- Course on demand
Perform Foundational Data, ML, and AI Tasks in Google Cloud
Familiarize yourself with big data, machine learning and artificial intelligence. Get started with Google Cloud tools like BigQuery, Cloud Speech API, and AI Platform. Upon completion of the quest, you will be able to earn a Google Cloud skill badge.
Exploring Data with Looker
Learn the basics of data mining in Looker and learn how to put that knowledge into practice using your company’s data in the mining interface. Complete this quest to receive a Google Cloud skill badge.
Build LookML Objects in Looker
Familiarize yourself with the basics of Looker, from design to creating and updating dimensions and measurements. You will also learn how to create, refine and join existing Explores. Complete this quest to receive a Google Cloud skill badge.
Insights from Data with BigQuery
Learn about basic BigQuery features, such as writing SQL queries, creating database tables in Cloud SQL, querying public tables, and more. Upon completion of the quest, you will be able to earn a Google Cloud skill badge.
Create ML Models with BigQuery ML
Learn about the characteristics of a good machine learning model. Learn how to build and evaluate machine learning models, and make predictions using BigQuery ML. Complete the quest and get a skill badge showing your knowledge.
Applying Advanced LookML Concepts in Looker
Familiarize yourself with advanced LookML concepts in Looker. Use Liquid to customize and create dynamic dimensions and measures, create custom native and dynamic SQL derived tables, and use extensions to modularize your LookML code.
Data Catalog Fundamentals
Data Catalog is a fully managed and scalable metadata management service that enables organizations to quickly identify, interpret and manage all their data. Learn how to find data items and metadata with Data Catalog, and how to tag them. Learn how to create MySQL, PostgreSQL, and SQL Server connectors to Data Catalog.
Manage Data Models in Looker
Learn how to: maintain the health of the LookML project; use SQL runner for data validation; use LOOKML best practices; optimize queries and reports for performance; implement persistent derived tables and cache policies.