This program provides the skills you need to advance your career in data engineering and recommends training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification. Through a combination of presentations, demos, and labs, you will enable data-driven decision making by collecting, transforming, and publishing data; and you'll gain real world experience through a number of hands-on Qwiklabs projects. You'll also have the opportunity to practice key job skills, including designing, building, and running data processing systems; and operationalizing machine-learning models. Upon successful completion of this program, you will earn a certificate of completion to share with your professional network and potential employers. If you would like to become Google Cloud certified and demonstrate your proficiency to design and build data processing systems and operationalize machine learning models on Google Cloud Platform, you will need to register for, and pass the official Google Cloud certification exam. You can find more details on how to register and additional resources to support your preparation at cloud.google.com/certifications.
关于此 专业证书
您将学到的内容有
- Learn the skills needed to be successful in a data engineer role
- Prepare for the Professional Data Engineer certification
- Learn about the infrastructure and platform services provided by Google Cloud Platform
100% 在线课程
灵活的计划
中级
英语（English）
专业证书是什么？
塑造技能，做好工作准备
无论您是想开始新的职业生涯，还是改变目前职业，Coursera 专业证书都能帮您为开始工作做好准备。选择最适合的时间和地点，自行安排学习进度。立即注册，探索新的职业道路，可免费试用 7 天。您可以随时暂停学习或结束订阅。
实践项目
将您的技能应用到实践项目，并丰富您的简历内容，进而向潜在雇主展示您已为开始工作做好准备。您需要成功完成项目以获得证书。
获得职业证书
当完后计划中的所有课程后，您将获得一张证书。您可以将其在专业网络上分享，并获得使用职业支持资源的权限，这能够为您开启职业生涯提供助力。许多招聘合作伙伴认可我们的许多专业证书，并且我们还有许多合作伙伴可以帮助您准备认证考试。您可以在适用的各个专业证书页面上找到更多信息。
此专业证书包含 6 门课程
Google Cloud Platform Big Data and Machine Learning Fundamentals
This 2-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • Google services are currently unavailable in China.
Modernizing Data Lakes and Data Warehouses with GCP
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud Platform in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. Learners will get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs.
Building Batch Data Pipelines on GCP
Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using QwikLabs.
Building Resilient Streaming Analytics Systems on GCP
*Note: this is a new course with updated content from what you may have seen in the previous version of this Specialization. Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud Platform using QwikLabs.
关于 Google 云端平台
常见问题
退款政策是如何规定的？
我可以只注册一门课程吗？
可以！点击您感兴趣的课程卡开始注册即可开始学习。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某证书的一部分，系统会自动为您订阅完整的证书。访问您的学生面板，跟踪您的进度。
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
How long does it take to complete the Professional Certificate?
This Professional Certificate is designed to be completed in one and a half months at 5 hours per week.
What background knowledge is necessary?
To get the most out of this program, learners should have basic proficiency with a common query language such as SQL; experience developing applications using a common programming language such as Python; experience with data modeling, extract, transform, load activities ; and familiarity with machine learning and/or statistics.
Do I need to take the courses in a specific order?
We strongly recommend you take these courses in order, beginning with Big Data and Machine Learning Fundamentals. This is especially important when completing the Qwiklabs projects, as these hands-on labs build upon the work you complete in preceding courses.
还有其他问题吗？请访问 学生帮助中心。