1. The title is misleading since "at scale" led people to think that large scale data processing platform such as spark and nosql databases will be central to the course right from the start

2. The assignments need a lot of improvements. I am not happy with how we're often only required to submit a single number as an answer. The biggest problem is that this way the grader won't be able to give you meaningful feedback / hint of where you might be wrong. A grader that only tells you "your answer is incorrect" does little to help you learn from mistakes.

3. I think assignment 2 can be paced differently - now it feels that we have a bunch of very easy parts (a-g) that is not very interesting, where the last three are significantly harder.

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 left me with mixing feelings. I learned some new things, but overall I don't think that I got understanding of base concepts. Week 4 seems to have disproportionately more material than previous weeks, as though authors tried to Although free, this is course is also offered as a part of paid specialization, and I would be really disappointed if I'd spent money on a course like this.

The grader is horrible leaving you with such brief error messages. You never know what is wrong with your code. The forum is not as useful as I expected nor as it is in other Coursera courses. You know, few classmates. The videos provide you with tons of information, but not much of them are well-organized. I often felt tired and confused since these long videos seldom got to the point.

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.

The exercises are fun and challenging. However, the lecture are not related to the exercises and are very hard to follow (I think it's the same thing as Brian's class in Johns Hopkins' data science course) If you are taking Bill Howe's class, just go straight to those exercises and skip lectures.

Many mistakes in the slides and poorly defined problems in the assignments have gone uncorrected for over a year. The content is very basic, as would be for an introductory course, but can even serve as a refresher for CS graduates.

Quite interesting subjects, but video material is not of high quality and many mistakes are not changed in later sessions but altered via a text in the screen of a note on the next sheet.

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

boring and easy, the homework is too easy and not well designed

This course totally reminds me of some courses back in college: unorganized material and the assignments are unrelated to the tutorial. The assignments themselves seem to be very helpful but the tutorials did no help of achieving these assignments.

I had a hard time following the instructor despite that I've completed all the certificate for python from University of Michigan. I'm aware of my background of python is still not strong enough so I thought it's probably just me not able to learn it fast enough.

But then I watched the tutorial about SQL. As a data architect / ETL developer, SQL is something I'm familiar with and use it everyday and then I realized that the instructor couldn't explain a nested query well. The reason I was able to understand about the SQL part is because I already know.

Lost in details. Professionals(btw I hold a MSc degree in Computer Engineering) cannot get anything from this. What is the point of writing frequency.pl where there is a hist() function in R? If the instructor is trying to teach us how to program in any language, then I can assure you the data science class is not the right place. I recommend the instructor check ESSEC Business School for analytics subject to better comprehend the Coursera and its goals.

The course info makes no mention of Python as a prerequisite. Yet the first assignment demands Python knowledge and skills. Without which you can't pass the assignment. Yet the week's lecture is not about Python.

Th first three classes are very 'thin' in content and the assignments are easy. The fourth class is basically optional and it has TONS of content. What's the point?

Very much outdated, assignments no longer technically possible

Outdated, unintelligibly exercises, terrible lectures.

Poorly designed videos, too long and confused

very poor!