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.

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!

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.

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.

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.

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.

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.

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.

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

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

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.

Need more pratice problems for polymer and ceramic multiphase composites.

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

Pretty difficult for a beginner / Undergraduate

it was very good and useful.

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

interesting class

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)

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

照本宣科。以及习题的设置，怕不是在开玩笑？

Too much talk about general idea. Lack of practice to learn skills