From Data to Insights with Google Cloud Platform (DIGCP)

Module 1: Introduction to Data on the Google Cloud Platform

• 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
• Lab: Getting started with Google Cloud Platform

Module 2: Big Data Tools Overview

• 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: Exploring Datasets with Google BigQuery

Module 3: Exploring your Data with SQL

• Walkthrough of a BigQuery Job
• Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
• Optimize Queries for Cost
• Lab: Calculate Google BigQuery Pricing

Module 4: Google BigQuery Pricing

• Walkthrough of a BigQuery Job
• Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
• Optimize Queries for Cost
• Lab: Calculate Google BigQuery Pricing

Module 5: Cleaning and Transforming your Data

• 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: Explore and Shape Data with Cloud Dataprep

Module 6: Storing and Exporting Data

• Compare Permanent vs Temporary Tables
• Save and Export Query Results
• Performance Preview: Query Cache
• Lab: Creating new Permanent Tables

Module 7: Ingesting New Datasets into Google BigQuery

• Query from External Data Sources
• Avoid Data Ingesting Pitfalls
• Ingest New Data into Permanent Tables
• Discuss Streaming Inserts
• Lab: Ingesting and Querying New Datasets

Module 8: Data Visualization

• Overview of Data Visualization Principles
• Exploratory vs Explanatory Analysis Approaches
• Demo: Google Data Studio UI
• Connect Google Data Studio to Google BigQuery
• Lab: Exploring a Dataset in Google Data Studio

Module 9: Joining and Merging Datasets

• 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: Join and Union Data from Multiple Tables

Module 10: Advanced Functions and Clauses

• 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: Deriving Insights with Advanced SQL Functions

Module 11: Schema Design and Nested Data Structures

• 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

Module 12: More Visualization with Google Data Studio

• Create Case Statements and Calculated Fields
• Avoid Performance Pitfalls with Cache considerations
• Share Dashboards and Discuss Data Access considerations

Module 13: Optimizing for Performance

• Avoid Google BigQuery Performance Pitfalls
• Prevent Hotspots in your Data
• Diagnose Performance Issues with the Query Explanation map
• Lab: Optimizing and Troubleshooting Query Performance

Module 14: Advanced Insights

• Introducing Cloud Datalab
• Cloud Datalab Notebooks and Cells
• Benefits of Cloud Datalab

Module 15: Data Access

• Compare IAM and BigQuery Dataset Roles
• Avoid Access Pitfalls
• Review Members, Roles, Organizations, Account Administration, and Service Accounts