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

4.7
stars
2,415 ratings
389 reviews

About the Course

Welcome to the Coursera specialization, From Data to Insights with Google Cloud Platform brought to you by the Google Cloud team. I’m Evan Jones (a data enthusiast) and I’m going to be your guide. This first course in this specialization is Exploring and Preparing your Data with BigQuery. Here we will see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud Platform. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Cloud Dataprep to analyze and transform your datasets. This course should take about one week to complete, 5-7 total hours of work. By the end of this course, you’ll be able to query and draw insight from millions of records in our BigQuery public datasets. You’ll learn how to assess the quality of your datasets and develop an automated data cleansing pipeline that will output to BigQuery. Lastly, you’ll get to practice writing and troubleshooting SQL on a real Google Analytics e-commerce dataset to drive marketing insights. >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<...

Top reviews

RS
Jan 15, 2019

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

AR
Apr 5, 2020

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

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376 - 389 of 389 Reviews for Exploring ​and ​Preparing ​your ​Data with BigQuery

By Minyoung

May 2, 2020

Too shallow on SQL

By Kirill K

Apr 19, 2021

good but brief

By Vladislav K

Jan 16, 2020

Too easy

By xin h

Jun 22, 2019

too easy

By Weerachai Y

Jul 22, 2020

thanks

By MALLUGALLA B

Jun 27, 2020

good

By Jay D R

May 28, 2020

Good

By Nuril F R

Apr 7, 2021

ok

By 1110 - V S

Mar 31, 2021

-

By Dabblu K S

May 28, 2020

o

By Dmitry S

Mar 19, 2021

Very outdated material.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??

By AntoStain

Sep 10, 2020

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

By Michael S

Jun 13, 2018

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.

By Francisco B

Aug 8, 2019

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