Course Overview
Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course!
This three-day instructor-led class teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, data visualization, and machine learning.
Who should attend
This class is intended for the following:
- 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
Prerequisites
To get the most out of this course, participants should have:
- Basic proficiency with ANSI SQL
Course Objectives
This course teaches participants the following skills:
- Derive insights from data using the analysis and visualization tools on Google Cloud Platform
- Load, clean, and transform data at scale with Google Cloud Dataprep
- Explore and Visualize data using Google Data Studio
- Troubleshoot, optimize, and write high performance queries
- Practice with pre-built ML APIs for image and text understanding
- Train classification and forecasting ML models using SQL with BQML
Course Content
- Module 1: Introduction to Data on the Google Cloud Platform
- Module 2: Big Data Tools Overview
- Module 3: Exploring your Data with SQL
- Module 4: Google BigQuery Pricing
- Module 5: Cleaning and Transforming your Data
- Module 6: Storing and Exporting Data
- Module 7: Ingesting New Datasets into Google BigQuery
- Module 8: Data Visualization
- Module 9: Joining and Merging Datasets
- Module 10: Advanced Functions and Clauses
- Module 11: Schema Design and Nested Data Structures
- Module 12: More Visualization with Google Data Studio
- Module 13: Optimizing for Performance
- Module 14: Data Access
- Module 15: Notebooks in the Cloud
- Module 16: How Google does Machine Learning
- Module 17: Applying Machine Learning to your Datasets (BQML)