I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments.

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

This course probably deserves 3-4 stars in a better, maintained form, but the entire specialization is not maintained, the lectures have no production values. Basically, it's a money pit that Coursera is keeping up cynically. It's a real shame because the syllabus correctly addresses a gap in most data scientists' skills.

Unrelated and incohesive lectures. Disappointed. Lots of random topics talked about .but nothing in depth.

The course is good. But it does not has lecture slides that is better for students to understand.

Same as before, subjects are quite interesting, but the video material is of quite low quality.

This course is quite outdated. I didn't learn much beyond what I already knew before I started. The Spark courses from edX are way better than these. Hopefully "Big Data Analysis with Scala and Spark" from the "École Polytechnique Fédérale de Lausanne" (also from Coursera) is good (I know their Scala courses, which are taught by Martin Odersky, are quite good).

There are very few quizzes between lectures and the assignments are not very challenging.

Many of the videos, specially the ones at the end were extremely rushed over. They serve more as a review if you know the subject, otherwise I don't think most people will get much from them.

The audio isn't very good for most of the lectures, many having an very annoying chirping sound (from when you leave an old flip phone near a computer... "teh-teh-teh teh-teh-teh teh-teh-teh teh-tehhhhhh....". Gosh, I haven't heard this sound in maybe over five years...).

The Kaggle competition at the end of the course can be fun if you do the hard work, but you don't need to put much of an effort to pass. I know that the submissions I peer reviewed were quite poor, but the grading criteria that we need to follow as reviewers is quite vague and not very thorough. You also run the risk of getting a lesser grade than you deserve because your reviewer is incompetent, which is a bummer... At the moment the course has very few people taking it (the same people I peer reviewed, also reviewed me, which leads to me to believe that maybe only 3 or 4 people were taking this course during the November 2016 iteration).

Hands down the worst course on Coursera. I thought it might be beneficial to take this course but it doesn't cover anything in details. Wherever algorithms are explained, a really lousy job is done. To be specific, first and second weeks are covered badly. I am still trying to understand the material by reading external articles on the course topics. I think that first course of this specialization was pretty great and this one is disappointing.

Terrible lecture videos with many typos, absence of lecture notes, absence of course staff on a discussion forum. Maybe suitable for those who is alredy a scientist and just need to get some general sence of data science.