BigQuery for Data Analysts (BQDA) – Outline

Detailed Course Outline

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.