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Вернуться к Materials Data Sciences and Informatics

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

4.5
звезд
Оценки: 280
Рецензии: 74

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

VV
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.

RR
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:

1–25 из 74 отзывов о курсе Materials Data Sciences and Informatics

автор: Yichi W

18 нояб. 2016 г.

Too much introduction, not much actual useful stuff. Too much mathematically without well illustrated examples.

автор: Сергей С К

8 июля 2019 г.

I think it's wonderful course, but I did not have enough real practical skills from it (in my opinion). Thank you very much to the instructors for this course!

автор: Justin F

14 июля 2017 г.

Useful introduction to vocabulary and concepts in the field, but can't help but feel the pacing and scope of the course takes an abrupt switch at times.

автор: Stefan B

24 февр. 2017 г.

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.

автор: Kevin Y J L

21 апр. 2019 г.

An excellent introduction to Material informatics. I highly recommend to any beginners to get started with learning informatics regarding materials.

автор: Pratik K

25 окт. 2017 г.

Excellent course if you are looking to understand how to design high performance materials leveraging current advances in data sciences.

Very well delivered by Dr. Surya Kalidindi and Prof McDowell. Reference to the book on the subject by Dr. Kalidindi supplemented by web search was useful.

Need to put the new skills acquired, in practice at work, where I see a huge potential.

Thanks Georgia Tech!!

автор: ANUPAM P

6 дек. 2017 г.

Very valuable course for materials modelling enthusiast. It provides me the firm grounding and preparation for my future research work in this material modeling. This course is a fine balance of technical knowledge, its implementation and the practical approaches one needs to adopt to effectively use this knowledge of materials modeling in real world. (Anupam Purwar)

автор: Rushikesh R

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

автор: Abdullah A

18 авг. 2019 г.

The course was overall good but some of the course content is outdated (installing PyMKS) please look into this matter.

автор: Bernard W

4 мая 2018 г.

Great introduction of the why and how of materials informatics!

автор: Sae D

21 сент. 2017 г.

This course discussed one particular issue in materials informatics. I hoped to see several other informatics-based techniques to solve problems in materials innovation.

автор: Lidiya P K

1 июня 2020 г.

The course has been very helpful in forming a basic understanding of data sciences application in Materials Engineering. Also it motivated me to explore even more, study and adopt these skills in my research.

In my opinion, a few more lectures on PyMKS applications in the last week would be of more help.

I strongly recommend setting up an advanced followup of this course with deeper analysis and some hands-on practice.

My heartfelt thanks to Prof. Kalidindi for this initiative.

автор: Zack P

2 апр. 2020 г.

I am in the process of transitioning from a purely design position to a professional materials engineer for a 3D house printing company. This course was a great fundamental introduction to materials processing history all the way to current high-end cyberinfrastructure like e-collaborative data pipelines, open-source machine learning libraries in python used to make cutting edge material breakthroughs today.

автор: Ongwenqing

18 июня 2020 г.

This course is very informative and relevant for Material Engineering students like me to incorporate Data Science and modern technology to speed up research on the discovery of new materials. This course has also provided useful computational tools such as Pymks. Pymks enable use to compute the 2 point spatial correlation and visualization does help in the analysis of the material's structure properties.

автор: Ferchichi Y

8 сент. 2020 г.

Thanks a lot for this clear and efficient MOOC! I look forward to learning more about the topic. I'll try to find time to read the examples on the pymks web site. Thanks Mr Kalidindi and all the staff!

Best Regards!

Yassine Ferchichi, University Teacher (Tunisia Private University - Mechanical Engineering Department)

автор: Mohammed S

11 июня 2020 г.

Very informative course. Cover many concepts of data science as well as the Material design field.

I would recommend this course to the people who want to stay in their core field while utilizing modern-day techniques such as machine learning and data science in their work.

автор: Yiming Z

19 июля 2017 г.

Thank you for the course. It is very helpful for my deeper understanding of Materials Informatics. I hope I can get more knowledge and assistance from Professors for my research in this field in future. Thank you!

автор: Victor V D C P

28 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.

автор: DHARMALINGAM G

28 апр. 2020 г.

This course is very much interesting and i have learned about micro structure analysis using data sciences simulation, regression ,finding mechanical properties etc

автор: PRIYANSHI C

8 окт. 2020 г.

It is a great way to combine both the branches, Material sciences, and data science. I completely loved this certification. Looking forward to learning more.

автор: Luis A G R

18 июля 2020 г.

Great initiative of creating this course! If you're curious about the idea of combining materials science and data science, this course is for you. Enjoy!

автор: Muhammad L M

11 нояб. 2020 г.

Well presented in a simple manner. Great courses to learn exploratory data in material science and engaging with current issues.

автор: Dhanush S B

11 мая 2020 г.

A perfect course if one wants to pursue a research career in material science with an engineering background.

автор: Siddhalingeshwar I G

8 сент. 2020 г.

I take this opportunity to express sincere gratitude to Dr Surya Kalidindi. Thank you COURSERA yet again.

автор: Fekadu T B

1 июня 2020 г.

You will learn four paradigms of science: empirical, theoretical, computational, and data-driven.