Online Course – Google Cloud Certified Professional Specialization in Data Engineering, Big Data, and Machine Learning

Data Engineering Specialization Course on Google Cloud Platform. Start a career in data engineering and unlock the power to create business value with big data and machine learning.

Suggested by: Coursera (What is Coursera?)

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Design and build data processing systems on Google Cloud Platform
  • Using Cloud Dataproc with Spark and ML API to work with unclassified data
  • Implement auto-scaling data pipelines in Cloud Dataflow for processing packet and streaming data
  • Extract business information from huge data sets using Google BigQuery
  • Train, evaluate, and predict machine learning models using TensorFlow and Cloud ML
  • Performing rapid analysis from flow data

What you will learn in the course

Courses for which the course is suitable

  • Data processing systems developer
  • Data Analyst
  • Data Engineer
  • Big Data Developer
  • Machine Learning Expert
  • Data Project Manager
  • Data Pipeline Developer
  • Business Analyst
  • Statistical Modeler
  • Solutions developer on Google Cloud Platform

Specialization – 5-lesson course series

This 5-week online course series is designed to teach you how to design and build data processing systems on Google Cloud Platform. During the course, you will learn about designing data processing systems, building end-to-end data pipelines, analyzing data, and performing machine learning.

Learned skills

  • Design and build data processing systems on Google Cloud Platform
  • Using Cloud Dataproc with Spark and ML API to work with unclassified data
  • Implement auto-scaling data pipelines in Cloud Dataflow for processing packet and streaming data
  • Extract business information from huge data sets using Google BigQuery
  • Train, evaluate, and predict machine learning models using TensorFlow and Cloud ML
  • Performing rapid analysis from flow data

Target audience

The course is designed for developers with experience involved in big data management, including:

  • Performing the search, loading, conversion, cleaning and data validation phase
  • Designing pipelines and architectures for data processing
  • Creating and maintaining machine learning and statistical models
  • Query data sets and display the results in a visual report

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Hands-on Learning Project

This course includes hands-on labs. To register, you need a Google account (you can use your Gmail account) and you also need to sign up for a free trial account on Google Cloud Platform. The free trial is valid for 12 months or until you use up $300 in credit, whichever comes first. Therefore, this course can be completed in 4 weeks.

These hands-on components allow you to apply the skills you learned in the video course. The project includes topics like Google BigQuery that are flexible and designed for use in coding workshops.

Details of the courses that make up the specialization

Big Data and Machine Learning Fundamentals in Google Cloud

Course 1

9 hours
4.5 (240 ratings)

  • Understand the data lifecycle in Google Cloud and the key products in big data and machine learning
  • Design data pipelines using Dataflow and Pub/Sub
  • Perform big data analysis using BigQuery
  • Understand the different ways to build ML solutions on Google Cloud

Modernize data pools and data warehouses with GCP

Course 2
8 hours
4.3 (48 ratings)

  • Understand the differences between data sinks and data warehouses
  • Understand the use cases of all storage types
  • Understand the role of the data engineer and the benefits that efficient data pipelines bring to the business
  • Discover why you should do data engineering in a cloud environment

Building data pipelines in batches on GCP

Course 3
17 hours
4.4 (29 ratings)

  • Explore different ways to load data: EL, ELT, ETL
  • Broadcast Hadoop while running Dataproc
  • Build data processing pipelines with Dataflow
  • Manage data pipelines with Data Fusion and Cloud Composer

Building robust streaming analytics systems on GCP

Course 4
10 hours
4.8 (18 ratings)

  • Interpret real-time streaming analytics use cases
  • Manage data events using the Pub/Sub asynchronous messaging service
  • Write streaming pipelines with conversions when needed
  • Stream data in real time and perform analytics using Dataflow, BigQuery, and Pub/Sub

Smart analytics, machine learning, and artificial intelligence on GCP

Course 5
6 hours
4.5 (31 ratings)

  • Understand the differences between ML, AI, and deep learning
  • Use ML APIs for unstructured data
  • Get BigQuery commands out of Notebooks
  • Create ML models with SQL in BigQuery

Skills you will develop

  • TensorFlow
  • BigQuery
  • Google Cloud Platform
  • Cloud computing