BigQuery for Data Analysts

BigQuery for Data AnalystsBQDAGOGoogleGO-BQDA1.0<ul> <li>Learn the purpose and value of BigQuery, Google Cloud’s enterprise data warehouse, and discuss its data analytics features.</li><li>Analyze large datasets in BigQuery with SQL.</li><li>Clean and transform your data in BigQuery with SQL.</li><li>Ingest new BigQuery datasets, and discuss options for external data sources.</li><li>Review visualization principles, and use Connected Sheets and Looker Studio to visualize data insights from BigQuery.</li><li>Use Dataform to develop scalable data transformation pipelines in BigQuery.</li><li>Use new integrations and assistive capabilities introduced with BigQuery Studio.</li></ul><ul> <li><a class="fl-href-prod" href="/swisscom/en/course/google-idagc"><svg role="img" aria-hidden="true" focusable="false" data-nosnippet class="cms-linkmark"><use xlink:href="/css/img/icnset-linkmarks.svg#linkmark"></use></svg>Introduction to Data Analytics on Google Cloud (IDAGC)</a></li></ul><ul> <li>Data analysts who want to learn how to use BigQuery for their data analysis needs.</li></ul><h4>Module 0 - Course Introduction</h4> Topics: <ul> <li>This module introduces the course agenda.</li></ul> Objectives: <ul> <li>Introduce the topics covered in the course.</li></ul><h4>Module 1 - BigQuery for data analysts</h4> Topics: <ul> <li>Overview</li><li>Data analytics on Google Cloud</li><li>From data to insights with BigQuery</li><li>Real-world use cases of companies transformed through analytics on Google Cloud</li></ul> Objectives: <ul> <li>Identify analytics challenges faced by data analysts, and compare big data on-premises versus in the cloud.</li><li>Learn the purpose and value of BigQuery, Google Cloud’s enterprise data warehouse, and discuss its data analytics features.</li></ul><h4>Module 2 - Exploring and preparing your data with BigQuery</h4> Topics: <ul> <li>Overview</li><li>Common data exploration techniques</li><li>Analysis of large datasets with BigQuery</li><li>Query basics</li><li>Working with functions</li><li>Enriching your queries with UNIONs and JOINs</li></ul> Objectives: <ul> <li>List common data exploration techniques.</li><li>Review SQL query basics.</li><li>Enrich queries with functions, unions, and joins.</li></ul> Activities: <ul> <li>Lab: Exploring an Ecommerce Dataset using SQL in Google BigQuery</li><li>Lab: Troubleshooting Common SQL Errors with BigQuery</li><li>Lab: Troubleshooting and Solving Data Join Pitfalls</li></ul><h4>Module 3 - Cleaning and transforming your data</h4> Topics: <ul> <li>Overview</li><li>Five principles of dataset integrity</li><li>Clean and transform data using SQL</li><li>Clean and transform data: Other options</li></ul> Objectives: <ul> <li>Identify what makes a good dataset.</li><li>Clean and transform data using SQL.</li><li>Clean and transform data with other options.</li></ul><h4>Module 4 - Ingesting and storing new BigQuery datasets</h4> Topics: <ul> <li>Overview</li><li>Permanent versus temporary data tables</li><li>Ingesting new datasets</li><li>External data sources</li></ul> Objectives: <ul> <li>Review differences between permanent and temporary data tables.</li><li>Ingest and store new BigQuery datasets.</li><li>Discuss options for external data sources.</li></ul> Activities: <ul> <li>Lab: Creating New Permanent Tables</li><li>Lab: Ingesting and Querying New Datasets</li></ul><h4>Module 5 - Visualizing your insights from BigQuery</h4> Topics: <ul> <li>Overview</li><li>Data visualization principles</li><li>Connected Sheets</li><li>Common data visualization pitfalls</li><li>Looker Studio</li><li>Analysis in a notebook</li></ul> Objectives: <ul> <li>Review data visualization principles and common visualization pitfalls.</li><li>Use Connected Sheets and Looker Studio to visualize data insights from BigQuery.</li><li>Discuss running analyses in a Jupyter Notebook.</li></ul> Activities: <ul> <li>Lab: Connected Sheets Qwik Start</li><li>Lab: Explore and Create Reports with Looker Studio</li></ul><h4>Module 6 - Developing scalable data transformation pipelines in BigQuery with Dataform</h4> Topics: <ul> <li>Overview</li><li>What is Dataform?</li><li>Getting started with Dataform</li></ul> Objectives: <ul> <li>Use Dataform to develop scalable data transformation pipelines in BigQuery.</li><li>Learn how to get started with Dataform by creating a repository and development workspace.</li><li>Create and execute a SQL workflow in Dataform.</li></ul> Activities: <ul> <li>Demo</li><li>Lab: Create and Execute a SQL Workflow in Dataform</li></ul><h4>Module 7 - BigQuery Studio</h4> Topics: <ul> <li>BigQuery Studio: What and why?</li><li>Unified analytics</li><li>Asset management</li><li>Embedded assistance</li></ul> Objectives: <ul> <li>Introduce BigQuery Studio.</li><li>Use Duet AI in BigQuery to explain and generate SQL queries.</li><li>Learn about new usability features and integrations with Dataform and Dataplex in the new BigQuery Studio interface.</li></ul> Activities: <ul> <li>Demo</li><li>Lab: Analyze Data with Duet AI Assistance</li><li>Lab: Generate Personalized Email Content with BigQuery Continuous Queries and Gemini</li></ul><h4>Module 8 - Summary</h4> Topics: <ul> <li>Summary</li></ul> Objectives: <ul> <li>Summarize the key topics covered in the course.</li></ul>- Learn the purpose and value of BigQuery, Google Cloud’s enterprise data warehouse, and discuss its data analytics features. - Analyze large datasets in BigQuery with SQL. - Clean and transform your data in BigQuery with SQL. - Ingest new BigQuery datasets, and discuss options for external data sources. - Review visualization principles, and use Connected Sheets and Looker Studio to visualize data insights from BigQuery. - Use Dataform to develop scalable data transformation pipelines in BigQuery. - Use new integrations and assistive capabilities introduced with BigQuery Studio.- Introduction to Data Analytics on Google Cloud (IDAGC)- Data analysts who want to learn how to use BigQuery for their data analysis needs.Module 0 - Course Introduction Topics: - This module introduces the course agenda. Objectives: - Introduce the topics covered in the course. Module 1 - BigQuery for data analysts Topics: - Overview - Data analytics on Google Cloud - From data to insights with BigQuery - Real-world use cases of companies transformed through analytics on Google Cloud Objectives: - Identify analytics challenges faced by data analysts, and compare big data on-premises versus in the cloud. - Learn the purpose and value of BigQuery, Google Cloud’s enterprise data warehouse, and discuss its data analytics features. Module 2 - Exploring and preparing your data with BigQuery Topics: - Overview - Common data exploration techniques - Analysis of large datasets with BigQuery - Query basics - Working with functions - Enriching your queries with UNIONs and JOINs Objectives: - List common data exploration techniques. - Review SQL query basics. - Enrich queries with functions, unions, and joins. Activities: - Lab: Exploring an Ecommerce Dataset using SQL in Google BigQuery - Lab: Troubleshooting Common SQL Errors with BigQuery - Lab: Troubleshooting and Solving Data Join Pitfalls Module 3 - Cleaning and transforming your data Topics: - Overview - Five principles of dataset integrity - Clean and transform data using SQL - Clean and transform data: Other options Objectives: - Identify what makes a good dataset. - Clean and transform data using SQL. - Clean and transform data with other options. Module 4 - Ingesting and storing new BigQuery datasets Topics: - Overview - Permanent versus temporary data tables - Ingesting new datasets - External data sources Objectives: - Review differences between permanent and temporary data tables. - Ingest and store new BigQuery datasets. - Discuss options for external data sources. Activities: - Lab: Creating New Permanent Tables - Lab: Ingesting and Querying New Datasets Module 5 - Visualizing your insights from BigQuery Topics: - Overview - Data visualization principles - Connected Sheets - Common data visualization pitfalls - Looker Studio - Analysis in a notebook Objectives: - Review data visualization principles and common visualization pitfalls. - Use Connected Sheets and Looker Studio to visualize data insights from BigQuery. - Discuss running analyses in a Jupyter Notebook. Activities: - Lab: Connected Sheets Qwik Start - Lab: Explore and Create Reports with Looker Studio Module 6 - Developing scalable data transformation pipelines in BigQuery with Dataform Topics: - Overview - What is Dataform? - Getting started with Dataform Objectives: - Use Dataform to develop scalable data transformation pipelines in BigQuery. - Learn how to get started with Dataform by creating a repository and development workspace. - Create and execute a SQL workflow in Dataform. Activities: - Demo - Lab: Create and Execute a SQL Workflow in Dataform Module 7 - BigQuery Studio Topics: - BigQuery Studio: What and why? - Unified analytics - Asset management - Embedded assistance Objectives: - Introduce BigQuery Studio. - Use Duet AI in BigQuery to explain and generate SQL queries. - Learn about new usability features and integrations with Dataform and Dataplex in the new BigQuery Studio interface. Activities: - Demo - Lab: Analyze Data with Duet AI Assistance - Lab: Generate Personalized Email Content with BigQuery Continuous Queries and Gemini Module 8 - Summary Topics: - Summary Objectives: - Summarize the key topics covered in the course.2 days1495.002065.001300.001300.001320.001300.001300.001300.001300.00