Data Engineering, Big Data, and Machine Learning on GCP
This online specialization provides participants a hands-on introduction to designing and building data pipelines on Google Cloud Platform.
Tensorflow Bigquery Google Cloud Platform Cloud Computing
Accessible for free. Completion certificates are offered.
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Google Cloud Training
This specialization includes these 5 courses.
Reddit Posts and Comments
2 posts • 16 mentions • top 15 shown below
28 points • polpenn
Thoughts on the "Data Engineering on Google Cloud Platform Specialization" from Coursera?
Here's the link to the specialization:
I'm thinking about paying for the entire specialization to learn how to deploy machine learning on an offsite server. I tried reading the documentation and working with the examples from the Google Cloud Platform website but I still feel lost about how everything fits together. I have taken a few machine learning courses but I feel completely loss when it comes to this stuff. Any thoughts on this specialization?
6 points • burgerboy9n
Hey there! I work on Google Cloud. You should checkout our courses on Coursera. For example, here is our Data Engineering, Big Data, and Machine Learning . Let me know if you have any questions.
5 points • fhoffa
Coursera: Data Engineering on Google Cloud Platform Specialization
5 points • blurarara
There is a course in coursera that uses GCP: https://coursera.org/specializations/gcp-data-machine-learning
3 points • joshlaird
I was surprised as 20% of the exam were machine learning questions; not just on the ML services but ML theory such as ways to reduce loss. Other surprise was a few questions on Stackdriver to see how your different services are performing.
I use GCP daily in my job so I had it easier but I'd say the best way to revise is to get your hands dirty using the services.
There are also multiple courses out there, in particular the Data Engineering course provided by Google themselves on Coursera was great: https://www.coursera.org/specializations/gcp-data-machine-learning. As this course gives you theory as well as hands on practice
2 points • fhoffa
Coursera: Data Engineering on Google Cloud Platform Specialization
5 points • johnreese421
"Associate Cloud Engineer" and "Associate cloud engineer"
Hello all. I am willing to get GCP certified which would add some weight on my resume.
So, I am still in way of gathering material/resources to start on.
According to my understanding of looking up various things online : first step having "Associate Cloud Engineer" certification and then going for "Professional Data Engineer" certification is a good way .
what are your thoughts on it.? and would the coursera course link ( https://www.coursera.org/specializations/gcp-data-machine-learning ) be good enough for passing both the certifications or separate course is to be done for "Associate cloud engineer" certification.?
Please let me know if you have any other resources/material suggestions .
3 points • _WeWereHere
Great idea, thanks for putting this together. You could add the two courses on Coursera and Udacity:
1 points • ASamir
The intern position had these in the requirements:
1- You’ve dabbled in high volume data, preferably with distributed systems such as Hadoop, BigTable, and Cassandra.
2- You’ve had exposure to data modeling, data access, and data storage techniques.
I signed up to take Google Cloud's Coursera Specialization " Data Engineering on Google Cloud Platform" but I still don't think it's enough. The application deadline is almost 2 months from today! Other than being confident in using Python and SQL, I'm looking for project ideas that could help me demonstrate my skills and interest in being a data engineer.
1 points • ethanenglish
If you're learning Google Cloud Platform, I'd suggest this Coursera course: https://www.coursera.org/specializations/gcp-data-machine-learning
2 points • NuwandaPython
I'm currently doing the Microsoft Professional Program Certification in Data Science and actually had its curriculum reviewed by Data Scientists friends, most of them say that this certificate has most of the skills required to get a decent data analyst entry level job. I'm halfway through the 11 courses and I couldn't be happier with the learning, just wishing it has more practical content that could help me further with my side projects. That's why my friends also told me to make sure that I do: projects way beyond what the certification requires to get the most out of the experience. Another one here on reddit also found a job after 7 months and he also took this certificate. Read more about that here: https://www.reddit.com/r/datascience/comments/71lsm1/after_7_months_ive_finally_made_a_career_change/
On a related note, I will also be taking the MITx micromasters via audit to further solidify my knowledge and skills. If you can program in Python already, I could say that the nanodegree and the micromasters would benefit you more in the long run. You could also try checking out Boston University's Online Graduate Certificate in Data Analytics as I have seen that this is the more comprehensive offering among other versions of its kind like the Cornell (from an Ivy so might be expensive but you can't discount that it's an Ivy credential) Data Analytics Certificate. If I had all the money in the world, I would make sure to take this.
1 points • Massnsen
Resources to learn GCP
I recently accepted a job ( junior Data Engineer) in a company that uses GCP services : BigQuery, GCS, Composer, Cloud Build ...etc.
It's my first time with cloud services and I want to learn the fundamentals. I did some googling and I found labs from Qwiklabs For 55$/Month. I would like to do hands on learning but certainly without giving up on the theoritical part.
I found some other ressources but I'm kind of lost, maybe you can help me.
- Linux Academy for 49$/Month
- Coursera for 43$/Month
- Google Developers it's free but, at first sight, not as structured as Qwiklabs
If there are any other resources I would really be happy to have your advice. As I said, I'm very interested in the "why" not only the "how to" if the resource (maybe a book) can give intuitions on desing choices of GCP Ii would be a great plus.
Thank you :)
1 points • ethanenglish
Google Cloud Platform Data Engineering Exam
I'm preparing to take the GCP Data Engineering Exam in January and wondering the best approach to studying from people that have passed. I've been working in GCP for the last year and passed the Coursera GCP Data Engineering Specialization. I took the practice exam and got a 60% -- not great so I started studying.
The exam case studies seem important and I've been told to read the documentation for Google's various products. Initially, I was reading through the core concepts of Storage, and created a massive pile of notes. Not sure that's sustainable for each product.
I'm trying a different approach: going through the case studies, understanding each of them, then going through each product and to figure out how, as a GCP Data Engineer, would help in each scenario.
Does that align other people's study habits? What did you do to pass? Detail would be very helpful.
1 points • DataNerd555
I don't know if you have the funds, but I would recommend checking out the data engineering nano-degree at udacity. If you apply during these times you can get a 40% discount because of COVID-19. This should give you the basics. https://www.udacity.com/course/data-engineer-nanodegree--nd027
I think you made a good call with data engineering, this is a really good role and frankly not enough people are applying for this kind of job. Too many people are trying for data science which is getting very saturated, but data engineering has plenty of void that needs to be filled.
Your work experience as a bank teller and a VW sales representative are not tech, which is bad, but you need to tell your employer during the interview that it show cases that you understand the working environment, how to communicate and collaborate with others and all the rest of those soft skills.
As far as the degree goes, you don't really need more schooling. What you need is proven experience that you can get the job done. For that, you need to convince the employer. Most employers will evaluate you based on:
- experience (not an option in this case),
- interview challenges (totally possible to prepare) [go to leetcode or find practice data engineering interview questions online]
- past projects (totally possible to prepare). [udaccity, udemy, coursera, online]
Your best bet is to break into a company that will teach you and you pick up the skills and the title and grow from there. I don't know how much you have gotten your feet wet with data engineering, but I would do a few projects and certificate just to familiarize yourself and boost portfolio (unless you know of someone that would give you internship or job right off the bat, if that is the case place the experience as the top priority)/
- Udacity has a data engineering nanodegree
- GCP offers this certificate through coursera
- Specialization in coursera
Also a good tip that would be helpful for your to break into your first job is applying for startups (cause once you get that first data engineering job it becomes a lot easier,) Generally, startups have a much lower barrier to entry and frankly need data engineers too. They will take a chance on you more than big companies. Once your there, you will be getting very hands on and learning fast and after a year or so you have that title and proven record and can jump somewhere else.
Happy to talk more but would love thoughts.
1 points • groovy-sky
April's free IT courses
Looks like April is the best time to start studying something new: Free courses from Google: * Architecting with Google Compute Engine Specialization * Data Engineering, Big Data, and Machine Learning on GCP Specialization * Machine Learning with TensorFlow on Google Cloud Platform Specialization
Free courses from RedHat: * [Red Hat Enterprise Linux Technical Overview] (https://www.redhat.com/en/services/training/rh024-red-hat-linux-technical-overview) * Red Hat Agile Integration Technical Overview * Ansible Essentials: Simplicity in Automation Technical Overview * Deploying Containerized Applications Technical Overview * Red Hat Satellite Technical Overview * Red Hat OpenStack Technical Overview * Virtualization and Infrastructure Migration Technical Overview
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