it was very good and useful.

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.

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