Module 1: Introduction to Data on the Google Cloud Platform

• 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

• 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

• 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

• 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: Data Access

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

Module 15: Notebooks in the Cloud

• Cloud Datalab
• Compute Engine and Cloud Storage
• Lab: Rent-a-VM to process earthquakes data
• Data Analysis with BigQuery

Module 16: How Google does Machine Learning

• Introduction to Machine Learning for analysts
• Practice with Pretrained ML APIs for image and text understanding
• Lab: Pretrained ML APIs

Module 17: Applying Machine Learning to your Datasets (BQML)

• Building Machine Learning datasets and analyzing features
• Creating classification and forecasting models with BQML
• Lab: Predict Visitor Purchases with a Classification Model in BQML
• Lab: Predict Taxi Fare with a BigQuery ML Forecasting Model

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

• Basic proficiency with ANSI SQL

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

• 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)