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Course Overview
Modern organizations are collecting more data than ever—but insight is only valuable when it’s actionable. Only 32% of available data is effectively used for business decision-making, leaving a significant opportunity for business analysts, data analysts, and cloud data engineers to lead with insight.
The From Data to Insights with Google Cloud Platform course teaches professionals how to derive insights from large-scale datasets using Google Cloud’s powerful tools. Through lectures, demos, and hands-on labs, you’ll work with BigQuery, Data Studio, Dataprep, and BigQuery ML to build complete, scalable data solutions on Google Cloud. Learn how to ingest new datasets, design efficient schemas, optimize queries, and apply machine learning—all while supporting data analysis and visualization using scalable cloud infrastructure.
Who Should Attend
Data Analysts, Business Analysts, Business Intelligence professionals
Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform
Course Objectives
This course is designed to help data analysts build scalable data solutions and for engineers who will be partnering with data analysts to support data analysis pipelines and projects.
Through a combination of theory and practice, you’ll learn how to build scalable data solutions that empower stakeholders with meaningful, timely data insights. You will learn how to:
- Perform data analysis and visualization using BigQuery and Data Studio
- Clean and transform datasets using SQL and Google Cloud Dataprep
- Optimize query performance and understand schema design
- Train and deploy machine learning models using BigQuery ML
- Apply security, access control, and best practices in analytics pipelines
Course Outline
Introduction to Data on Google Cloud Platform
- Analytics challenges in the enterprise
- Comparing cloud vs. on-premises big data solutions
- How companies use insights with Google Cloud Platform
- Setting up your Google Cloud project
Analyzing Large Datasets with BigQuery
- Key features of BigQuery
- Tools for analysts, scientists, and engineers
- Where BigQuery fits in the analytics workflow
Exploring Your Data with SQL
- Common data exploration strategies
- Writing queries against public and private datasets
Cleaning and Transforming Your Data with Dataprep
- Dataset integrity and structure
- Using Google Cloud Dataprep for transformation
- Applying SQL to shape and clean datasets
Visualizing Insights and Scheduling Queries
- Principles of data visualization
- Avoiding common design pitfalls
- Creating dashboards with Google Data Studio
- Scheduling recurring queries
Storing and Ingesting New Datasets
- Temporary vs. permanent tables
- How to ingest new datasets efficiently
Enriching Your Data Warehouse with JOINs
- Using JOIN and UNION to merge historical datasets
- Table wildcards and linking across schemas
- JOIN examples and potential pitfalls
Advanced Features and Partitioning
- Statistical and user-defined functions
- Partitioned and clustered tables for performance
- Advanced query structuring
Designing Schemas that Scale
- BigQuery’s nested schema model
- ARRAY and STRUCT syntax
- Comparing relational and cloud-native architecture
Optimizing Queries for Performance
- Avoiding BigQuery performance pitfalls
- Using query plans to troubleshoot
- Preventing data hotspots in large queries
Controlling Access with Data Security
- Column hashing and authorized views
- IAM roles and BigQuery access management
- Security pitfalls in analytics environments
Predicting Return Purchases with BigQuery ML
- Using BigQuery ML for structured prediction
- Use case: customer lifetime value
- Creating models with SQL
Deriving Insights from Unstructured Data
- Business value from ML on text and images
- Using pre-built ML APIs
- Enhancing models with AutoML or custom training