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所在平台: Udemy |
课程主页: https://www.udemy.com/course/google-cloud-professional-data-engineer-get-certified/
课程评论：没有评论
课程名称：Google Cloud专业数据工程师：认证准备2022 课程概述：数据工程师的需求持续增长，获得认证的数据工程师通常是薪酬最高的专业人士之一。数据工程师需要具备多种技能，包括设计系统以摄取大量数据、以具成本效益的方式存储数据，以及使用从报告和可视化到机器学习的工具高效处理和分析数据。获取Google Cloud专业数据工程师认证证明您具备构建、调整和监控高性能数据工程系统的知识和技能。 本课程由官方Google Cloud专业数据工程师考试指南的作者以及拥有超过20年数据库、数据架构和机器学习经验的数据架构师设计和开发。课程结合讲座、测验和动手实践，确保您理解如何摄取数据、在Cloud Dataflow中创建数据处理管道、部署关系数据库、设计高性能的Bigtable、BigQuery和Cloud Spanner数据库、查询Firestore数据库，以及使用Cloud Dataproc创建Spark和Hadoop集群。 课程的最后部分将专注于考试中最具挑战性的部分：机器学习。如果您对反向传播、随机梯度下降、过拟合、欠拟合和特征工程等概念不熟悉，那么您还未准备好参加考试。幸运的是，本课程旨在帮助您。从机器学习的基础开始，介绍监督学习和非监督学习之间的区别，我们将逐步深入理解如何设计、训练和评估机器学习模型。在此过程中，我们将解释通过专业数据工程师考试所需理解的基本概念，并回顾Google Cloud机器学习服务和基础设施，例如BigQuery ML和张量处理单元（TPU）。 课程还包括一项50题的练习考试，用于测试您对数据工程概念的知识，并帮助您识别需要进一步学习的领域。通过本课程，您将准备好使用Google Cloud数据工程服务设计、部署和监控数据管道，部署先进的数据库系统，构建数据分析平台，并支持生产机器学习环境。您准备好参加考试了吗？加入我，我将告诉您如何实现目标！
The need for data engineers is constantly growing and certified data engineers are some of the top paid certified professionals. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning. Earning a Google Cloud Professional Data Engineer certification demonstrates you have the knowledge and skills to build, tune, and monitor high performance data engineering systems.This course is designed and developed by the author of the official Google Cloud Professional Data Engineer exam guide and a data architect with over 20 years of experience in databases, data architecture, and machine learning. This course combines lectures with quizzes and hands-on practical sessions to ensure you understand how to ingest data, create a data processing pipelines in Cloud Dataflow, deploy relational databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc. The final portion of the course is dedicated to the most challenging part of the exam: machine learning. If you are not familiar with concepts like backpropagation, stochastic gradient descent, overfitting, underfitting, and feature engineering then you are not ready to take the exam. Fortunately, this course is designed for you. In this course we start from the beginning with machine learning, introducing basic concepts, like the difference between supervised and unsupervised learning. We'll build on the basics to understand how to design, train, and evaluate machine learning models. In the process, we'll explain essential concepts you will need to understand to pass the Professional Data Engineer exam. We'll also review Google Cloud machine learning services and infrastructure, such as BigQuery ML and tensor processing units.The course includes a 50 question practice exam that will test your knowledge of data engineering concepts and help you identify areas you may need to study more.By the end of this course, you will be ready to use Google Cloud Data Engineering services to design, deploy and monitor data pipelines, deploy advanced database systems, build data analysis platforms, and support production machine learning environments.ARE YOU READY TO PASS THE EXAM? Join me and I'll show you how!
