Module 1: Introduction to Data on Google Cloud

• Compare on-premises data infrastructure with Google Cloud.

Module 2: Analyzing Large Datasets with BigQuery

• Identify tasks and challenges faced by data analysts.
• Explore BigQuery’s core features.
• Understand the roles and tools of data analysts, scientists, and engineers.
• Use the BigQuery web UI to explore public datasets with SQL.

Module 3: Exploring Public Datasets with SQL

• Learn common data exploration techniques.
• Understand SQL SELECT statements, functions, and calculated fields.
• Troubleshoot dataset quality issues using BigQuery’s web UI.

Module 4: Cleaning and Transforming Data with Dataprep

• Identify dataset shapes and skew patterns.
• Clean and transform data using SQL and Dataprep.

Module 5: Visualizing Insights and Creating Scheduled Queries

• Compare different data visualization methods.
• Build dashboards and visualizations using Looker Studio.

Module 6: Storing and Ingesting New Datasets

• Differentiate between permanent and temporary data tables.
• Identify data types and formats supported by BigQuery.
• Load and store new datasets in BigQuery.

Module 7: Enriching Data Warehouses with JOINs

• Understand when to use JOINs and UNIONs.
• Identify common pitfalls in merging datasets.
• Write SQL JOINs and UNIONs for enriched insights.

Module 8: Advanced Features and Partitioning for Enhanced Insights

• Use statistical approximation and user-defined functions.
• Apply RANK(), PARTITION, and Common Table Expressions (WITH) for complex queries.

Module 9: Designing Scalable Schemas with Arrays and Structs

• Compare BigQuery’s schema design to traditional architectures.
• Work with ARRAYs and STRUCTs for nested data fields.

Module 10: Optimizing Queries for Performance

• Identify performance bottlenecks in BigQuery queries.
• Interpret the Query Explanation Map and troubleshoot query issues.

Module 11: Controlling Access with Data Security Best Practices

• Understand Google Cloud and BigQuery data access roles.
• Avoid common data access pitfalls with best practices.

Module 12: Predicting Customer Behavior with BigQuery ML

• Learn how structured ML data drives business value.
• Build an ML model to predict customer lifetime value (LTV).

Module 13: Insights from Unstructured Data Using Machine Learning

• Explore how ML creates business value from unstructured data.
• Understand the differences between pre-built, custom, and new models in AI strategies.

This course provides a comprehensive pathway to mastering data analysis and machine learning with Google Cloud, helping you drive business insights and optimize decision-making.