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 three-day instructor-led class teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.
Objectives
This course teaches participants the following skills:
- Derive insights from data using the analysis and visualization tools on Google Cloud Platform
- Interactively query datasets using Google BigQuery
- 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
Audience
This class is intended for the following:
- 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
Prerequisites
To get the most out of this course, participants should have:
- Basic proficiency with ANSI SQL (reference)
Course Outline
Module 1: Introduction to 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
Module 2: Analyzing Large Datasets with BigQuery
- 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
Module 3: Exploring your Public Dataset 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: Explore your Ecommerce Dataset with SQL in Google BigQuery
Module 4: Cleaning and Transforming your Data with Cloud Dataprep
- 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
Module 5: Visualizing Insights and Creating Scheduled Queries
- 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
Module 6: Storing and Ingesting new Datasets
- Compare Permanent vs Temporary Tables
- Save and Export Query Results
- Performance Preview: Query Cache
- Lab: Ingesting New Datasets into BigQuery
Module 7: 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
- Walkthrough JOIN Examples and Pitfalls
- Lab: Troubleshooting and Solving Data Join Pitfalls
Module 8: Partitioning your Queries and Tables for Advanced Insights
- 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
Module 9: Designing Schemas that Scale: Arrays and Structs 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
Module 10: Optimizing Queries for Performance
- Walkthrough of a BigQuery Job
- Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
- Optimize Queries for Cost
Module 11: Controlling Access with Data Security Best Practices
- Data Security Best Practices
- Controlling Access with Authorized Views
Module 12: Predicting Visitor Return Purchases with BigQuery ML
- Intro to ML
- Feature Selection
- Model Types
- Machine Learning in BigQuery
- Lab: Predict Visitor Purchases with a Classification Model with BigQuery ML
Module 13: Deriving Insights from Unstructured Data using Machine Learning
- 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
Module 14: Completion
- Summary and course wrap-up