From Data to Insights with Google Cloud Platform
Contact us to book this course
Learning Track
Data Engineering and Analytics
Delivery methods
On-Site, Virtual
Duration
3 days
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, and data visualization.
Objectives
This course teaches participants the following skills:
- Derive insights from data using the analysis and visualization tools on Google Cloud Platform
- Interactively query datasets using Google BigQuery
- 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
Audience
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 (reference)
Course outline
- Highlight Analytics Challenges Faced by Data Analysts
- Compare Big Data On-Premises vs on the Cloud
- Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
- Navigate Google Cloud Platform Project Basics
- Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
- Demo: Analyze 10 Billion Records with Google BigQuery
- Explore 9 Fundamental Google BigQuery Features
- Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
- Lab: BigQuery Basics
- Compare Common Data Exploration Techniques
- Learn How to Code High Quality Standard SQL
- Explore Google BigQuery Public Datasets
- Visualization Preview: Google Data Studio
- Lab: Explore your Ecommerce Dataset with SQL in Google BigQuery
- Examine the 5 Principles of Dataset Integrity
- Characterize Dataset Shape and Skew
- Clean and Transform Data using SQL
- Clean and Transform Data using a new UI: Introducing Cloud Dataprep
- Lab: Creating a Data Transformation Pipeline with Cloud Dataprep
- Overview of Data Visualization Principles
- Exploratory vs Explanatory Analysis Approaches
- Demo: Google Data Studio UI
- Connect Google Data Studio to Google BigQuery
- Lab: How to Build a BI Dashboard Using Google Data Studio and BigQuery
- Compare Permanent vs Temporary Tables
- Save and Export Query Results
- Performance Preview: Query Cache
- Lab: Ingesting New Datasets into BigQuery
- Merge Historical Data Tables with UNION
- Introduce Table Wildcards for Easy Merges
- Review Data Schemas: Linking Data Across Multiple Tables
- Walkthrough JOIN Examples and Pitfalls
- Lab: Troubleshooting and Solving Data Join Pitfalls
- Review SQL Case Statements
- Introduce Analytical Window Functions
- Safeguard Data with One-Way Field Encryption
- Discuss Effective Sub-query and CTE design
- Compare SQL and Javascript UDFs
- Lab: Creating Date-Partitioned Tables in BigQuery
- Compare Google BigQuery vs Traditional RDBMS Data Architecture
- Normalization vs Denormalization: Performance Tradeoffs
- Schema Review: The Good, The Bad, and The Ugly
- Arrays and Nested Data in Google BigQuery
- Lab: Querying Nested and Repeated Data
- Lab: Schema Design for Performance: Arrays and Structs in BigQuery
- Walkthrough of a BigQuery Job
- Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
- Optimize Queries for Cost
- Data Security Best Practices
- Controlling Access with Authorized Views
- Intro to ML
- Feature Selection
- Model Types
- Machine Learning in BigQuery
- Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML
- Structured vs Unstructured ML
- Prebuilt ML models
- Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
- Lab: Training with Pre-built ML Models using Cloud Vision API and AutoML
- Summary and course wrap-up
