Not enough Cloud Data Prep or Cloud Data Fusion.

Some parts are quite advanced for beginners.

il y avait trop des longues vidéos

difficult to follow for beginners

volume is not consistent

partially satisfied

Too shallow on SQL

good but brief

Too easy

too easy

thanks

good

Good

ok

-

o

Very outdated material. The course reference GCP as it was 4 (four) years ago and GCP is a very rapidly changing platform with the interface that had numerous changes since 2017. Material of the course is not always consistent, the sequence of individual sections does not always present a coherent content. Not sure I understand how the labs are combined with the detail review of the lab being after the lab is completed - should it be the other way around? Or better of - should those videos be embedded in the lab instructions??

Most of the videos from this course were really informal, I felt like I was watching a presentation made by someone that was not fully prepared, like without a script or guide. I expect a good narrator for the presentations when I take these courses. Also some of the labs didn't explain all the steps, for example, on one ocassion I was just following instructions to set the permissions and roles without fully comprehending what roles or permissions I was setting up.

The content is great, but there are three problems: 1. The links to the datasets don't work. This is a major issue on the timed labs, as you spend half the time trying to figure out where the info is to get started, instead of doing the labs.

2. Signing in to the cloud console to access things is confusing (particularly if you are signing into Coursera via Google) 3. The UI of google cloud has all been updated since the videos were taken.

Maybe not East Palestine, but this course is a bit of a train-wreck. From contradictory answers, to incomplete information, to old UI versions, and a crazy login process, this course is a trial-by-fire introduction to BigQuery. For what it's worth, the individual videos are great. They're just out of date and you can tell the content has been "stitched together".

Not worth the money in my mind. I have experience so I was bored, and if I didn't I'd likely be lost. They essentially give you queries to run, after introducing you to basic syntax, without you needing to build from scratch. I'm hoping that the next couple classes are better.

Week 6 lab 1 link is not working and not getting proper support from staff. I can see other students also raised the same people and their issues are also not resolved. Please give value to learner's time.

The topic is interesting but it would be better to have more challenging graded exercises and more practice without guidance.

The course was very one dimensional. Not a lot of hands on training.

I have many issues with this course. I'd like to start by saying it was a good overview of BigQuery and really helpful in understanding what I can do with it. So, it accomplished its task. First, there are multiple modules that are out of order, so it randomly jumps hugely in difficulty, and then all of the sudden he "introduces" SQL. This happens a couple times, with different datasets. This is a huge problem and frustrating.Second, a bunch of the course is essentially an advertisement for Google. Which is fine, but it means the course skirts around cost (it's in there, but it's hugely vague and basically just says to look at the website. Why not say the cost of all the queries run in the course? It feels like an afterthought). Also probably about a third of the course is just talking about how great Google is - once again, I get it, but tone it down. Fully understanding cost is important and the length of the course could be considerably reduced by removing redundant Google info. Third, it only made me more confused about what data science IS. The first task of a data scientist, according to the slides, is to analyze, while the first task of a data analyst is to derive. Does the analyst not analyze?? That's a small example but this pattern repeats. I do not understand the dividing line. Data engineering makes more sense.Additionally - the labs didn't give me credit for completion a couple times making me redo them. Also, the SQL data is badly formatted and promotes bad practices IMO - why fix data with queries instead of fixing the schema, the root of the problem, which would save cost and time? I get the point is that data scientists need to cleanse the data, but like I said, that is a ducktape on a leaky pipe. At least mentioning that would be good.Once again, I did get value from the course. However, I think it needs a serious overhaul.