01
Google Cloud 實作研究室導覽
在第一個實作研究室中，您會存取 Google Cloud 控制台並使用下列 Google Cloud 基本功能：專案、資源、IAM 使用者、角色、權限和 API。
Data Analyst Learning Path
A Data Analyst gathers and analyzes data to identify trends and develops valuable insights to help solve problems and inform business decisions. They have skills in data cleansing, visualization, and analysis including an understanding of data ethics and tooling.
A Data Analyst gathers and analyzes data to identify trends and develops valuable insights to help solve problems. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the Data Analyst role. Once you complete the path, check out our catalog for 700+ labs and courses to keep going on your professional journey.
02
Introduction to Data Analytics on Google Cloud
This introductory course explores the basics of data analysis, including collection, storage, exploration, visualization, and sharing. This course also introduces Google Cloud's data analytics tools and services. Through video lectures, demos, quizzes, and hands-on labs, this course demonstrates how to...
03
Analyzing and Visualizing Data in Looker
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...
04
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...
05
Prepare Data for ML APIs on Google Cloud
Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and...
06
Prepare Data for Looker Dashboards and Reports
Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge to demonstrate skills in the following: filtering, sorting, and pivoting data, merging results from different Looker Explores, and using functions and operators to build Looker dashboards and reports...
07
Derive Insights from BigQuery Data
Complete the introductory Derive Insights from BigQuery Data skill badge to demonstrate skills in the following: write SQL queries, query public tables, load sample data into BigQuery, troubleshoot common syntax errors with the query validator in BigQuery, and create reports...
08
Create ML Models with BigQuery ML
Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in the following: creating and evaluating machine learning models with BigQuery ML to make data predictions. A skill badge is an exclusive digital badge issued by...
09
Applying Advanced LookML Concepts in Looker
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...
10
Data Catalog Fundamentals
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...
11
Build LookML Objects in Looker
Complete the introductory Build LookML Objects in Looker skill badge 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...
12
Manage Data Models in Looker
Complete the intermediate Manage Data Models in Looker skill badge 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...