From Data to Insights with Google Cloud

Module 1:  Introduction to Data on Google Cloud

• Compare data infrastructure on-premises versus on Google Cloud

Module 2: 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 

Module 3: 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

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

• Compare data visualizations and make recommendations for improvement
• Create dashboards and visualizations with Looker Studio 

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 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 

Module 8: 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

Module 9: Designing Schemas that Scale: Arrays and Structs in BigQuery

• Differentiate between BigQuery and traditional data architecture
• Work with ARRAYs and STRUCTs as part of nested fields in data schemas 

Module 10: 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

Module 11: Controlling Access with Data Security Best Practices

• Review data access roles within Google Cloud and BigQuery
• Highlight key data access pitfalls and how to avoid them

Module 12: 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 

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