Tue Vu
成为会员时间:2022
白银联赛
21625 积分
成为会员时间:2022
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.
完成中级技能徽章课程“在 BigQuery 中执行预测性数据分析”, 展示以下方面的技能:导入 CSV 和 JSON 文件,在 BigQuery 中创建数据集; 利用 BigQuery 的强大功能与精细的 SQL 分析概念,包括使用 BigQuery ML,根据足球比赛数据 来训练一个进球数预测模型,并评估世界杯进球的观赏性。
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.
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 Managed Service for Apache Spark 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。
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.
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。
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.
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.
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.
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。
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。
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 Data Studio 中 创建报告。
完成 Looker 使用入门技能徽章课程,赢取技能徽章, 在这门课程中,您将学习如何使用 Looker Studio 和 Looker 分析、直观呈现和整理数据。
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.
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.
了解如何在 Google Cloud 上将原始数字转化为清晰、以数据为依据的决策。通过实操实验和实际应用场景,本新手级课程将引导您在 BigQuery 中查询数据、在 Looker 中探索趋势,以及在数据洞察中制作可共享的交互式报告。
完成中级技能徽章课程在 Looker 中管理数据 模型,展示以下方面的技能:维护 LookML 项目的健康状况;利用 SQL Runner 进行数据验证;采用 LookML 最佳实践;优化查询和 报告;以及实现永久性派生表和缓存政策。
完成在 Looker 中构建 LookML 对象入门技能徽章课程,展示以下方面的技能: 构建新的维度、测量、视图和派生表;根据需求设置测量的过滤条件和 类型;更新维度和测量; 构建并优化探索;将视图联接到现有探索;并根据业务需求 决定要创建哪些 LookML 对象。
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.
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.
在本课程中,您将获得在 Looker 中应用高级 LookML 概念 的实践经验。您将学习如何使用 Liquid 自定义和创建动态 维度和测量、创建动态 SQL 派生表和自定义原生 派生表,并运用扩展功能来模块化你的 LookML 代码
在本课程中,您将通过实操练习掌握 Looker 中的 LookML。您将学习如何编写 LookML 代码来创建新的维度和度量、创建派生表并将其与“探索”联接、为“探索”设置过滤条件, 以及在 LookML 中定义缓存政策。
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。