Date d'abonnement : 2023
Recommendations with AI Applications
Earned nov. 12, 2025 EST
BigQuery Fundamentals for Snowflake Professionals
Earned mars 1, 2024 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned fév. 28, 2024 EST
Preparing for Your Associate Cloud Engineer Journey
Earned fév. 28, 2024 EST
Build a Data Warehouse with BigQuery
Earned fév. 17, 2024 EST
Do you want to keep your users engaged by suggesting content they'll love? This course equips you with the skills to build a cutting-edge recommendations app using your own data with no prior machine learning knowledge. You learn to leverage AI Applications to build recommendation applications so that audiences can discover more personalized content, like what to watch or read next, with Google-quality results customized using optimization objectives.
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Snowflake and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Snowflake, you also learn about similarities and differences between Snowflake and BigQuery to help you get started with data warehouses in BigQuery.
Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.