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Data Engineering, Big Data, and Machine Learning on GCP Course

Data Engineering, Big Data, and Machine Learning on GCP Course Course

The "Data Engineering, Big Data, and Machine Learning on GCP" specialization offers a comprehensive and practical approach to data engineering and machine learning on Google Cloud Platform. It's parti...

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Data Engineering, Big Data, and Machine Learning on GCP Course on Coursera — The "Data Engineering, Big Data, and Machine Learning on GCP" specialization offers a comprehensive and practical approach to data engineering and machine learning on Google Cloud Platform. It's particularly beneficial for individuals seeking to build and deploy data solutions in cloud environments.

Pros

  • Taught by experienced instructors from Google Cloud.
  • Hands-on labs and projects to solidify learning.
  • Flexible schedule accommodating self-paced learning.
  • Applicable to both academic and industry settings.

Cons

  • Requires prior experience in Python and a basic understanding of cloud computing concepts.
  • Some learners may seek more advanced topics beyond the scope of this specialization.

Data Engineering, Big Data, and Machine Learning on GCP Course Course

Platform: Coursera

What you will learn in Data Engineering, Big Data, and Machine Learning on GCP Course

  • Understand the roles and responsibilities of a data engineer.

  • Design and build data processing systems on Google Cloud Platform (GCP).

  • Build end-to-end data pipelines using GCP tools and services.

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  • Analyze data and carry out machine learning tasks on GCP.

  • Prepare for the Google Cloud Professional Data Engineer certification.​

Program Overview

Modernizing Data Lakes and Data Warehouses with Google Cloud

⏱️8 hours

  • Differentiate between data lakes and data warehouses.
  • Explore use-cases for each type of storage and the available solutions on GCP.
  • Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.
  • Examine why data engineering should be done in a cloud environment.​

Building Batch Data Pipelines on Google Cloud

⏱️17 hours

  • Review different methods of data loading: EL, ELT, and ETL.
  • Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs.
  • Build data processing pipelines using Dataflow.
  • Manage data pipelines and monitor their performance.​

Building Resilient Streaming Analytics Systems on Google Cloud

⏱️ 12 hours

  • Design streaming data pipelines using Pub/Sub and Dataflow.
  • Implement real-time analytics solutions.
  • Ensure reliability and scalability in streaming systems.
  • Monitor and troubleshoot streaming data pipelines.

Smart Analytics, Machine Learning, and AI on Google Cloud

⏱️ 12 hours

  • Explore Google’s AI and machine learning tools.
  • Implement machine learning models using BigQuery ML and Vertex AI.
  • Integrate AI solutions into data pipelines.
  • Understand the ethical considerations in AI and machine learning.​​

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Job Outlook

  • Proficiency in data engineering and machine learning on GCP is essential for roles such as Data Engineer, Machine Learning Engineer, and Cloud Data Engineer.

  • Skills acquired in this specialization are applicable across various industries, including technology, healthcare, finance, and more.

  • Completing this specialization can enhance your qualifications for positions that require expertise in big data and machine learning on cloud platforms.

Explore More Learning Paths

Enhance your cloud and data engineering skills with these curated courses designed to provide hands-on experience in big data, machine learning, and Google Cloud Platform (GCP) services.

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Support your understanding of data-driven solutions:

  • What Does a Data Engineer Do? – Explore the role of data engineers, their responsibilities, and the tools they use to manage, process, and optimize large-scale data systems.

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FAQs

Who will benefit most from this specialization, and what career outcomes can it support?
Designed for roles such as Data Engineers, Cloud Engineers, or professionals preparing for the Google Cloud Professional Data Engineer certification. Skills gained include designing scalable pipelines, real-time analytics, and integrating ML into data workflows—useful in startup to enterprise contexts. Completers receive a Google Cloud specialization certificate via Coursera, improving visibility on LinkedIn and supporting credential validation.
What are the strengths and limitations of this specialization?
Strengths: Developed by Google Cloud Training with real-world relevance and trusted industry authority. Strong 4.6/5 rating from over 12,500 learners. Applied learning approach using Qwiklabs to reinforce knowledge with hands-on labs. Limitations: Focuses on foundational tools and workflows; learners seeking advanced AI or deep data engineering expertise may need supplementary training. Emphasizes GCP—less relevant for those focused on on-prem or multi-cloud environments.
What topics, tools, and skills are covered in the specialization?
Modernizing Data Lakes & Warehouses: Data lake vs. warehouse concepts, data pipeline roles, cloud-native storage. Batch Data Pipelines: ETL/ELT workflows using Dataflow, Dataproc, Data Fusion, and Cloud Composer. Streaming Analytics Systems: Real-time data ingestion with Pub/Sub, streaming transforms with Dataflow, analysis with BigQuery. Smart Analytics, ML & AI: ML API integration, BigQuery ML, Vertex AI AutoML, TensorFlow use in notebooks. Hands-On Labs: Labs run on Qwiklabs, offering real practical experience with GCP tools like BigQuery and Dataflow.
What prior experience or skills are needed before enrolling?
The course is Intermediate-level, ideal for those with some technical or data experience. Recommended background includes: Familiarity with SQL, data modeling, or ETL processes Experience coding in Python Exposure to statistics, data engineering concepts, or cloud infrastructure basics
How long does the specialization take, and is it self-paced?
Consists of 4 courses, designed to be completed in approximately 4 weeks at 10 hours per week (~40 hours total). A longer self-paced schedule—like 9 to 17 weeks at 3–4 hours/week—is also common, especially if balancing other commitments. Fully self-paced, allowing you to learn at your own rhythm.

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