From Data to Insights with Google Cloud Platform Training in Republic of the Philippines

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

• Basic proficiency with ANSI SQL

• 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: Intro 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

1. Compare Permanent vs Temporary Tables
2. Save and Export Query Results
3. Performance Preview: Query Cache
4. 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