Mastering Amazon QuickSight for Data-Driven Decision Making
You're under pressure. Reports don’t answer the real questions. Stakeholders demand insights, not dashboards. You know data holds the answers - but extracting them feels slow, messy, and disconnected from actual business impact. What if you could transform raw data into trusted, interactive visuals that guide strategy, secure buy-in, and position you as the go-to decision intelligence expert on your team? This isn’t about learning another tool - it’s about mastering a system that turns uncertainty into authority. Mastering Amazon QuickSight for Data-Driven Decision Making is your proven blueprint to go from overwhelmed analyst to confident insights architect in 30 days or less. By the end, you’ll have built a board-ready dashboard suite that answers real business questions, powered by live data, automated logic, and visual clarity that executives trust. Take Sarah M., a finance operations lead at a mid-market SaaS company. After completing this course, she replaced four legacy reporting tools with one unified QuickSight dashboard. Her CFO now refers to it as “the single source of truth,” and she was fast-tracked into a new analytics leadership role - with a 27% salary increase. This isn’t theoretical. You’ll gain hands-on mastery of data preparation, calculated fields, real-time alerting, and dynamic filtering - skills that directly translate to visibility, credibility, and career momentum. You don’t need prior coding experience or data science training. You do need a structured, step-by-step path proven to deliver results - even if you’ve struggled with BI tools before. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced | Immediate Online Access | Lifetime Updates Included This course is designed for professionals who need flexibility without sacrificing outcomes. You gain instant online access upon enrollment, allowing you to progress at your own pace, on your schedule, from any device. What You Get
- On-Demand Learning: No fixed start dates, no time zones to match - begin whenever you're ready.
- Typical Completion Time: Most learners complete the core curriculum in 25–30 hours, with many building their first production-grade dashboard within the first week.
- Lifetime Access: Return anytime for refresher learning, updated content, or to revisit advanced topics as your role evolves.
- Ongoing Updates: As Amazon QuickSight evolves, so does this course - all future enhancements are included at no extra cost.
- Mobile-Friendly: Learn during commutes, between meetings, or remotely - fully responsive across phones, tablets, and desktops.
Instructor Support You Can Rely On
You’re not alone. Gain direct access to curated guidance from certified data architects with real-world enterprise experience. Each module includes step-by-step walkthroughs, decision trees, and annotated examples to clarify complexity and accelerate understanding. Support is delivered through structured documentation, contextual tips, and scenario-based learning patterns - designed to mirror real job challenges. Certificate of Completion issued by The Art of Service
Upon finishing the course, you’ll earn a globally recognised Certificate of Completion. The Art of Service is trusted by over 100,000 professionals worldwide for high-integrity, vendor-aligned training in data, analytics, and digital transformation. This credential validates your proficiency in Amazon QuickSight and strengthens your professional profile on LinkedIn, resumes, and internal promotion reviews. Zero-Risk Enrollment: Satisfied or Refunded
We offer a 30-day money-back guarantee. If you complete the first three modules and don’t feel you’re gaining immediate, practical value, simply request a full refund - no questions asked. This is our commitment to your success. Transparent Pricing - No Hidden Fees
The price you see is the price you pay. There are no subscriptions, surprise charges, or tiered access locks. One payment grants full, permanent access to all course materials, updates, and the certificate. Accepted Payment Methods: Visa, Mastercard, PayPal. Secure Post-Enrollment Process
After enrollment, you’ll receive an automated confirmation email. Your access details and login instructions will be sent separately once your course materials are prepared - ensuring error-free setup and system compatibility. This Course Works - Even If…
- You’ve never used Amazon QuickSight before.
- You’re not in a formal data role - but need to deliver data-backed decisions.
- Your company uses a mix of AWS and on-prem systems.
- You’ve tried other analytics courses and didn’t retain or apply what you learned.
Why? Because this isn’t about passive consumption. It’s a task-driven, outcome-focused learning journey with real datasets, decision scenarios, and iterative feedback loops. You’ll build actual artifacts you can adapt to your job from Day 1. Trust comes from results, not promises. That’s why this course eliminates risk with lifetime access, proven structure, and a satisfaction guarantee - so you can invest in your growth with complete confidence.
Module 1: Foundations of Amazon QuickSight and Modern Data Strategy - Understanding the role of business intelligence in data-driven organisations
- How Amazon QuickSight fits into the AWS analytics ecosystem
- Comparing QuickSight with Power BI, Tableau, and Looker
- Benefits of cloud-native vs on-premise analytics platforms
- Introduction to SPICE: the Super-fast, Parallel, In-memory Calculation Engine
- QuickSight pricing models: Standard vs Enterprise edition
- Security and governance best practices in AWS analytics
- Setting up your AWS account with proper IAM roles for QuickSight
- Enabling QuickSight within your AWS organisation
- Understanding QuickSight namespaces and user management
- Adding users and groups via email or SSO integration
- Configuring permissions: admin, author, and reader roles
- Enabling data sets, analyses, and dashboards in your account
- Setting default time zones, language, and regional settings
- Best practices for organising dashboards by department or function
- Introduction to the QuickSight console interface
- Navigating between datasets, analyses, and dashboards
- Customising the QuickSight homepage for team productivity
- Using search and filters to locate content quickly
- Setting up notifications and email subscriptions
Module 2: Data Connectivity and Source Integration - Supported data sources: AWS and third-party options
- Connecting to Amazon S3 using manifest files and direct path access
- Importing CSV, JSON, and Parquet files from S3
- Linking to relational databases: Aurora, RDS, Redshift
- Using JDBC connections securely with SSL encryption
- Connecting to Athena for serverless SQL queries
- Linking to AWS Glue Data Catalog for metadata management
- Integrating with Salesforce, ServiceNow, and Marketo via native connectors
- Using the Generic SQL connector for enterprise databases
- Import vs Direct Query: use cases, performance, and limitations
- Setting up scheduled refreshes for imported datasets
- Choosing optimal refresh frequencies based on data volatility
- Handling incremental data loads using timestamp fields
- Managing credentials securely using AWS Secrets Manager
- Testing connectivity and validating data source health
- Troubleshooting common connection errors and timeout issues
- Best practices for naming and documenting data sources
- Designing reusable data source templates for teams
- Using folder structures to organise data sources by domain
- Monitoring data source usage and performance metrics
Module 3: Data Preparation and Cleaning in QuickSight - Overview of the data preparation workflow in QuickSight
- Importing and previewing raw datasets
- Renaming and reordering columns for clarity
- Changing data types: string, integer, decimal, date, boolean
- Handling null values and missing data gracefully
- Removing duplicate rows using automated detection
- Splitting columns by delimiter (commas, pipes, tabs)
- Merging multiple columns into a single field
- Creating calculated fields during data prep
- Using conditional logic to clean categorical variables
- Standardising text: uppercase, lowercase, title case
- Trimming whitespace and special characters
- Extracting substrings using STARTSWITH, CONTAINS, and POS
- Formatting date and time fields for visual consistency
- Creating custom date hierarchies: year, quarter, month, week
- Grouping low-frequency categories into Other buckets
- Filtering rows based on business rules
- Sampling large datasets for faster prototyping
- Using filters to exclude test or archived records
- Saving cleaned datasets for reuse across projects
Module 4: Building Semantic Data Models with Calculated Fields - The power of calculated fields in QuickSight
- Syntax and structure of calculation expressions
- Naming conventions for clarity and reuse
- Arithmetic operations: addition, subtraction, multiplication, division
- Creating key business metrics: revenue, profit, margin, CAC, LTV
- Working with percentages and ratios
- Using IF-ELSE logic for conditional calculations
- Nested IF statements for multi-tier logic
- Using CASE statements as an alternative to IF
- Handling division by zero and null handling with coalesce
- String manipulation functions: CONCAT, REPLACE, SUBSTRING
- Date math: DATEDIFF, DATEADD, and relative calculations
- Boolean logic for flagging conditions
- Using aggregation functions: SUM, AVG, MIN, MAX, COUNT
- Fixed-level aggregations with CALCULATE
- Time-based comparisons: prior period, YTD, QTD, MTD
- Creating rolling averages and moving totals
- Using window functions for ranking and percentiles
- Forecasting with built-in predictive functions
- Leveraging QuickSight ML Insights for anomaly detection
Module 5: Visualisation Theory and Dashboard Design Principles - Choosing the right chart type for your message
- Best practices for bar, column, line, and area charts
- When to use pie, donut, and treemap visuals
- Scatter plots for correlation analysis
- Heat maps for density and performance grading
- Geospatial visualisations using map charts
- Waterfall and funnel charts for process tracking
- Single metric and KPI cards for executive summaries
- Designing for readability: font size, colour contrast, spacing
- Colour theory for data: accessible palettes and CVD safety
- Avoiding misleading scales and truncated axes
- Labelling data points for instant clarity
- Hiding clutter: gridlines, borders, and unnecessary legends
- Using annotations to highlight key events or trends
- Aligning visuals using the grid layout tool
- Maintaining consistent styling across dashboards
- Building mobile-responsive dashboards
- Designing for dark mode and high-resolution displays
- Creating reusable visual templates
- Validating dashboard usability with stakeholders
Module 6: Interactive Analysis and Exploration Techniques - Creating and saving analyses from datasets
- Dragging fields into visual wells: rows, columns, values
- Editing visual properties: titles, legends, tooltips
- Drill-down functionality across hierarchies
- Drill-through to detailed records and source data
- Using cross-filtering to isolate data segments
- Linked analysis: how changes propagate across visuals
- Editing visual-level filters vs analysis-level filters
- Using parameters to control calculations dynamically
- Creating dropdowns and sliders for interactive input
- Setting default values for parameters
- Using dynamic destinations to link dashboard actions
- Navigating between dashboards using buttons and links
- Adding URLs and external links to visuals
- Using bookmarks to save analysis states
- Sharing analysis snapshots via email and links
- Exporting analysis results to PDF or CSV
- Collaborating with team members using comments
- Setting up version control for analysis iterations
- Saving analysis as dashboard or template
Module 7: Dashboard Development and User Experience - Creating a new dashboard from an analysis
- Adding multiple visuals to a single dashboard
- Resizing and repositioning visuals for impact
- Using containers to group related visuals
- Adding text boxes with context and instructions
- Inserting images and logos for branding
- Managing visual layering and transparency
- Setting dashboard-level filters for consistency
- Using global filters across multiple visuals
- Filtering by date ranges dynamically
- Creating cascading filters for dependent selections
- Using control widgets: dropdowns, sliders, calendars
- Linking filter controls to multiple visuals
- Setting default filter values for user experience
- Using template controls for reusable configurations
- Adding interactivity with actions and navigation
- Conditional formatting based on data thresholds
- Using themes to standardise dashboard appearance
- Creating and applying custom themes
- Publishing dashboards for team consumption
Module 8: Security, Sharing, and Governance - Understanding dashboard sharing permissions
- Sharing with individual users or groups
- Setting read-only vs edit permissions
- Using public URLs with embedded dashboards
- Configuring access control for embedded analytics
- Integrating dashboards into portals and apps
- Row-level security (RLS): principles and use cases
- Setting up RLS using user attributes and rules
- Dynamic RLS for multi-tenant reporting
- Testing RLS with different user profiles
- Column-level security to hide sensitive fields
- Auditing dashboard access and usage logs
- Setting up email subscriptions and alerts
- Scheduling PDF or CSV report delivery
- Configuring delivery times and time zones
- Managing subscription lists and recipients
- Revoking access and deactivating shares
- Using tags for resource tracking and cost allocation
- Monitoring usage with AWS Cost Explorer
- Compliance considerations: GDPR, HIPAA, SOC 2
Module 9: Advanced Analytics and Machine Learning Integration - Overview of QuickSight's ML-powered features
- Enabling ML Insights in your account
- Using the Explainable AI feature to understand drivers
- Identifying key influencers in performance outcomes
- Automatic outlier detection in time series data
- Forecasting future trends with confidence intervals
- Setting forecast durations and seasonality rules
- Validating forecast accuracy with historical backtesting
- Creating what-if scenarios using parameters
- Simulating business changes: price, volume, cost
- Combining forecasting with conditional logic
- Using natural language queries with QuickSight Q
- Setting up Q for your dataset and terminology
- Training Q with custom business terms
- Embedding Q into dashboards for self-service
- Analysing unstructured feedback using sentiment detection
- Linking ML features to dashboard interactivity
- Displaying explanations alongside predictions
- Using ML outputs in calculated fields
- Documenting model assumptions and limitations
Module 10: Real-World Projects and Industry-Specific Applications - Building a sales performance dashboard with pipeline analysis
- Creating a customer health scorecard using support and usage data
- Designing a marketing ROI tracker across channels
- Building a SaaS metrics dashboard: MRR, churn, ARR
- Developing an operations efficiency monitor
- Creating a supply chain visibility dashboard
- Analysing website traffic and conversion funnels
- Building a finance close tracking system
- Designing a human capital management (HCM) dashboard
- Creating a real-time alerting system for anomalies
- Using parameters to toggle between departments
- Building multi-language dashboards for global teams
- Integrating financial planning data into live reports
- Linking budget vs actuals with variance analysis
- Using benchmarks and targets in visuals
- Adding trend arrows and performance icons
- Building scorecards with traffic light indicators
- Creating scenario planning dashboards
- Developing executive summary dashboards
- Designing board-ready reporting suites
Module 11: Deployment, Automation, and Scalability - Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement
Module 12: Career Advancement and Certification Preparation - How QuickSight expertise accelerates your career
- Roles that value QuickSight skills: analyst, manager, consultant
- Adding your Certificate of Completion to LinkedIn
- Writing compelling resume bullet points with metrics
- Preparing for analytics interviews with portfolio examples
- Using your dashboards as interview talking points
- Quantifying business impact of your work
- Positioning yourself as a data translator
- Transitioning into high-demand roles: BI developer, data product owner
- Understanding AWS certification pathways
- How this course prepares you for AWS Certified Data Analytics
- Building a personal analytics portfolio
- Hosting dashboards securely for public demonstration
- Creating anonymised versions for job applications
- Earning the Certificate of Completion issued by The Art of Service
- Verification process for employers and recruiters
- Accessing alumni resources and updates
- Joining the global network of data professionals
- Continuing education paths in data strategy and visual analytics
- Next steps: advanced learning, community engagement, and leadership
- Understanding the role of business intelligence in data-driven organisations
- How Amazon QuickSight fits into the AWS analytics ecosystem
- Comparing QuickSight with Power BI, Tableau, and Looker
- Benefits of cloud-native vs on-premise analytics platforms
- Introduction to SPICE: the Super-fast, Parallel, In-memory Calculation Engine
- QuickSight pricing models: Standard vs Enterprise edition
- Security and governance best practices in AWS analytics
- Setting up your AWS account with proper IAM roles for QuickSight
- Enabling QuickSight within your AWS organisation
- Understanding QuickSight namespaces and user management
- Adding users and groups via email or SSO integration
- Configuring permissions: admin, author, and reader roles
- Enabling data sets, analyses, and dashboards in your account
- Setting default time zones, language, and regional settings
- Best practices for organising dashboards by department or function
- Introduction to the QuickSight console interface
- Navigating between datasets, analyses, and dashboards
- Customising the QuickSight homepage for team productivity
- Using search and filters to locate content quickly
- Setting up notifications and email subscriptions
Module 2: Data Connectivity and Source Integration - Supported data sources: AWS and third-party options
- Connecting to Amazon S3 using manifest files and direct path access
- Importing CSV, JSON, and Parquet files from S3
- Linking to relational databases: Aurora, RDS, Redshift
- Using JDBC connections securely with SSL encryption
- Connecting to Athena for serverless SQL queries
- Linking to AWS Glue Data Catalog for metadata management
- Integrating with Salesforce, ServiceNow, and Marketo via native connectors
- Using the Generic SQL connector for enterprise databases
- Import vs Direct Query: use cases, performance, and limitations
- Setting up scheduled refreshes for imported datasets
- Choosing optimal refresh frequencies based on data volatility
- Handling incremental data loads using timestamp fields
- Managing credentials securely using AWS Secrets Manager
- Testing connectivity and validating data source health
- Troubleshooting common connection errors and timeout issues
- Best practices for naming and documenting data sources
- Designing reusable data source templates for teams
- Using folder structures to organise data sources by domain
- Monitoring data source usage and performance metrics
Module 3: Data Preparation and Cleaning in QuickSight - Overview of the data preparation workflow in QuickSight
- Importing and previewing raw datasets
- Renaming and reordering columns for clarity
- Changing data types: string, integer, decimal, date, boolean
- Handling null values and missing data gracefully
- Removing duplicate rows using automated detection
- Splitting columns by delimiter (commas, pipes, tabs)
- Merging multiple columns into a single field
- Creating calculated fields during data prep
- Using conditional logic to clean categorical variables
- Standardising text: uppercase, lowercase, title case
- Trimming whitespace and special characters
- Extracting substrings using STARTSWITH, CONTAINS, and POS
- Formatting date and time fields for visual consistency
- Creating custom date hierarchies: year, quarter, month, week
- Grouping low-frequency categories into Other buckets
- Filtering rows based on business rules
- Sampling large datasets for faster prototyping
- Using filters to exclude test or archived records
- Saving cleaned datasets for reuse across projects
Module 4: Building Semantic Data Models with Calculated Fields - The power of calculated fields in QuickSight
- Syntax and structure of calculation expressions
- Naming conventions for clarity and reuse
- Arithmetic operations: addition, subtraction, multiplication, division
- Creating key business metrics: revenue, profit, margin, CAC, LTV
- Working with percentages and ratios
- Using IF-ELSE logic for conditional calculations
- Nested IF statements for multi-tier logic
- Using CASE statements as an alternative to IF
- Handling division by zero and null handling with coalesce
- String manipulation functions: CONCAT, REPLACE, SUBSTRING
- Date math: DATEDIFF, DATEADD, and relative calculations
- Boolean logic for flagging conditions
- Using aggregation functions: SUM, AVG, MIN, MAX, COUNT
- Fixed-level aggregations with CALCULATE
- Time-based comparisons: prior period, YTD, QTD, MTD
- Creating rolling averages and moving totals
- Using window functions for ranking and percentiles
- Forecasting with built-in predictive functions
- Leveraging QuickSight ML Insights for anomaly detection
Module 5: Visualisation Theory and Dashboard Design Principles - Choosing the right chart type for your message
- Best practices for bar, column, line, and area charts
- When to use pie, donut, and treemap visuals
- Scatter plots for correlation analysis
- Heat maps for density and performance grading
- Geospatial visualisations using map charts
- Waterfall and funnel charts for process tracking
- Single metric and KPI cards for executive summaries
- Designing for readability: font size, colour contrast, spacing
- Colour theory for data: accessible palettes and CVD safety
- Avoiding misleading scales and truncated axes
- Labelling data points for instant clarity
- Hiding clutter: gridlines, borders, and unnecessary legends
- Using annotations to highlight key events or trends
- Aligning visuals using the grid layout tool
- Maintaining consistent styling across dashboards
- Building mobile-responsive dashboards
- Designing for dark mode and high-resolution displays
- Creating reusable visual templates
- Validating dashboard usability with stakeholders
Module 6: Interactive Analysis and Exploration Techniques - Creating and saving analyses from datasets
- Dragging fields into visual wells: rows, columns, values
- Editing visual properties: titles, legends, tooltips
- Drill-down functionality across hierarchies
- Drill-through to detailed records and source data
- Using cross-filtering to isolate data segments
- Linked analysis: how changes propagate across visuals
- Editing visual-level filters vs analysis-level filters
- Using parameters to control calculations dynamically
- Creating dropdowns and sliders for interactive input
- Setting default values for parameters
- Using dynamic destinations to link dashboard actions
- Navigating between dashboards using buttons and links
- Adding URLs and external links to visuals
- Using bookmarks to save analysis states
- Sharing analysis snapshots via email and links
- Exporting analysis results to PDF or CSV
- Collaborating with team members using comments
- Setting up version control for analysis iterations
- Saving analysis as dashboard or template
Module 7: Dashboard Development and User Experience - Creating a new dashboard from an analysis
- Adding multiple visuals to a single dashboard
- Resizing and repositioning visuals for impact
- Using containers to group related visuals
- Adding text boxes with context and instructions
- Inserting images and logos for branding
- Managing visual layering and transparency
- Setting dashboard-level filters for consistency
- Using global filters across multiple visuals
- Filtering by date ranges dynamically
- Creating cascading filters for dependent selections
- Using control widgets: dropdowns, sliders, calendars
- Linking filter controls to multiple visuals
- Setting default filter values for user experience
- Using template controls for reusable configurations
- Adding interactivity with actions and navigation
- Conditional formatting based on data thresholds
- Using themes to standardise dashboard appearance
- Creating and applying custom themes
- Publishing dashboards for team consumption
Module 8: Security, Sharing, and Governance - Understanding dashboard sharing permissions
- Sharing with individual users or groups
- Setting read-only vs edit permissions
- Using public URLs with embedded dashboards
- Configuring access control for embedded analytics
- Integrating dashboards into portals and apps
- Row-level security (RLS): principles and use cases
- Setting up RLS using user attributes and rules
- Dynamic RLS for multi-tenant reporting
- Testing RLS with different user profiles
- Column-level security to hide sensitive fields
- Auditing dashboard access and usage logs
- Setting up email subscriptions and alerts
- Scheduling PDF or CSV report delivery
- Configuring delivery times and time zones
- Managing subscription lists and recipients
- Revoking access and deactivating shares
- Using tags for resource tracking and cost allocation
- Monitoring usage with AWS Cost Explorer
- Compliance considerations: GDPR, HIPAA, SOC 2
Module 9: Advanced Analytics and Machine Learning Integration - Overview of QuickSight's ML-powered features
- Enabling ML Insights in your account
- Using the Explainable AI feature to understand drivers
- Identifying key influencers in performance outcomes
- Automatic outlier detection in time series data
- Forecasting future trends with confidence intervals
- Setting forecast durations and seasonality rules
- Validating forecast accuracy with historical backtesting
- Creating what-if scenarios using parameters
- Simulating business changes: price, volume, cost
- Combining forecasting with conditional logic
- Using natural language queries with QuickSight Q
- Setting up Q for your dataset and terminology
- Training Q with custom business terms
- Embedding Q into dashboards for self-service
- Analysing unstructured feedback using sentiment detection
- Linking ML features to dashboard interactivity
- Displaying explanations alongside predictions
- Using ML outputs in calculated fields
- Documenting model assumptions and limitations
Module 10: Real-World Projects and Industry-Specific Applications - Building a sales performance dashboard with pipeline analysis
- Creating a customer health scorecard using support and usage data
- Designing a marketing ROI tracker across channels
- Building a SaaS metrics dashboard: MRR, churn, ARR
- Developing an operations efficiency monitor
- Creating a supply chain visibility dashboard
- Analysing website traffic and conversion funnels
- Building a finance close tracking system
- Designing a human capital management (HCM) dashboard
- Creating a real-time alerting system for anomalies
- Using parameters to toggle between departments
- Building multi-language dashboards for global teams
- Integrating financial planning data into live reports
- Linking budget vs actuals with variance analysis
- Using benchmarks and targets in visuals
- Adding trend arrows and performance icons
- Building scorecards with traffic light indicators
- Creating scenario planning dashboards
- Developing executive summary dashboards
- Designing board-ready reporting suites
Module 11: Deployment, Automation, and Scalability - Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement
Module 12: Career Advancement and Certification Preparation - How QuickSight expertise accelerates your career
- Roles that value QuickSight skills: analyst, manager, consultant
- Adding your Certificate of Completion to LinkedIn
- Writing compelling resume bullet points with metrics
- Preparing for analytics interviews with portfolio examples
- Using your dashboards as interview talking points
- Quantifying business impact of your work
- Positioning yourself as a data translator
- Transitioning into high-demand roles: BI developer, data product owner
- Understanding AWS certification pathways
- How this course prepares you for AWS Certified Data Analytics
- Building a personal analytics portfolio
- Hosting dashboards securely for public demonstration
- Creating anonymised versions for job applications
- Earning the Certificate of Completion issued by The Art of Service
- Verification process for employers and recruiters
- Accessing alumni resources and updates
- Joining the global network of data professionals
- Continuing education paths in data strategy and visual analytics
- Next steps: advanced learning, community engagement, and leadership
- Overview of the data preparation workflow in QuickSight
- Importing and previewing raw datasets
- Renaming and reordering columns for clarity
- Changing data types: string, integer, decimal, date, boolean
- Handling null values and missing data gracefully
- Removing duplicate rows using automated detection
- Splitting columns by delimiter (commas, pipes, tabs)
- Merging multiple columns into a single field
- Creating calculated fields during data prep
- Using conditional logic to clean categorical variables
- Standardising text: uppercase, lowercase, title case
- Trimming whitespace and special characters
- Extracting substrings using STARTSWITH, CONTAINS, and POS
- Formatting date and time fields for visual consistency
- Creating custom date hierarchies: year, quarter, month, week
- Grouping low-frequency categories into Other buckets
- Filtering rows based on business rules
- Sampling large datasets for faster prototyping
- Using filters to exclude test or archived records
- Saving cleaned datasets for reuse across projects
Module 4: Building Semantic Data Models with Calculated Fields - The power of calculated fields in QuickSight
- Syntax and structure of calculation expressions
- Naming conventions for clarity and reuse
- Arithmetic operations: addition, subtraction, multiplication, division
- Creating key business metrics: revenue, profit, margin, CAC, LTV
- Working with percentages and ratios
- Using IF-ELSE logic for conditional calculations
- Nested IF statements for multi-tier logic
- Using CASE statements as an alternative to IF
- Handling division by zero and null handling with coalesce
- String manipulation functions: CONCAT, REPLACE, SUBSTRING
- Date math: DATEDIFF, DATEADD, and relative calculations
- Boolean logic for flagging conditions
- Using aggregation functions: SUM, AVG, MIN, MAX, COUNT
- Fixed-level aggregations with CALCULATE
- Time-based comparisons: prior period, YTD, QTD, MTD
- Creating rolling averages and moving totals
- Using window functions for ranking and percentiles
- Forecasting with built-in predictive functions
- Leveraging QuickSight ML Insights for anomaly detection
Module 5: Visualisation Theory and Dashboard Design Principles - Choosing the right chart type for your message
- Best practices for bar, column, line, and area charts
- When to use pie, donut, and treemap visuals
- Scatter plots for correlation analysis
- Heat maps for density and performance grading
- Geospatial visualisations using map charts
- Waterfall and funnel charts for process tracking
- Single metric and KPI cards for executive summaries
- Designing for readability: font size, colour contrast, spacing
- Colour theory for data: accessible palettes and CVD safety
- Avoiding misleading scales and truncated axes
- Labelling data points for instant clarity
- Hiding clutter: gridlines, borders, and unnecessary legends
- Using annotations to highlight key events or trends
- Aligning visuals using the grid layout tool
- Maintaining consistent styling across dashboards
- Building mobile-responsive dashboards
- Designing for dark mode and high-resolution displays
- Creating reusable visual templates
- Validating dashboard usability with stakeholders
Module 6: Interactive Analysis and Exploration Techniques - Creating and saving analyses from datasets
- Dragging fields into visual wells: rows, columns, values
- Editing visual properties: titles, legends, tooltips
- Drill-down functionality across hierarchies
- Drill-through to detailed records and source data
- Using cross-filtering to isolate data segments
- Linked analysis: how changes propagate across visuals
- Editing visual-level filters vs analysis-level filters
- Using parameters to control calculations dynamically
- Creating dropdowns and sliders for interactive input
- Setting default values for parameters
- Using dynamic destinations to link dashboard actions
- Navigating between dashboards using buttons and links
- Adding URLs and external links to visuals
- Using bookmarks to save analysis states
- Sharing analysis snapshots via email and links
- Exporting analysis results to PDF or CSV
- Collaborating with team members using comments
- Setting up version control for analysis iterations
- Saving analysis as dashboard or template
Module 7: Dashboard Development and User Experience - Creating a new dashboard from an analysis
- Adding multiple visuals to a single dashboard
- Resizing and repositioning visuals for impact
- Using containers to group related visuals
- Adding text boxes with context and instructions
- Inserting images and logos for branding
- Managing visual layering and transparency
- Setting dashboard-level filters for consistency
- Using global filters across multiple visuals
- Filtering by date ranges dynamically
- Creating cascading filters for dependent selections
- Using control widgets: dropdowns, sliders, calendars
- Linking filter controls to multiple visuals
- Setting default filter values for user experience
- Using template controls for reusable configurations
- Adding interactivity with actions and navigation
- Conditional formatting based on data thresholds
- Using themes to standardise dashboard appearance
- Creating and applying custom themes
- Publishing dashboards for team consumption
Module 8: Security, Sharing, and Governance - Understanding dashboard sharing permissions
- Sharing with individual users or groups
- Setting read-only vs edit permissions
- Using public URLs with embedded dashboards
- Configuring access control for embedded analytics
- Integrating dashboards into portals and apps
- Row-level security (RLS): principles and use cases
- Setting up RLS using user attributes and rules
- Dynamic RLS for multi-tenant reporting
- Testing RLS with different user profiles
- Column-level security to hide sensitive fields
- Auditing dashboard access and usage logs
- Setting up email subscriptions and alerts
- Scheduling PDF or CSV report delivery
- Configuring delivery times and time zones
- Managing subscription lists and recipients
- Revoking access and deactivating shares
- Using tags for resource tracking and cost allocation
- Monitoring usage with AWS Cost Explorer
- Compliance considerations: GDPR, HIPAA, SOC 2
Module 9: Advanced Analytics and Machine Learning Integration - Overview of QuickSight's ML-powered features
- Enabling ML Insights in your account
- Using the Explainable AI feature to understand drivers
- Identifying key influencers in performance outcomes
- Automatic outlier detection in time series data
- Forecasting future trends with confidence intervals
- Setting forecast durations and seasonality rules
- Validating forecast accuracy with historical backtesting
- Creating what-if scenarios using parameters
- Simulating business changes: price, volume, cost
- Combining forecasting with conditional logic
- Using natural language queries with QuickSight Q
- Setting up Q for your dataset and terminology
- Training Q with custom business terms
- Embedding Q into dashboards for self-service
- Analysing unstructured feedback using sentiment detection
- Linking ML features to dashboard interactivity
- Displaying explanations alongside predictions
- Using ML outputs in calculated fields
- Documenting model assumptions and limitations
Module 10: Real-World Projects and Industry-Specific Applications - Building a sales performance dashboard with pipeline analysis
- Creating a customer health scorecard using support and usage data
- Designing a marketing ROI tracker across channels
- Building a SaaS metrics dashboard: MRR, churn, ARR
- Developing an operations efficiency monitor
- Creating a supply chain visibility dashboard
- Analysing website traffic and conversion funnels
- Building a finance close tracking system
- Designing a human capital management (HCM) dashboard
- Creating a real-time alerting system for anomalies
- Using parameters to toggle between departments
- Building multi-language dashboards for global teams
- Integrating financial planning data into live reports
- Linking budget vs actuals with variance analysis
- Using benchmarks and targets in visuals
- Adding trend arrows and performance icons
- Building scorecards with traffic light indicators
- Creating scenario planning dashboards
- Developing executive summary dashboards
- Designing board-ready reporting suites
Module 11: Deployment, Automation, and Scalability - Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement
Module 12: Career Advancement and Certification Preparation - How QuickSight expertise accelerates your career
- Roles that value QuickSight skills: analyst, manager, consultant
- Adding your Certificate of Completion to LinkedIn
- Writing compelling resume bullet points with metrics
- Preparing for analytics interviews with portfolio examples
- Using your dashboards as interview talking points
- Quantifying business impact of your work
- Positioning yourself as a data translator
- Transitioning into high-demand roles: BI developer, data product owner
- Understanding AWS certification pathways
- How this course prepares you for AWS Certified Data Analytics
- Building a personal analytics portfolio
- Hosting dashboards securely for public demonstration
- Creating anonymised versions for job applications
- Earning the Certificate of Completion issued by The Art of Service
- Verification process for employers and recruiters
- Accessing alumni resources and updates
- Joining the global network of data professionals
- Continuing education paths in data strategy and visual analytics
- Next steps: advanced learning, community engagement, and leadership
- Choosing the right chart type for your message
- Best practices for bar, column, line, and area charts
- When to use pie, donut, and treemap visuals
- Scatter plots for correlation analysis
- Heat maps for density and performance grading
- Geospatial visualisations using map charts
- Waterfall and funnel charts for process tracking
- Single metric and KPI cards for executive summaries
- Designing for readability: font size, colour contrast, spacing
- Colour theory for data: accessible palettes and CVD safety
- Avoiding misleading scales and truncated axes
- Labelling data points for instant clarity
- Hiding clutter: gridlines, borders, and unnecessary legends
- Using annotations to highlight key events or trends
- Aligning visuals using the grid layout tool
- Maintaining consistent styling across dashboards
- Building mobile-responsive dashboards
- Designing for dark mode and high-resolution displays
- Creating reusable visual templates
- Validating dashboard usability with stakeholders
Module 6: Interactive Analysis and Exploration Techniques - Creating and saving analyses from datasets
- Dragging fields into visual wells: rows, columns, values
- Editing visual properties: titles, legends, tooltips
- Drill-down functionality across hierarchies
- Drill-through to detailed records and source data
- Using cross-filtering to isolate data segments
- Linked analysis: how changes propagate across visuals
- Editing visual-level filters vs analysis-level filters
- Using parameters to control calculations dynamically
- Creating dropdowns and sliders for interactive input
- Setting default values for parameters
- Using dynamic destinations to link dashboard actions
- Navigating between dashboards using buttons and links
- Adding URLs and external links to visuals
- Using bookmarks to save analysis states
- Sharing analysis snapshots via email and links
- Exporting analysis results to PDF or CSV
- Collaborating with team members using comments
- Setting up version control for analysis iterations
- Saving analysis as dashboard or template
Module 7: Dashboard Development and User Experience - Creating a new dashboard from an analysis
- Adding multiple visuals to a single dashboard
- Resizing and repositioning visuals for impact
- Using containers to group related visuals
- Adding text boxes with context and instructions
- Inserting images and logos for branding
- Managing visual layering and transparency
- Setting dashboard-level filters for consistency
- Using global filters across multiple visuals
- Filtering by date ranges dynamically
- Creating cascading filters for dependent selections
- Using control widgets: dropdowns, sliders, calendars
- Linking filter controls to multiple visuals
- Setting default filter values for user experience
- Using template controls for reusable configurations
- Adding interactivity with actions and navigation
- Conditional formatting based on data thresholds
- Using themes to standardise dashboard appearance
- Creating and applying custom themes
- Publishing dashboards for team consumption
Module 8: Security, Sharing, and Governance - Understanding dashboard sharing permissions
- Sharing with individual users or groups
- Setting read-only vs edit permissions
- Using public URLs with embedded dashboards
- Configuring access control for embedded analytics
- Integrating dashboards into portals and apps
- Row-level security (RLS): principles and use cases
- Setting up RLS using user attributes and rules
- Dynamic RLS for multi-tenant reporting
- Testing RLS with different user profiles
- Column-level security to hide sensitive fields
- Auditing dashboard access and usage logs
- Setting up email subscriptions and alerts
- Scheduling PDF or CSV report delivery
- Configuring delivery times and time zones
- Managing subscription lists and recipients
- Revoking access and deactivating shares
- Using tags for resource tracking and cost allocation
- Monitoring usage with AWS Cost Explorer
- Compliance considerations: GDPR, HIPAA, SOC 2
Module 9: Advanced Analytics and Machine Learning Integration - Overview of QuickSight's ML-powered features
- Enabling ML Insights in your account
- Using the Explainable AI feature to understand drivers
- Identifying key influencers in performance outcomes
- Automatic outlier detection in time series data
- Forecasting future trends with confidence intervals
- Setting forecast durations and seasonality rules
- Validating forecast accuracy with historical backtesting
- Creating what-if scenarios using parameters
- Simulating business changes: price, volume, cost
- Combining forecasting with conditional logic
- Using natural language queries with QuickSight Q
- Setting up Q for your dataset and terminology
- Training Q with custom business terms
- Embedding Q into dashboards for self-service
- Analysing unstructured feedback using sentiment detection
- Linking ML features to dashboard interactivity
- Displaying explanations alongside predictions
- Using ML outputs in calculated fields
- Documenting model assumptions and limitations
Module 10: Real-World Projects and Industry-Specific Applications - Building a sales performance dashboard with pipeline analysis
- Creating a customer health scorecard using support and usage data
- Designing a marketing ROI tracker across channels
- Building a SaaS metrics dashboard: MRR, churn, ARR
- Developing an operations efficiency monitor
- Creating a supply chain visibility dashboard
- Analysing website traffic and conversion funnels
- Building a finance close tracking system
- Designing a human capital management (HCM) dashboard
- Creating a real-time alerting system for anomalies
- Using parameters to toggle between departments
- Building multi-language dashboards for global teams
- Integrating financial planning data into live reports
- Linking budget vs actuals with variance analysis
- Using benchmarks and targets in visuals
- Adding trend arrows and performance icons
- Building scorecards with traffic light indicators
- Creating scenario planning dashboards
- Developing executive summary dashboards
- Designing board-ready reporting suites
Module 11: Deployment, Automation, and Scalability - Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement
Module 12: Career Advancement and Certification Preparation - How QuickSight expertise accelerates your career
- Roles that value QuickSight skills: analyst, manager, consultant
- Adding your Certificate of Completion to LinkedIn
- Writing compelling resume bullet points with metrics
- Preparing for analytics interviews with portfolio examples
- Using your dashboards as interview talking points
- Quantifying business impact of your work
- Positioning yourself as a data translator
- Transitioning into high-demand roles: BI developer, data product owner
- Understanding AWS certification pathways
- How this course prepares you for AWS Certified Data Analytics
- Building a personal analytics portfolio
- Hosting dashboards securely for public demonstration
- Creating anonymised versions for job applications
- Earning the Certificate of Completion issued by The Art of Service
- Verification process for employers and recruiters
- Accessing alumni resources and updates
- Joining the global network of data professionals
- Continuing education paths in data strategy and visual analytics
- Next steps: advanced learning, community engagement, and leadership
- Creating a new dashboard from an analysis
- Adding multiple visuals to a single dashboard
- Resizing and repositioning visuals for impact
- Using containers to group related visuals
- Adding text boxes with context and instructions
- Inserting images and logos for branding
- Managing visual layering and transparency
- Setting dashboard-level filters for consistency
- Using global filters across multiple visuals
- Filtering by date ranges dynamically
- Creating cascading filters for dependent selections
- Using control widgets: dropdowns, sliders, calendars
- Linking filter controls to multiple visuals
- Setting default filter values for user experience
- Using template controls for reusable configurations
- Adding interactivity with actions and navigation
- Conditional formatting based on data thresholds
- Using themes to standardise dashboard appearance
- Creating and applying custom themes
- Publishing dashboards for team consumption
Module 8: Security, Sharing, and Governance - Understanding dashboard sharing permissions
- Sharing with individual users or groups
- Setting read-only vs edit permissions
- Using public URLs with embedded dashboards
- Configuring access control for embedded analytics
- Integrating dashboards into portals and apps
- Row-level security (RLS): principles and use cases
- Setting up RLS using user attributes and rules
- Dynamic RLS for multi-tenant reporting
- Testing RLS with different user profiles
- Column-level security to hide sensitive fields
- Auditing dashboard access and usage logs
- Setting up email subscriptions and alerts
- Scheduling PDF or CSV report delivery
- Configuring delivery times and time zones
- Managing subscription lists and recipients
- Revoking access and deactivating shares
- Using tags for resource tracking and cost allocation
- Monitoring usage with AWS Cost Explorer
- Compliance considerations: GDPR, HIPAA, SOC 2
Module 9: Advanced Analytics and Machine Learning Integration - Overview of QuickSight's ML-powered features
- Enabling ML Insights in your account
- Using the Explainable AI feature to understand drivers
- Identifying key influencers in performance outcomes
- Automatic outlier detection in time series data
- Forecasting future trends with confidence intervals
- Setting forecast durations and seasonality rules
- Validating forecast accuracy with historical backtesting
- Creating what-if scenarios using parameters
- Simulating business changes: price, volume, cost
- Combining forecasting with conditional logic
- Using natural language queries with QuickSight Q
- Setting up Q for your dataset and terminology
- Training Q with custom business terms
- Embedding Q into dashboards for self-service
- Analysing unstructured feedback using sentiment detection
- Linking ML features to dashboard interactivity
- Displaying explanations alongside predictions
- Using ML outputs in calculated fields
- Documenting model assumptions and limitations
Module 10: Real-World Projects and Industry-Specific Applications - Building a sales performance dashboard with pipeline analysis
- Creating a customer health scorecard using support and usage data
- Designing a marketing ROI tracker across channels
- Building a SaaS metrics dashboard: MRR, churn, ARR
- Developing an operations efficiency monitor
- Creating a supply chain visibility dashboard
- Analysing website traffic and conversion funnels
- Building a finance close tracking system
- Designing a human capital management (HCM) dashboard
- Creating a real-time alerting system for anomalies
- Using parameters to toggle between departments
- Building multi-language dashboards for global teams
- Integrating financial planning data into live reports
- Linking budget vs actuals with variance analysis
- Using benchmarks and targets in visuals
- Adding trend arrows and performance icons
- Building scorecards with traffic light indicators
- Creating scenario planning dashboards
- Developing executive summary dashboards
- Designing board-ready reporting suites
Module 11: Deployment, Automation, and Scalability - Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement
Module 12: Career Advancement and Certification Preparation - How QuickSight expertise accelerates your career
- Roles that value QuickSight skills: analyst, manager, consultant
- Adding your Certificate of Completion to LinkedIn
- Writing compelling resume bullet points with metrics
- Preparing for analytics interviews with portfolio examples
- Using your dashboards as interview talking points
- Quantifying business impact of your work
- Positioning yourself as a data translator
- Transitioning into high-demand roles: BI developer, data product owner
- Understanding AWS certification pathways
- How this course prepares you for AWS Certified Data Analytics
- Building a personal analytics portfolio
- Hosting dashboards securely for public demonstration
- Creating anonymised versions for job applications
- Earning the Certificate of Completion issued by The Art of Service
- Verification process for employers and recruiters
- Accessing alumni resources and updates
- Joining the global network of data professionals
- Continuing education paths in data strategy and visual analytics
- Next steps: advanced learning, community engagement, and leadership
- Overview of QuickSight's ML-powered features
- Enabling ML Insights in your account
- Using the Explainable AI feature to understand drivers
- Identifying key influencers in performance outcomes
- Automatic outlier detection in time series data
- Forecasting future trends with confidence intervals
- Setting forecast durations and seasonality rules
- Validating forecast accuracy with historical backtesting
- Creating what-if scenarios using parameters
- Simulating business changes: price, volume, cost
- Combining forecasting with conditional logic
- Using natural language queries with QuickSight Q
- Setting up Q for your dataset and terminology
- Training Q with custom business terms
- Embedding Q into dashboards for self-service
- Analysing unstructured feedback using sentiment detection
- Linking ML features to dashboard interactivity
- Displaying explanations alongside predictions
- Using ML outputs in calculated fields
- Documenting model assumptions and limitations
Module 10: Real-World Projects and Industry-Specific Applications - Building a sales performance dashboard with pipeline analysis
- Creating a customer health scorecard using support and usage data
- Designing a marketing ROI tracker across channels
- Building a SaaS metrics dashboard: MRR, churn, ARR
- Developing an operations efficiency monitor
- Creating a supply chain visibility dashboard
- Analysing website traffic and conversion funnels
- Building a finance close tracking system
- Designing a human capital management (HCM) dashboard
- Creating a real-time alerting system for anomalies
- Using parameters to toggle between departments
- Building multi-language dashboards for global teams
- Integrating financial planning data into live reports
- Linking budget vs actuals with variance analysis
- Using benchmarks and targets in visuals
- Adding trend arrows and performance icons
- Building scorecards with traffic light indicators
- Creating scenario planning dashboards
- Developing executive summary dashboards
- Designing board-ready reporting suites
Module 11: Deployment, Automation, and Scalability - Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement
Module 12: Career Advancement and Certification Preparation - How QuickSight expertise accelerates your career
- Roles that value QuickSight skills: analyst, manager, consultant
- Adding your Certificate of Completion to LinkedIn
- Writing compelling resume bullet points with metrics
- Preparing for analytics interviews with portfolio examples
- Using your dashboards as interview talking points
- Quantifying business impact of your work
- Positioning yourself as a data translator
- Transitioning into high-demand roles: BI developer, data product owner
- Understanding AWS certification pathways
- How this course prepares you for AWS Certified Data Analytics
- Building a personal analytics portfolio
- Hosting dashboards securely for public demonstration
- Creating anonymised versions for job applications
- Earning the Certificate of Completion issued by The Art of Service
- Verification process for employers and recruiters
- Accessing alumni resources and updates
- Joining the global network of data professionals
- Continuing education paths in data strategy and visual analytics
- Next steps: advanced learning, community engagement, and leadership
- Best practices for naming dashboards and datasets
- Organising content using folders and tags
- Setting up dashboard versioning and change logs
- Cloning dashboards for A/B testing and iterations
- Using templates to standardise reporting formats
- Exporting and importing dashboards across accounts
- Setting up automated dataset refresh pipelines
- Monitoring refresh success and failure alerts
- Handling schema changes in source data
- Using field aliases to maintain consistency
- Automating dashboard distribution via email
- Scheduling batch deliveries to stakeholder groups
- Setting up real-time SPICE ingestion for critical data
- Managing SPICE capacity and dataset priorities
- Archiving outdated dashboards and datasets
- Conducting quarterly dashboard audits
- Documenting business logic and calculation formulas
- Creating user guides and tooltips for end users
- Training non-technical users on dashboard navigation
- Building feedback loops for continuous improvement