From Data to Insights with Google Cloud
Description
Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s
serverless, highly scalable, and cost-effective cloud data warehouse. This course
uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery,
including how to create a data transformation pipeline, build a BI dashboard, ingest
new datasets, and design schemas at scale.
Intended audience
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
Topics
Introduction to Data on Google Cloud
• Analytics Challenges Faced by Data Analysts
• Big Data On-premise Versus on the Cloud
• Real-world Use Cases of Companies Transformed Through Analytics on the Cloud
• Google Cloud Project Basics
Analyzing Large Datasets with BigQuery
• Data Analyst Tasks, Challenges, and Google Cloud Data Tools
• Fundamental BigQuery Features
• Google Cloud Tools for Analysts, Data Scientists, and Data Engineers
Exploring your Public Dataset with SQL
• Common Data Exploration Techniques
• Use SQL to Query Public Datasets
Cleaning and Transforming your Data with Dataprep
• 5 Principles of Dataset Integrity
• Dataset Shape and Skew
• Clean and Transform Data using SQL
• Introducing Dataprep by Trifacta
Visualizing Insights and Creating Scheduled Queries
• Data Visualization Principles
• Common Data Visualization Pitfalls
• Google Data Studio
Storing and Ingesting New Datasets
• Permanent Versus Temporary Data Tables
• Ingesting New Datasets
Enriching your Data Warehouse with JOINs
• Merge Historical Data Tables with UNION
• Introduce Table Wildcards for Easy Merges
• Review Data Schemas: Linking Data Across Multiple Tables
• JOIN Examples and Pitfalls
Advanced Features and Partitioning your Queries and Tables for Advanced Insights
• Advanced Functions (Statistical, Analytic, User-defined)
• Date-Partitioned Tables
Designing Schemas that Scale: Arrays and Structs in BigQuery
• BigQuery Versus Traditional Relational Data Architecture
• ARRAY and STRUCT Syntax
• BigQuery Architecture
Optimizing Queries for Performance
• BigQuery Performance Pitfalls
• Prevent Data Hotspots
• Diagnose Performance Issues with the Query Explanation Map
Controlling Access with Data Security
• Hashing Columns
• Authorized Views
• IAM and BigQuery Dataset Roles
• Access Pitfalls
Predicting Visitor Return Purchases with BigQuery ML
• Machine Learning on Structured Data
• Scenario: Predicting Customer Lifetime Value
• Choosing the Right Model Type
• Creating ML models with SQL
Deriving Insights From Unstructured Data Using Machine Learning
• ML Drives Business Value
• How does ML on unstructured data work?
• Choosing the Right ML Approach
• Pre-built AI Building Blocks
• Customizing Pre-built Models with AutoML
• Building a Custom Model
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