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Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

• Create ML models using BigQuery ML, APIs, and AutoML.
• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
• Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
• Hire BigQuery and Cloud SQL for interactive data analysis

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This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Scaling Data Analytics
• Introducing Google Cloud Platform ‘
• Data Processing Architectures
• Machine Learning
• Compute and Storage Fundamentals
• Additional Resources
• Data Analytics on the Cloud

Reference: https://cloud.google.com/certification/data-engineer

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Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.