Introduction to Data on Google Cloud
Analyzing Large Datasets with BigQuery
Identify data analyst tasks, and challenges, and introduce Google Cloud data tools
Explore 9 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
Exploring your Public Dataset with SQL
Compare common data exploration techniques
Identify the key components of a basic SQL SELECT statement and common pitfalls
Discuss the basics of SQL functions and how they create calculated fields with input parameters
Explore BigQuery public datasets
Troubleshoot dataset quality issues by analyzing duplicate records with SQL in the BigQuery Web UI
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
Visualizing Insights and Creating Scheduled Queries
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
Enriching your Data Warehouse with JOINs
Explain when to use UNIONs and when to use JOINs
Identify the key pitfalls 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
Advanced Features and Partitioning your Queries and Tables for Advanced Insights
Identify the available statistical approximation functions and user-defined 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
Designing Schemas that Scale: Arrays and Structs in BigQuery
Optimizing Queries for Performance
Identify BigQuery performance pitfalls
Discuss the Query Explanation map and how to interpret MAX and AVG processing times per stage
Describe how to analyze and troubleshoot broken queries
Controlling Access with Data Security Best Practices
Predicting Visitor Return Purchases with BigQuery ML
Explain how ML on structured data drives value
Describe how customer LTV can be predicted with an ML model
Choose the right model type for different structured data use cases
Create ML models with SQL
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
