Module 1: Introduction to Data on the Google Cloud Platform

Before and Now: Scalable Data Analysis in the Cloud

Topics Covered

• • Highlight Analytics Challenges Faced by Data Analysts
• • Compare Big Data On-Premise 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

Sharpen the Tools in your Data Analyst toolkit

Topics Covered

• • 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

Get Familiar with Google BigQuery and Learn SQL Best Practices

Topics Covered

• • Compare Common Data Exploration Techniques
• • Learn How to Code High Quality Standard SQL
• • Explore Google BigQuery Public Datasets
• • Visualization Preview: Google Data Studio
• • Lab: Troubleshoot Common SQL Errors

Module 4: Google BigQuery Pricing

Calculate Google BigQuery Storage and Query Costs

Topics Covered

• • 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

Wrangle your Raw Data into a Cleaner and Richer Dataset

Topics Covered

• • 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

Create new Tables and Exporting Results

Topics Covered

• • 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

Bring your Data into the Cloud

Topics Covered

• • 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

Effectively Explore and Explain your Data through Visualization

Topics Covered

• • 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

Combine and Enrich your Datasets with more Data

Topics Covered

• • 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: Google BigQuery Table Deep Dive

What sets Cloud Architecture apart?

Topics Covered

• • Compare Data Warehouse Storage Methods
• • Deep-dive into Column-Oriented Storage
• • Examine Logical Views, Date-Partitioned Tables, and Best Practices
• • Query the Past with Time Travelling Snapshots

Module 11: Schema Design and Nested Data Structures

Model your Datasets for Scale in Google BigQuery

Topics Covered

• • 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: Advanced Visualization with Google Data Studio

Create Pixel-Perfect Dashboards

Topics Covered

• • Create Case Statements and Calculated Fields
• • Avoid Performance Pitfalls with Cache considerations
• • Share Dashboards and Discuss Data Access considerations
• • Lab: Visualizing Insights with Google Data Studio

Module 13: Advanced Functions and Clauses

Dive Deeper into Advanced Query Writing with Google BigQuery

Topics Covered

• • 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 14: Optimizing for Performance

Troubleshoot and Solve Query Performance Problems

Topics Covered

• • 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 15: Advanced Insights

Think, Analyze, and Share Insights like a Data Scientist

Topics Covered

• • Distill Complex Queries
• • Brainstorm Data-Driven Hypotheses
• • Think like a Data Scientist
• • Introducing Cloud Datalab
• • Lab: Reading a Google Cloud Datalab notebook

Module 16: Data Access

Keep Data Security top-of-mind in the Cloud

Topics Covered

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