课程
Select a Google Cloud Database for Your Applications
- 基础技能水平
- 4.7+
- 29 条评价
In this course, you learn to analyze and choose the right database for your needs, to effectively develop applications on Google Cloud.
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观看专家讲师的短视频,然后在浏览器中通过互动练习实践所学内容。
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In this course, you learn to analyze and choose the right database for your needs, to effectively develop applications on Google Cloud.
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This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run.
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This course introduces the Cloud Run serverless platform for running applications.
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This course reviews the essential security features of Model Armor and equips you to work with the service.
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This course is a thrilling mix of expert-led courses and immersive Google Cloud challenges through interactive labs.
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Learn best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
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With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.
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This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.
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Learn to upload, organize, share, and manage files and folders in Google Drive from any device.
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Learn to create and manage events, schedule meetings, share calendars, and use tasks and reminders to stay organized.
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It explores practical methods and tools to implement AI privacy and safety recommended practices.
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Learn about gemini CLI installation and configuration, and introduces use cases and security best practices
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It discusses the importance of AI transparency for developers and engineers.
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Learn to create, format, and collaborate on documents in real time using Google Docs, stored securely in the cloud.
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The course introduces the benefits of Gemini Code Assist and compares the features of the different Gemini Code Assist editions.
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Explore streaming data architectures on Google Cloud with Pub/Sub, Managed Kafka, Dataflow, and BigQuery for real-time data processing.
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Design and operate batch data pipelines on Google Cloud using Dataflow, Serverless Spark, Cloud Composer, and data validation techniques.
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Learn to schedule, host, and manage video meetings in Google Meet, including screen sharing and collaboration tools.
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With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data.
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Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
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You learn best practices for cloud applications, and how to select compute and data options to match your application use cases.
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Secure and monitor GKE production environments. Learn access control, logging, monitoring, CI/CD pipelines, and managed storage integration on Google Cloud.
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Use Agent Search on Gemini Enterprise Agent Platform to provide your website users a generative search experience.
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Learn to message individuals and groups, collaborate in spaces, and integrate Google Chat with other Workspace apps.
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Master Apache Beam and Dataflow foundations including portability, Runner v2, Shuffle Service, Streaming Engine, IAM, quotas, and security.
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In this course, you’ll combine and apply key concepts such as cloud security principles, risk management, and more in an interactive capstone project.
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Work with Gemini AI models in BigQuery for sentiment analysis. Analyze customer reviews using SQL and Python notebooks with Gemini.
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This course introduces you to the core features and functionalities of Gemini Code Assist, an AI-powered app development collaborator for Google Cloud.
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Scale and manage multi-cluster GKE environments. Master fleets, Cloud Service Mesh, identity management, CI/CD at scale, and GKE Enterprise capabilities.
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This course introduces you to event-based applications and teaches you how to use service orchestration and choreography to coordinate microservices.
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数据科学是一个专注于从数据中获取信息的专业领域。数据科学家使用编程技能、科学方法、算法等来分析数据,形成可操作的洞察。
您需要学习 Python 或 R 等编程语言,掌握数学和统计学原理。数据分析方法和数据科学工具的知识也是必不可少的。学习数据科学有很多方法。除了正式的教育途径,如学位或大学学习,还有很多其他资源可以帮助您按自己的节奏学习。除了在线课程和教程,还有书籍、视频等。
除了数学和统计学知识,数据科学家还需要 Python、R 和 SQL 等语言的编程技能。此外,数据科学需要处理大型数据集的能力、数据可视化、数据整理和数据库管理知识。机器学习和深度学习技能也很有用。
在专业领域,几乎每个行业都可以在某种程度上使用数据科学。医疗机构使用数据科学来检测和治疗疾病,金融公司用它来检测和预防欺诈。各种行业都将数据科学用于营销,如构建推荐系统和分析客户流失。
是的,数据科学是美国和全球增长最快的行业之一。它也是薪酬最高的职业之一。根据 Payscale 的数据,在美国,有经验的数据科学家平均收入为 97,609 美元,满意度评分为五星中的四星。
这里有几个需要考虑的因素。首先,数据科学学位的竞争可能很激烈,通常需要持续的高分。同样,数据科学所需的许多技能需要大量的学习和耐心。掌握所有必要的基础知识可能需要几个月的时间,还需要大量的实践经验才能获得入门级职位。
是的,您需要一些 Python、R、SQL、Java 和 C/C++ 等语言的编程经验。不过,由于语法相对简单,Python 编程语言通常是新手的首选。
对于没有编程经验和/或数学背景的人来说,通常需要 7 到 12 个月的密集学习才能达到入门级数据科学家的水平。但是,重要的是要记住,仅仅学习数据科学的理论基础可能不会让您成为真正的数据科学家。
掌握数据科学基础后,您可以专攻各种领域,包括机器学习、人工智能、大数据分析、商业分析和智能、数据挖掘等。
借助我们的移动课程和每日 5 分钟编程挑战,随时随地取得进步。