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From Data to Insights with Google Cloud Platform
GOOGLE CLOUD, IOT, Kubernetes, Security, bigdata, virtualmachines, deeplearning, bigquery, cloudarchitect, developers, virtualcontainers, app, iaas, paas, saas, serverless, dataengineer, monitoring, devops, googlecloudplatform, datascientist, dataanalyst
NewCondition € 1600.00
Pre-Order
Pre-Order
DURATION
3 days
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, 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)
TOPICS
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
3 days
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, 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)
TOPICS
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
