
Amazon QuickSight Course
Master Amazon QuickSight from the ground up and turn raw AWS data into actionable business intelligence. This course covers everything from connecting data sources and building visualizations to implementing security, ML insights, and embedded analytics. Whether you are a data analyst, BI developer, or cloud professional, you will gain the practical skills to deploy production-ready QuickSight solutions.
What you will learn:
You will learn how to connect QuickSight to AWS services like S3, Athena, and Redshift, as well as external databases. You will build and transform datasets using joins, calculated fields, and filters, then create a full range of visualizations and interactive dashboards. The course covers row-level security, column-level access control, and audit logging to meet enterprise data governance requirements. You will also apply QuickSight's built-in ML features, including forecasting, anomaly detection, and natural language queries. Finally, you will explore embedding dashboards into web applications and automating workflows through the QuickSight API.
How you study in practice Amazon QuickSight Course
How you practise Amazon QuickSight Course
For companies looking to train their teams
With Elevify for businesses, the course includes exercises and examples tailored to your company and its specific needs.
Course content
8 Chapters • 40 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsIntroduction to Amazon QuickSight
Introduction to Amazon QuickSight
Lesson 1 • QuickSight Editions and Pricing
Covers Standard vs. Enterprise editions, capacity pricing, and per-session models. Helps students select the right tier for organizational needs.
Lesson 2 • What Is Amazon QuickSight
Defines QuickSight as a cloud-native BI service and contrasts it with traditional BI tools. Sets context for the entire course by anchoring the platform's value proposition.
Lesson 3 • Navigating the QuickSight Console
Walks through the QuickSight interface, menus, and workspace layout. Builds the navigation fluency needed for all subsequent hands-on exercises.
Lesson 4 • Core Concepts and Terminology
Defines datasets, analyses, visuals, dashboards, and SPICE. Provides the shared vocabulary used throughout every chapter.
Lesson 5 • Setting Up Your QuickSight Account
Guides account creation, IAM permissions, and initial configuration steps. Ensures every student has a working environment before progressing.
Chapter 2HideHide detailsSee detailsConnecting Data Sources
Connecting Data Sources
Lesson 1 • Supported Data Source Types
Surveys all supported connectors including databases, files, and SaaS sources. Frames the data ingestion landscape before hands-on connection work begins.
Lesson 2 • Connecting to External Databases
Covers JDBC-based connections to MySQL, PostgreSQL, SQL Server, and others. Expands connectivity skills beyond the AWS ecosystem.
Lesson 3 • Connecting to AWS Data Services
Demonstrates connections to S3, Athena, Redshift, and RDS. Reinforces AWS integration patterns central to enterprise QuickSight deployments.
Lesson 4 • Managing Data Source Credentials
Teaches secure credential storage, rotation, and shared data source governance. Connects data access security to organizational compliance requirements.
Lesson 5 • Uploading and Managing File Sources
Explains direct file uploads, S3 manifest files, and refresh scheduling for file-based data. Addresses common file ingestion errors and best practices.
Chapter 3HideHide detailsSee detailsBuilding and Transforming Datasets
Building and Transforming Datasets
Lesson 1 • Filtering and Row-Level Preparation
Explains dataset-level filters that restrict rows before analysis begins. Reduces data volume and enforces business rules at the source layer.
Lesson 2 • Dataset Editor Fundamentals
Introduces the dataset editor interface, field types, and preview pane. Establishes the workspace where all data preparation tasks are performed.
Lesson 3 • SPICE Import and Refresh Strategies
Details SPICE ingestion, capacity management, and incremental refresh configuration. Optimises query performance and controls storage costs.
Lesson 4 • Joining and Blending Data
Covers inner, left, right, and full joins across tables and data sources. Enables multi-table analysis essential for real-world reporting scenarios.
Lesson 5 • Calculated Fields and Expressions
Teaches creation of calculated fields using QuickSight's expression language. Extends raw data with business metrics directly inside the dataset.
Chapter 4HideHide detailsSee detailsCreating Visualizations and Analyses
Creating Visualizations and Analyses
Lesson 1 • Geospatial and Advanced Visual Types
Introduces maps, heat maps, tree maps, and funnel charts. Expands the visual toolkit for specialised analytical and executive reporting needs.
Lesson 2 • Tables, Pivot Tables, and KPIs
Builds tabular visuals, pivot tables with drill-down, and KPI cards. Addresses reporting needs that require exact values alongside visual summaries.
Lesson 3 • Formatting and Visual Customisation
Covers titles, labels, colours, legends, and axis configuration. Ensures visuals communicate clearly and meet organisational style standards.
Lesson 4 • Analysis Workspace Overview
Introduces the analysis canvas, field wells, and visual menu. Provides the operational foundation for all visualisation work in this chapter.
Lesson 5 • Core Chart Types and Selection
Covers bar, line, pie, scatter, and combo charts with selection guidance. Teaches students to match chart type to data structure and analytical goal.
Chapter 5HideHide detailsSee detailsFilters, Parameters, and Interactivity
Filters, Parameters, and Interactivity
Lesson 1 • Filter Controls and UI Elements
Adds dropdowns, sliders, date pickers, and text inputs to the analysis canvas. Transforms static analyses into interactive self-service tools.
Lesson 2 • Drill-Down and Hierarchies
Sets up field hierarchies and enables drill-down within visuals. Gives users the ability to explore data at multiple levels of granularity.
Lesson 3 • Actions and Cross-Visual Filtering
Configures filter actions, URL actions, and navigation actions between sheets. Enables drill-through and linked exploration across an analysis.
Lesson 4 • Parameters and Dynamic Values
Teaches parameter creation, data types, and binding parameters to filters and visuals. Enables reusable, flexible analyses driven by user-selected values.
Lesson 5 • Analysis-Level Filters
Explains filter scope, filter types, and applying filters across multiple visuals. Connects filtering to the data preparation concepts from the previous chapter.
Chapter 6HideHide detailsSee detailsBuilding and Publishing Dashboards
Building and Publishing Dashboards
Lesson 1 • Publishing Dashboards from Analyses
Walks through the publish workflow, versioning, and replacing published content. Establishes the production deployment process for finished analyses.
Lesson 2 • Dashboard Design Principles
Covers layout, visual hierarchy, whitespace, and storytelling structure. Grounds dashboard construction in design principles before technical publishing steps.
Lesson 3 • Themes and Branding
Applies custom themes, fonts, and colour palettes to align dashboards with brand standards. Produces visually consistent outputs across all published content.
Lesson 4 • Sharing and Access Control
Configures dashboard sharing with users, groups, and public embedding. Connects access control to organisational security and governance policies.
Lesson 5 • Sheets and Multi-Page Layouts
Teaches multi-sheet analysis organisation, sheet navigation, and tab labelling. Enables complex dashboards that separate topics across logical pages.
Chapter 7HideHide detailsSee detailsRow-Level Security and Governance
Row-Level Security and Governance
Lesson 1 • Audit Logging and Compliance
Integrates CloudTrail logging, monitors API activity, and generates access reports. Supports compliance requirements by providing a complete audit trail.
Lesson 2 • Namespaces and Multi-Tenancy
Covers QuickSight namespaces for tenant isolation in SaaS deployments. Enables scalable multi-tenant architectures with separate user pools and assets.
Lesson 3 • Row-Level Security Fundamentals
Explains RLS concepts, permission datasets, and how rules restrict row visibility. Establishes the security model before configuration steps are introduced.
Lesson 4 • Column-Level Security
Restricts visibility of sensitive columns to authorised users or groups. Complements RLS by adding field-level access control to the governance model.
Lesson 5 • Configuring RLS on Datasets
Walks through attaching permission datasets, testing rules, and handling edge cases. Produces correctly restricted datasets ready for dashboard deployment.
Chapter 8HideHide detailsSee detailsAdvanced Analytics and ML Insights
Advanced Analytics and ML Insights
Lesson 1 • QuickSight Q Natural Language Queries
Enables Q, configures topics, and trains the NLQ engine with business terminology. Empowers non-technical users to query data using plain language.
Lesson 2 • Narrative Insights and Auto-Narratives
Adds auto-generated text narratives to visuals using QuickSight's insight engine. Converts chart data into plain-language summaries for executive audiences.
Lesson 3 • Integrating External ML Predictions
Connects SageMaker-generated prediction columns to QuickSight datasets. Extends analytics with custom ML outputs beyond QuickSight's native capabilities.
Lesson 4 • Anomaly Detection and Alerts
Sets up ML-powered anomaly detection and threshold-based email alerts. Enables proactive monitoring without manual data review.
Lesson 5 • Forecasting with ML Models
Configures QuickSight's built-in forecasting on time-series visuals. Produces forward-looking projections that support planning and decision-making.

Your valid completion certificate
This course is for you:
Data analyst: wants to replace manual reporting with interactive, scalable dashboards.
BI developer: needs to add a cloud-native visualization platform to their skill set.
Cloud engineer: supports AWS infrastructure and wants to expand into analytics delivery.
Database administrator: manages data sources and needs to expose them to business users.
Product manager: oversees data-driven products and wants to understand embedded analytics.
Career changer: moving into data roles and needs a practical, portfolio-building project.
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