Opiniones y comentarios de aprendices correspondientes a Exploring and Preparing your Data with BigQuery por parte de Google Cloud
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Principales reseñas
I love how this course was well structured. The labs helped excellently in getting hands-on experience with the tools. I highly recommend this one for starting out any analyzing with BigQuery
I thoroughly enjoyed learning about BigQuery and using the Google Data Prep blew my mind! I am planning to use it for my day to day work and also take up more courses about Data prep
351 - 359 de 359 revisiones para Exploring and Preparing your Data with BigQuery
por Vladislav K
•Too easy
por Xin H
•too easy
por Weerachai Y
•thanks
por MALLUGALLA B
•good
por Jay D R
•Good
por Dabblu K S
•o
por AntoStain
•The topic is interesting but it would be better to have more challenging graded exercises and more practice without guidance.
por Michael S
•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.
por Francisco B
•No puedo finalizar el curso, ya que no existe el laboratorio "Lab: Explore and Create an Ecommerce Analytics Pipeline with Cloud Dataprep" en QwikLABS
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