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.

✓ Formation officielle Google CloudNiveau Intermediate⏱️ 2 jours (14h)

Ce que vous allez apprendre

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.

Prérequis

Introduction to Data Analytics on Google Cloud

Public cible

Data analysts who want to learn how to use BigQuery for their data analysis needs.

Programme de la Formation

9 modules pour maîtriser les fondamentaux

Objectifs

Introduce the topics covered in the course.

Sujets abordés

→This module introduces the course agenda.

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.