學生對 密歇根大学 提供的 Introduction to Data Science in Python 的評價和反饋

The lecture is so compact that does not tell us all the needed details. The assignments are just too much work, and we are expected to find everything by ourselves... Not recommended to newbies.

They barely touche over the material in the lectures, the assignments are mostly a waste of time and unrelated to the topic thought in the lectures.

This would be a good course if it was free.

The explanations in the videos are very superficial compared to the level you need to demonstrate in the assignments. It's basically self-learning with very negligible amount of guidance.

I think that this course is not an introduction course. The assignment were really hard. If there was little assignments after 2-3 min. of video lectures, it would be more informative.

Content In Video Course Was Very Poor Especially the background kept me disturbing regularly, I literally had to cover the area with my hand to hide the background movement with hand.

The course is neither for beginner nor for intermediate level students. Course is sort of tutorial where how is well explained, what is some time explained but why is always missing.

The introduction videos are tiresome to watch. Instead of explaining how to program it would to be better to give an assignment and use videos to only explain data science concepts.

The course content and the learning strategy is very good, but restricting assignments submissions to the paid subscription, although I have received financial aid from Coursera.

The code is already written, which means there is no opportunity to practice. More over, the examples have no baring on the homework problems. Would not recommend this course.

The lectures are not very useful in solving the assignments.

This cannot be said as an introductory course rather it requires good python skills before starting this course.

Lot's of problems with the autograder, lack of teaching staff support to answer questions in a timely manner. Assignments need to be more representative of class lectures.

Content wise is ok. An easy introduction. The bugs made it annoying. Especially Week 2 final exercise https://nzh13lxjj0.execute-api.us-east-1.amazonaws.com/prod/index/2/

It would be a very nice course... if this guy was actually teaching and not explaining the basics and expecting people just to search online of the remaining 90%

the voice of the teacher is not loud plus the assignments are too tough to clear, I think a basic version of learning pandas should be there before this course.

Too fast and need to break the content even smaller with frequent practise assignment and graded quiz, just make it like python for everybody specialization

I didn’t like how the subjects are explained, and i feel the grading system is not good at all. It would be better if assignments were graded by peers.

The course rushes through a lot of concepts and expects the learner to figure out real insights using public resources. The reference material is poor.

A horrible course with very little detail than required, I had to access a lot of additional resources than coursera in order to complete this course.

The content of teaching in videos is a way too less than the level of asssignments .I had to make a lot of efforts on my own to understand the things.

The ways is taught is not very good - basically a lot info in short timespan. I don't think the Jupyter Notebooks format is the adequate for teaching.

its hard for beginners to understand in the flow , it took me time to complete the course by studying about the same concept in other resources too

For some reason the format in which the lectures are organized lack a lot of tact and seem to be made with no pedagogic consideration whatsoever.

Explanations of python were pretty coarse, the video was not helping much. I feel like I spent time doing 99% of python and 1% of data science.

Many bugs, lecture content does not cover topics required to complete the assignments, assignment instructions are not written in a clear way.

The speaker speaks too fast for any useful "sharing". One need to do reverse engineering to understand the code and underlying concept.