The only reason this course is two stars is because the content could be useful to a beginner in the field. The course itself, however, is of poor quality with un-engaging video content and an unedited book with multiple sections that are clearly derived verbatim from the sub-par video lectures. It could be drastically improved with a little effort and would hopefully provide more value to learners with genuine interest.

Came in with high expectations, but the content didn't meet them. Some of the videos have poor audio/video quality, read out dry definitions that are not very relevant. The lecture notes and video content contain factual mistakes (section of software is filled with errors) and confuse the notion of machine learning with data science throughout.

They could have stated the audience of the course more clearly. I found most of the information irrelevant that added little value. Most of the things discussed are generic and would apply to any project.

Too much Superficial. Too fewer quizes. More external videos about hadoop, python, spark, data lakes. More paradigms broken. Need to explain what is On premise, rent and cloud.

Not very complete, also you need some knowledge of the field already otherwise you will be left in the dark at certain moments. Not a very consistent course. I expected better

Too theoretical, e.g, comparison between statistics and ML is not at all useful. Too many quizzes after very short classes and on topics of absolutely generic things.

Good intent but poor execution. Tries to summarize all the major topics but ends up delivering a totally disjointed, cut-and-paste experience with no real flow.

very elementary. Takes approximately 2 hours to complete.

cannot continue with the in-dept material but have to wait until next week (and payment ofcourse).

At some places, one lesson had the text and the next lesson was redundant with part of the information on video. you could club them in one an

too basic, the 4 courses of this specialization could be just one course.

Very general course. Doesn't give much insight into data science.

Very short. Quality of the course is also not that good.

1-week does not seem enough for a Crash Course

Not that engaging content. bit lengthy

This course is too short.

Overly academic

Very shallow

All the course content is too basic to bother with. It’s just a bunch of common sense and a few definitions of vocabulary words that can already be found on Wikipedia. There is typically one page of reading stating basic definitions and intuitive ideas followed by a 10-15 minute video reiterating those basic ideas. For example, there is an entire unit just to explain that a report on a data science experiment should be clearly written, avoid unnecessary detail, and have concise conclusions. There is a page of reading to list these as bullet points and a 14 minute video to repeat these points.

This course is a waste of time. Thankfully, it’s short and free.

A LITTLE TOO THEORETICAL. THE INSTRUCTORS SEEM CHALLENGED TO PRESENT REAL LIFE EXAMPLES. Quoting favorite examples from historical pure play ideas (e.g. predicting heights of boys) are frankly a bit dated, not relevant for a business audience, and hint at real life incompetence of instructors. And what is with trying to promote books these guys have written?

It's too short, I think it should be a part of a course and not a course itself.

It is a repetition of concepts and examples from other courses by john hopkins univ.

It would be great if slides were prepared and used through out the videos....

I was looking for something a little but more technical...

This course is very sparse on details, and just a week of content. The lecturer is not the best at explaining concepts.

Very basic, for absolute beginners/ managers completely new to data (let alone data science)

Too short to get any depth of information. Too expensive for the content provided.

No much information presented. Disappointed that this comes from John Hopkins.