From Data to Insights with Google Cloud Platform

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

The course includes presentations, demonstrations, and hands-on labs.

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

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