开始时间: 12/21/2023 持续时间: Approximately 4 months to complete Suggested pace of 4 hours/week
所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/specializations/gcp-data-machine-learning
课程评论:没有评论
This online specialization provides participants a hands-on introduction to designing and building data pipelines on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and derive insights. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data pipelines on Google Cloud Platform • Lift and shift your existing Hadoop workloads to the Cloud using Cloud Dataproc. • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Manage your data Pipelines with Data Fusion and Cloud Composer. • Derive business insights from extremely large datasets using Google BigQuery • Learn how to use pre-built ML APIs on unstructured data and build different kinds of ML models using BigQuery ML. • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Integrating analytics and machine learning capabilities into data pipelines • Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
GCP专业化方面的数据工程,大数据和机器学习:此在线专业化课程向参与者提供了动手介绍,以介绍如何在Google Cloud Platform上设计和构建数据管道。通过演示,演示和动手实验的结合,参与者将学习如何设计数据处理系统,建立端到端数据管道,分析数据并获得见解。该课程涵盖结构化,非结构化和流数据。 本课程教授以下技能: •在Google Cloud Platform上设计和建立数据管道 •使用Cloud Dataproc将现有的Hadoop工作负载提升并转移到云中。 •通过在Cloud Dataflow上实现自动扩展数据管道来处理批处理和流数据 •使用数据融合和Cloud Composer管理数据管道。 •使用Google BigQuery从庞大的数据集中获得业务见解 •了解如何在非结构化数据上使用预构建的ML API,以及如何使用BigQuery ML构建不同种类的ML模型。 •从流数据中获得即时见解 此类适用于负责以下工作的开发人员: •提取,加载,转换,清洁和验证数据 •设计用于数据处理的管道和架构 •将分析和机器学习功能集成到数据管道中 •查询数据集,可视化查询结果并创建报告 &gt;&gt;&gt;通过注册该专业,您同意FAQ中列出的Qwiklabs服务条款,该条款位于:https://qwiklabs.com/terms_of_service&lt;&lt;&lt;Course Link: https://www.coursera.org/learn/gcp-big-data-ml-fundamentals
Name:Google Cloud Big Data and Machine Learning Fundamentals
Description:Offered by Google Cloud. This course introduces the Google Cloud big data and machine learning products and services that support the ... Enroll for free.
Course Link: https://www.coursera.org/learn/data-lakes-data-warehouses-gcp
Name:Modernizing Data Lakes and Data Warehouses with Google Cloud
Description:Offered by Google Cloud. The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for ... Enroll for free.
Course Link: https://www.coursera.org/learn/batch-data-pipelines-gcp
Name:Building Batch Data Pipelines on Google Cloud
Description:Offered by Google Cloud. Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load ... Enroll for free.
Course Link: https://www.coursera.org/learn/streaming-analytics-systems-gcp
Name:Building Resilient Streaming Analytics Systems on Google Cloud
Description:Offered by Google Cloud. Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics ... Enroll for free.
Course Link: https://www.coursera.org/learn/smart-analytics-machine-learning-ai-gcp
Name:Smart Analytics, Machine Learning, and AI on Google Cloud
Description:Offered by Google Cloud. Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from ... Enroll for free.
谷歌云平台的数据工程专项课程系列(Data Engineering on Google Cloud Platform Specialization)” ,该系列课程由Google云平台制作,包含5门子课程,内容主要包含Google云平台上的大数据分析和机器学习,以及相关的工具的使用,例如BigQuery、 Tensorflow等,感兴趣的同学可以关注: Data Engineering on Google Cloud Platform Specialization-Launch your career in Data Engineering. Deliver business value with big data and machine learning.