleviate the boredom though so do download the program and try it out for yourself after understanding the basics of the course.

автор: Zisheng Z

30 апр. 2018 г.

A great introductory course into Material Data Sciences and Informatics. Had a relatively hard time when the course turned form introduction into hardcore statistics. Moreover, it can be more helpful if there are more practical projects and tutorial on introduced tools.

автор: Priyabrata D

29 апр. 2020 г.

Some lectures from week 1 and week 5 are identical, hence repetitive. The case study is really good. Week 3 contains the most important information. Hence, week 3 needs more clarification on a basic level. Sometimes I felt unconnected with the lectures.

автор: Sashanka A

3 июля 2020 г.

This course provides great inputs on how data science can be implemented in material science. Though it didn't deal deep into all the concepts, it was focussed to explain briefly what is out there in the field of materials informatics.

автор: Navneeth R

2 мая 2020 г.

Overall it was a very good course and I recommend it for all students interested in material science.But the installation procedure could have been updated and I still face problems in installation of Softwares to use.

автор: Ashish S

23 авг. 2020 г.

Got an overview about how materials data is analysed. This course helps us in understanding the need of data sciences for accelerating material development.

автор: Pranav K

13 мая 2020 г.

Good theory lessons. There should have been more focus on utilising software (PyMKS) to implement concepts, throughout the course rather than just the end

автор: Biplab B

28 мар. 2020 г.

the course is nice and useful, but is very tough. You require a good knowledge of statistics, computation, and material science to make it through it.

автор: Sachin K B

22 авг. 2020 г.

Need more pratice problems for polymer and ceramic multiphase composites.

автор: Veronica T

25 апр. 2020 г.

The course is great but sometimes it was entirely too wordy.

автор: Sai S S B

4 мар. 2020 г.

Pretty difficult for a beginner / Undergraduate

автор: 杜傳彬

10 авг. 2022 г.

This course is a little hard to understand.

автор: Sukru T

17 дек. 2020 г.

it was very good and useful.

автор: Chandramouli S

2 июля 2021 г.

Some topics were very lightly touched while some of them were outright skipped. For example the instructor said the topic of leave one out cross validation was done earlier while it wasn't. Overall I was an insightful course for those who want to link Material Science and Computation/Modelling but the caveat is a lot of external reading is suggested and I would suggest you might have a basic data science/Linear algebra knowledge for PCA analysis and Spatial Correlations along with python for pyMKS system.

автор: Javier G M

5 июня 2020 г.

Not bad, but it would be better with a bit of hands-on practice.

автор: Xin L

30 дек. 2019 г.

interesting class

автор: CEDRIC T

4 дек. 2019 г.

The course gives a "good" overview of some techniques but is way too descriptive, way too theoretical. There is no progressive (computational) practice. The major flaws of this course are: 1)no handouts of the slides provided, 2) reference to papers are not clickable URL's, 3) PyMKS runs in Python 2.7 (not 3.4) with many modules deprecated. Running this PyMks is therefore not easy at all and bugged with the environments. Once you get in the course is just about replicating some logic without going in-depth of the potential of this tool. As well , what are more up to date tools to be used? 5) instructors are not really good at teaching , 6) there is no active learners community at this period (november 2019)

автор: Rachel H

20 мая 2020 г.

It took until the last 15 minutes of week 5 to get to the actual data science...