GCP-BQDA: BigQuery for Data Analysts

Module 0: Course Introduction
Topic:

• 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.

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.

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

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.

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.

Module 8: Summary
Topics:

• Summary

Objectives:

• Summarize the key topics covered in the course.