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Data Engineering, Big Data, and Machine Learning on GCP

$44.00

(6 customer reviews)
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Prices differ based on Coursera’s programmes.

Coursera might get your enrollment for free, you only pay for the certificate

This online specialization provides participants a hands-on introduction to designing and building data pipelines on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and derive insights. The course covers structured, unstructured, and streaming data.

This course teaches the following skills:

• Design and build data pipelines on Google Cloud Platform

• Lift and shift your existing Hadoop workloads to the Cloud using Cloud Dataproc.

• Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow

• Manage your data Pipelines with Data Fusion and Cloud Composer.

• Derive business insights from extremely large datasets using Google BigQuery

• Learn how to use pre-built ML APIs on unstructured data and build different kinds of ML models using BigQuery ML.

• Enable instant insights from streaming data

This class is intended for developers who are responsible for:

• Extracting, Loading, Transforming, cleaning, and validating data

• Designing pipelines and architectures for data processing

• Integrating analytics and machine learning capabilities into data pipelines

• Querying datasets, visualizing query results and creating reports

>>> 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 <<<

6 reviews for Data Engineering, Big Data, and Machine Learning on GCP

  1. emem m

    Multiple labs had issues loading. In particular, any labs that required me to open ungit in Datalab would hang for extended periods of time or fail altogether. I would have to reload or reopen the Datalab pages multiple times just to be able to click to the next section. These assignments are timed. The amount of time wasted was not critical to my project success, but they were very frustrating to complete.

    The last lab (before the final module quiz) simply stopped working. I closed the lab and tried again. I closed Coursera and tried again. I closed my browser and tried again. I restarted the computer. After 4 attempts, this process just became too frustrating to be work fighting with – especially since my employer wants me to compare Google Cloud Platform to Microsoft Azure and choose the one that works.

    After working with GCP in this course, I can say that the Google Cloud Platform has exceptional features and API support. However, I have no reason to fight to learn the Google Cloud Platform if the course fails to load key aspects of the Platform in a reliable manner.

  2. peter b

    It felt a bit long in some places but I appreciate the breadth of information being squeezed in a small time frame. The qwik labs and datalab notebooks were great to learn some things by trying them out. I think it would be even better if there was a task to create a simple notebook from scratch – that is something that I will need to do to continue learning.

  3. mangesh k s

    I completed the “Google Cloud Platform Big Data and Machine Learning Fundamentals”. I found that this course has the required material one should get to do hands on. I have seen people in my organization who says that they have given other cloud certification but they didn’t do hands on. And these people who hasn’t done hands on are unable to implement there knowledge in real time scenarios. For example if you have to schedule a job that loads the data (daily) from Datstore (GCS) then these guys says to open a bigquery and to do it manually. Whereas one who has done hands on will schedule a job and will call a wrapper (shell script or Python code) that will do this activity without manual intervention. The one who has not done hands on says why you are running Bigquery from Python or shell script. I am very much found of this course as this course connects you to the every possibility of connecting GCP with real world applications.

  4. yip t w

    It is a great course suitable for any beginner without prior experience and knowledge about Google Cloud Platform.It is aimed for those who interested in Big Data and Machine Learning.Some of the Big Data and Machine Learning products in GCP are explored and learnt about BigQuery using SQL,Cloud Data Pub/Sub,AutoML Vision and lastly about how to build Tensorflow model.The hands-on labs given are helpful and gave step-by-step instructions to the user.The videos are designed to teach someone who is not familiar with common query language,SQL,data modeling and Machine Learning.It is totally fun and enjoyable throughout the learning journey.Can’t wait to learn more and deeper about Machine Learning with Tensorflow in GCP or the specialization for Big Data and Machine Learning!Aim to become Google certified Associate Cloud Engineer next!!

  5. jineesh m

    A course which is must to be done by a beginner who like to come to the cloud world as a beginner. Very interesting sessions mixed up with hands-on labs will give you a lots of confidence to make experiment on GCP without any other tutorials. Almost all the products and technologies are well explained not only the concepts but the actual real world scenario where we can suggest and implement the GCP tools. This course extremely boosted my confidence to go for my next goal which is to become a Google certified Professional Data Engine

  6. Mahesh b

    I took this Google Cloud Platform Big Data and Machine Learning Fundamentals without any knowledge on GCP. I strongly recommend this course for professional who willing to step in to cloud analytics and data engineering . This course really helped me to push more to learn and dive in to MLE and other cloud platform tool. Coursera designed the course in such a way it will be definitely helpful for professionals who want to achieve in Cloud Platform. You can learn at anytime and anywhere.

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