Learner Reviews & Feedback for Exploring ​and ​Preparing ​your ​Data with BigQuery by Google Cloud

Good course, but I was plagued by Qwiklabs issues, otherwise 4 stars due to sometimes strange jumps between topics.

Basic introduction to SQL analytics queries.About half of the videos just give the explanation of the Lab.

One of the main reason I am giving it a 3 star is because the labs are very disorganized and not clear clarity on why the system is broken with linking with coursera after specific labs are complete. The knowledge gained about BigQuery and how data transformation can be done is good. However, the quizes arent hard or challenging at all. I feel it can use a bit of more logical questions which may cause you to think. Plus it kind of sounds promotional for google itself with this course where they talk a lot about the features and how overly impressive this. Come on Google. Let us decide that.

The video is too long, I recommend people just finished the Quiz and labs and lad solutions. That is enough.

the labs can be little bit more clear. they are not updated to the new cloud dataprep environment, and some situations I could not find the correct data-insight data. That is frustrating.

Starts out as mostly a product pitch, is fairly light on actual BigQuery / SQL skill-building & mental model-forming. Last piece of last module (on DataPrep) is entirely different, with much longer & more extensive lab exercises. Feels very uneven overall, and wouldn't be my preferred intro to GBQ (it wasn't). Despite these shortcomings, is a fairly decent intro. Will see how subsequent courses in the specialization are.

Very basic in some parts. I wonder if you really have to learn about SQL or about the advantages of cloud vs on premise computing when you are taking this specialization

Very good for the starter. A bit too simple to who already expert in SQL

too easy

How one can deal with job fail in dataprep would be nice. Thanks :)

good concept , but course not update big query ui is already changed

Last lab was interesting. Previous lessons where boring for someone who knows SQL.

Unclear if I will continue given the difficulties with Quicklabs integration. This class is overall pretty interesting but IAM needs to be much simpler. There must be a way to provide a provisional account for the enitrety of the course instead of having to get temp logins .. It is just so cumbersome and the time constraint on labs is not practical/required. The goal is to learn about GCP not race through the labs to meet a time quota.

lots of system error in lab 4

The labs do not work. I would error out.

Disappointing.

Badly structured, half-baked explanations. Teacher is a sound analyst but lousy teacher and incapable of communicating concepts clearly.

Expected better from Google.

This is essentially an ad for Google services you have to pay for.

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.

No puedo finalizar el curso, ya que no existe el laboratorio "Lab: Explore and Create an Ecommerce Analytics Pipeline with Cloud Dataprep" en QwikLABS

very basic and verbose