Course Description
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, data visualization, and machine learning.
Content
The course includes presentations, demonstrations, and hands-on labs.
Module 1: Introduction to Data on the Google Cloud Platform Before and Now: Scalable Data Analysis in the Cloud
- 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
- 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
Module 3: Exploring your Data Get Familiar with Google BigQuery and Learn SQL Best Practices
- Compare Common Data Exploration Techniques
- Learn How to Code High Quality Standard SQL
- Explore Google BigQuery Public Datasets
- Visualization Preview: Google Data Studio
- Lab 3: Troubleshoot Common SQL Errors
Module 4: Google BigQuery Pricing Calculate Google BigQuery Storage and Query Costs
- Walkthrough of a BigQuery Job
- Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
- Optimize Queries for Cost
- Lab 4: Calculate Google BigQuery Pricing
Module 5: Cleaning and Transforming your Data Wrangle your Raw Data into a Cleaner and Richer Dataset
- 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 5: Explore and Shape Data with Cloud Dataprep
Module 6: Storing and Exporting Data Create new Tables and Exporting Results
- Compare Permanent vs. Temporary Tables
- Save and Export Query Results
- Performance Preview: Query Cache
- Lab 6: Creating New Permanent Tables
Module 7: Ingesting New Datasets into Google BigQuery Bring your Data into the Cloud
- Query from External Data Sources
- Avoid Data Ingesting Pitfalls
- Ingest New Data into Permanent Tables
- Discuss Streaming Inserts
- Lab 7: Ingesting and Querying New Datasets
Module 8: Data Visualization Effectively Explore and Explain Data through Visualization
- Overview of Data Visualization Principles
- Exploratory vs. Explanatory Analysis Approaches
- Demo: Google Data Studio UI
- Connect Google Data Studio to Google BigQuery
- Lab 8: Exploring a Dataset in Google Data Studio
- Lab 8: Exploring a Dataset in Google Data Studio
Module 9: Joining and Merging Datasets Combine and Enrich Datasets with More Data
- 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 9: Join and Union Data from Multiple Tables
Module 10: Advanced Functions and Clauses Dive Deeper into Advanced Query Writing with Google BigQuery
- 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 10: Deriving Insights with Advanced SQL Functions
Module 11: Schema Design and Nested Data Structures Model Datasets for Scale in Google BigQuery
- Compare Google BigQuery vs. Traditional RDBMS Data Architecture
- Normalization vs. Denormalization: Performance Trade-Offs
- Schema Review: The Good, The Bad, and The Ugly
- Arrays and Nested Data in Google BigQuery
- Lab 11: Querying Nested and Repeated Data
Module 12: More Visualization with Google Data Studio Create Pixel-Perfect Dashboards
- Create Case Statements and Calculated Fields
- Avoid Performance Pitfalls with Cache Considerations
- Share Dashboards and Discuss Data Access Considerations
Module 13: Optimizing for Performance Troubleshoot and Solve Query Performance Problems
- Avoid Google BigQuery Performance Pitfalls
- Prevent Hotspots in Data
- Diagnose Performance Issues with the Query Explanation Map
- Lab 13: Optimizing and Troubleshooting Query Performance
Module 14: Data Access Keep Data Security Top-of-Mind 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 Leverage pre-built ML APIs for your projects
- 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
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