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From Data to Insights with Google Cloud

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 course teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.

Audience

This class is intended for the following participants:
  • Data Analysts,
  • Business Analysts,
  • Business Intelligence professionals,
  • Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on GCP.

Pre-requisites

To get the most out of this course, participants should have:

Duration

3 days

Investment

Check the next open public class in our enrollment page.
If you are interested in a private training class for your company, contact us.
From Data to Insights with Google Cloud Platform dependencies with other courses and certifications
From Data to Insights with Google Cloud Platform dependencies with other courses and certifications

Course Outline

The course includes presentations, demonstrations, and hands-on labs.
  • 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