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在 Google Cloud 控制台中运用您的技能

Tue Vu

成为会员时间：2022

白银联赛

21625 积分
Exploring and Preparing your Data with BigQuery Earned Oct 18, 2023 EDT
Perform Predictive Data Analysis in BigQuery Earned Oct 17, 2023 EDT
DEPRECATED Applied Data: Blockchain Earned Oct 17, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Oct 16, 2023 EDT
DEPRECATED BigQuery for Marketing Analysts Earned Oct 16, 2023 EDT
使用 BigQuery 构建数据仓库 Earned Oct 16, 2023 EDT
DEPRECATED BigQuery for Data Warehousing Earned Oct 16, 2023 EDT
Data Catalog Fundamentals Earned Oct 15, 2023 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned Oct 13, 2023 EDT
面向机器学习的 BigQuery Earned Oct 12, 2023 EDT
通过 BigQuery ML 创建机器学习模型 Earned Oct 12, 2023 EDT
从 BigQuery 数据中挖掘数据洞见 Earned Oct 12, 2023 EDT
Get Started with Looker Earned Oct 11, 2023 EDT
Developing Data Models with LookML - Locales Earned Oct 11, 2023 EDT
The Modern Data Platform and LookML Earned Oct 11, 2023 EDT
Google Cloud 数据分析功能简介 Earned Oct 11, 2023 EDT
Manage Data Models in Looker Earned Oct 10, 2023 EDT
Build LookML Objects in Looker Earned Oct 10, 2023 EDT
Developing Data Models with LookML Earned Oct 10, 2023 EDT
Analyzing and Visualizing Data in Looker Earned Oct 10, 2023 EDT
Applying Advanced LookML Concepts in Looker Earned Oct 9, 2023 EDT
Understanding LookML in Looker Earned Oct 9, 2023 EDT
为 Looker 信息中心和报告准备数据 Earned Oct 9, 2023 EDT

In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets. This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.

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Complete the intermediate Perform Predictive Data Analysis in BigQuery skill badge course to demonstrate skills in the following: creating datasets in BigQuery by importing CSV and JSON files; harnessing the power of BigQuery with sophisticated SQL analytical concepts, including using BigQuery ML to train an expected goals model on soccer event data and evaluate the impressiveness of World Cup goals.

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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。 技能徽章是 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度； 您需要在交互式实操环境中参加考核，证明自己运用所学知识的能力后 才能获得。完成此技能徽章课程和作为最终评估的实验室挑战赛， 获得数字徽章，在您的人际圈中炫出自己的技能。

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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

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想要仅使用 SQL 就能在几分钟内构建机器学习模型，而不是花费数小时？BigQuery 借助机器学习，数据分析师能够使用现有的 SQL 工具和技能创建、训练、评估机器学习模型，并使用这些模型进行预测， 从而实现机器学习的普及。在 本系列实验中，您将尝试不同的模型类型，并了解 如何构建出色的模型。

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完成中级技能徽章课程通过 BigQuery ML 创建机器学习模型，展示您在以下方面的技能： 使用 BigQuery ML 创建和评估机器学习模型，以执行数据预测。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您在 Google Cloud 产品与服务方面的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能 徽章课程和作为最终评估的实验室挑战赛，即可获得技能徽章， 在您的人际圈中炫出自己的技能。

了解详情

完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”，展示您在以下方面的技能： 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误，以及通过连接到 BigQuery 数据在 Looker Studio 中 创建报告。

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Earn a skill badge by completing the Get Started with Looker skill badge course, where you learn how to analyze, visualize, and curate data using Looker Studio and Looker.

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This course, Developing Data Models with LookML - Locales, is intended for non-English learners only. To enroll in this content in English, please enroll in Developing Data Models with LookML. This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.

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在本新手级课程中，您将了解 Google Cloud 数据分析工作流，以及可用于探索、分析和直观呈现数据并与相关人员共享发现结果的工具。结合案例研究、实操实验、讲座和测验/演示，本课程展示了如何将原始数据集转化为纯净数据，进而转化为实用的可视化图表和信息中心。无论您是已经在从事数据工作并想了解如何通过 Google Cloud 取得成功，还是在寻求职业发展，都可以借助本课程迈出第一步。几乎所有在工作中执行或使用数据分析的人都可以从本课程中受益。

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Complete the intermediate Manage Data Models in Looker skill badge course to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies.

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Complete the introductory Build LookML Objects in Looker skill badge course to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements.

了解详情

This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

了解详情

In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.

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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.

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完成为 Looker 信息中心和报告准备数据入门级技能徽章课程， 展现您在以下方面的技能：对数据进行过滤、排序和透视；将来自不同 Looker 探索的结果合并； 以及使用函数和运算符构建 Looker 信息中心和报告以用于数据分析和可视化。

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