GCP200BQ4A
BigQuery for Data Analysts
This course is designed for data analysts who want to learn about using BigQuery for their data analysis needs. Through a combination of videos, labs, and demos, we cover various topics that discuss how to ingest, transform, and query your data in BigQuery to derive insights that can help in business decision-making.
What you will learn
- 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.
Prerequisites
- Introduction to Data Analytics on Google Cloud
Target audience
- Data analysts who want to learn how to use BigQuery for their data analysis needs.
Training Program
9 modules to master the fundamentals
Objectives
- Introduce the topics covered in the course.
Topics covered
- →This module introduces the course agenda.
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.
Topics covered
- →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
- List common data exploration techniques.
- Review SQL query basics.
- Enrich queries with functions, unions, and joins.
Topics covered
- →Overview
- →Common data exploration techniques
- →Analysis of large datasets with BigQuery
- →Query basics
- →Working with functions
- →Enriching your queries with 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
Objectives
- Identify what makes a good dataset.
- Clean and transform data using SQL.
- Clean and transform data with other options.
Topics covered
- →Overview
- →Five principles of dataset integrity
- →Clean and transform data using SQL
- →Clean and transform data: Other options
Objectives
- Review differences between permanent and temporary data tables.
- Ingest and store new BigQuery datasets.
- Discuss options for external data sources.
Topics covered
- →Overview
- →Permanent versus temporary data tables
- →Ingesting new datasets
- →External data sources
Activities
Lab: Creating New Permanent Tables
Lab: Ingesting and Querying New Datasets
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.
Topics covered
- →Overview
- →Data visualization principles
- →Connected Sheets
- →Common data visualization pitfalls
- →Looker Studio
- →Analysis in a notebook
Activities
Lab: Connected Sheets Qwik Start
Lab: Explore and Create Reports with Looker Studio
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.
Topics covered
- →Overview
- →What is Dataform?
- →Getting started with Dataform
Activities
Demo
Lab: Create and Execute a SQL Workflow in Dataform
Objectives
- Introduce BigQuery Studio.
- Use Duet Al 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.
Topics covered
- →BigQuery Studio: What and why?
- →Unified analytics
- →Asset management
- →Embedded assistance
Activities
Demo
Lab: Analyze Data with Duet Al Assistance
Lab: Generate Personalized Email Content with BigQuery Continuous Queries and Gemini
Objectives
- Summarize the key topics covered in the course.
Topics covered
- →Summary
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
January 19, 2026
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âś“ Guaranteed session
March 16, 2026
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May 11, 2026
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July 8, 2026
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September 14, 2026
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November 16, 2026
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February 20, 2026
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April 16, 2026
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June 4, 2026
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August 20, 2026
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October 20, 2026
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December 17, 2026
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February 25, 2026
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April 16, 2026
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June 4, 2026
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August 20, 2026
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October 20, 2026
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December 17, 2026
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Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote