Module 1: Introduction to Data on Google Cloud
Module 2: Analyzing Large Datasets with BigQuery
Identify data analyst tasks and challenges, and introduce Google Cloud data tools.
Explore nine fundamental BigQuery features.
Compare the differences in roles and toolsets between data analysts, data scientists, and data engineers.
Access the BigQuery web UI and explore a public dataset with basic SQL.
Module 3: Exploring your Public Dataset with SQL
Module 4: Cleaning and Transforming your Data with Dataprep
Characterize different dataset shapes and potential skew.
Clean and transform data using SQL.
Clean and transform data using Dataprep
Module 5: Visualizing Insights and Creating Scheduled Queries
Module 6: Storing and Ingesting New Datasets
Differentiate between permanent and temporary data tables.
Identify what types and formats of data BigQuery can ingest.
Differentiate between native BigQuery table storage and external data source connections.
Load new data into BigQuery.
Module 7: Enriching your Data Warehouse with JOINs
Explain when to use UNIONs and when to use JOINs.
Identify the key pitalls when joining and merging datasets.
Differentiate between join types visually.
Explain how union wildcards work and when to use them.
Write SQL JOINs and UNIONs against a dataset in the BigQuery web UI.
Module 8: Advanced Features and Paritioning your Queries and Tables for Advanced Insights
Identify the available statistical approximation functions and userdefned functions.
Apply large-scale record estimation with approximate aggregation functions.
Deconstruct an analytical window query and explain when to use RANK() and PARTITION.
Explain when to use Common Table Expressions (WITH) to break apart complex queries.
Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery
Module 10: Optimizing Queries for Perormance
Identify BigQuery perormance pitalls.
Discuss the Query Explanation map and how to interpret MAX and AVG processing times per stage.
Describe how to analyze and troubleshoot broken queries.
Module 11: Controlling Access with Data Security Best Practices
Module 12: Predicting Visitor Return Purchases with BigQuery ML
Module 13: Deriving Insights From Unstructured Data Using Machine Learning
Discuss how ML is able to drive business value.
Explain how ML on unstructured data works.
Differentiate between pre-built ML models, custom models, and new models when considering an AI application strategy