Course gives you good overview on different important data science technologies. Hands on labs are important to get the grip on concepts.

Last week of the course is too much information and without any assignments it kind of doesn't make much sense and it doesn't stick.

covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies

Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.

Great content. The questions are academic and sometimes hard to understand the desired outcome

Excellent course. I just sad about the absence of any assignment or even quiz in Week 4..

It is a comprehensive course for learning quite up-to-date technology and concept.

Good Course for beginner in Data Scientist field. I recommend this course

Very good class - the assignments were pretty uninteresting, though.

I enjoyed this course and found it challenging. Good job!

Great assignment and course design! Not easy for me.

Very good course! Interesting problem sets.

Interesting intro to some powerful ideas

recommend to improve assignment details

Good introduction to Big Data systems.

it is very useful but easy enough

Very usefull course. Great!

Excellent course!

Good

This course has appealing assignments and covers interesting topics. The course, however, has two fatal flaws. First, the lectures are a bit disjointed. While there is much to learn in the lectures, the lecturers style is a bit halting and scattered (it would have been much better presented if the lecturer had a script to read off of.) As is, the lectures are mediocre, which is unfortunate since the lecturer is clearly knowledgeable about the topics presented.

Second, the assignments suffer from a lack of good error messaging and no support in the forums (aside from what you will find from other students, which can be very helpful at times.) The assignments themselves are a great approach to learning concepts (and you get to work with real data, like the Twitter data), but without good error messaging when you submit a script you pretty much end guessing where you are taking a wrong turn.

I had high hopes for this course, but it seems as though it fails on execution.

Overall I enjoyed this course and got a broad overview of the various technologies used in big data analysis. The course is video heavy but short on practice. There are 3 assignments the first 3 weeks, then week4 is an endless series of videos. I really enjoyed the assignments but felt there should have been more assessment/practice provided -there are no quizzes to reinforce understanding. The readings provided are mostly academic ones which aren't that clear to beginners (even to programmers like me).

In contrast, a Python data science course on another MOOC platform has 4 times as much content with practice exercises after every video, mid and final exams, weekly problem sets as well as readings.

Ultimately the course showed me what I need to learn next to get into Data Science but the first course hasn't given me confidence that the rest of the specialization will be worth the money.

The lectures are all just the right length. As a working professional, it was easy to consume the course in my varying bits of free time. For the most part, the assignments were good. There were a few places where there were mistakes in the instructions or the code downloaded from github had some errors. It was fairly clear that these mistakes have been around for quite sometime, so I wonder why nobody ever bothered to update the code in github or update the bad instructions. This is the internet, not a published textbook. That should be pretty easy to fix. It was also a little disappointing that the 4th lesson was extra long in terms of lectures and had no related assignments. The course was marked as completed when I finished the 3rd assignment so the fourth lesson was effectively optional.

Pros: The content of the course is great. It introduces fundamentals of big data technologies to those who are new to this field, with some hands-on practices.

Cons: The instructions of assignments are not always clear - they are corrected in the discussion forum but why not updating in the assignment page? Usage of Python 2.7 is also somewhat out of date since it's 2019.

Biggest con: The way the lecturer talks is more than annoying. Full of stop words like 'fine', 'ok', with occasionally correcting mistakes on slides or diverging to other topics - there are only a few minutes each video and how much time did the lecturer wasted on talking nonsense? It's fine if he talks like that on some 90-min-long classes but it's on Coursera. Sometimes I just skimmed the slides rather than listen to him.

Lots of good material, though I don't like how they've repackaged the original material from the prior longer version, which I worked through at my own pace a year ago, off session. Cramming all the material relating to NoSQL and Graph Analytics into the final week without assignment is ineffective. Instead, consider focusing the 4th week on NoSQL, and keep an assignment with it, maybe even the original Pig assignment that required and AWS account. I don't think the nominal charge Amazon will levy would hold anybody up who's serious about learning to process data at scale, it's just a few bucks.

Somehow interesting course about Data analysis. The lectures are interesting for those who have no prior knowledge about the topics, but boring to those who have it. The assignments are quite challenging and the disadvantage is, that they are not connected to the lectures and are therefore not well explained. What I like about the assignments is, that it is practical.