AI-Powered Data Visualisation Training Course

Participants can assess the structure, meaning and limitations of data before visualising it.

• Grain, unit of analysis and record-level versus aggregate datasets
• Dimensions, measures, identifiers and derived fields within reporting datasets
• Data dictionaries documenting definitions, sources, refreshes and business ownership
• Recognising missingness, duplicates, outliers and inconsistent category values
• Denominators, rates and population bases for meaningful comparisons
• Time-series frequency, seasonality and calendar effects in reported metrics

Participants can use generative AI appropriately within a controlled visual analytics workflow.

• Large language model capabilities, limitations and common analytical failure modes
• Prompt components: role, task, context, data, constraints and format
• Separating ideation support from factual analysis and final decision evidence
• Hallucination patterns in generated calculations, citations, code and narratives
• Human-in-the-loop review points for AI-assisted visualisation production
• Selecting AI tasks suitable for public, internal and restricted data

Participants can write prompts that generate useful, reviewable analytical and visual outputs.

• Structured prompts requesting assumptions, calculations, checks and presentation formats
• Few-shot prompting with approved examples of chart specifications
• Chain-of-verification prompts for checking generated analytical claims independently
• Prompting exploratory questions by segment, period, geography and metric
• Requesting alternative hypotheses rather than accepting first-generated explanations
• Prompt libraries organised by profiling, charting, narration and quality assurance

Participants can prepare source data and document checks before AI-assisted visualisation.

• Tidy data principles for variables, observations and analytical units
• Power Query transformations for types, joins, filters and calculated columns
• Validation rules comparing record counts, totals, ranges and reconciliation values
• Handling nulls through deletion, imputation, categorisation and disclosure
• Outlier investigation using business rules, distribution checks and source verification
• Data lineage records connecting source systems, transformations and dashboard measures

Participants can create and interpret measures that support fair visual comparison.

• Ratios, rates and indexed values for comparing unlike populations
• Percent change, percentage points and base effects explained correctly
• Descriptive statistics: mean, median, quartiles, variance and distribution shape
• Confidence intervals and sample sizes displayed without overstating certainty
• Correlation, causation and confounding risks in visual analytical narratives
• DAX measure logic for filtered contexts and reusable dashboard metrics

Participants can select chart forms that accurately communicate comparison, change and distribution.

• Cleveland and McGill ranking for perceptual accuracy in chart encodings
• Bars, lines, dots and tables selected by analytical question
• Scatterplots exposing relationships, clusters, outliers and possible nonlinear patterns
• Histograms and box plots showing distribution, spread and unusual values
• Small multiples comparing segments without overloaded single-chart displays
• Avoiding dual axes, truncated scales and misleading area encodings

Participants can design readable visual products for diverse users and viewing conditions.

• Gestalt principles guiding grouping, proximity, similarity and visual hierarchy
• Pre-attentive attributes directing attention through position, colour and size
• ColourBrewer palettes supporting sequential, diverging and categorical data
• WCAG 2.2 contrast requirements for text, marks and interface elements
• Direct labelling, annotations and titles that state analytical findings
• Responsive layouts for desktop, projected and mobile dashboard viewing

Participants can use AI to draft chart specifications while retaining design and analytical control.

• Chart specification prompts defining fields, aggregation, encodings and filters
• Requesting Vega-Lite JSON specifications from plain-language analytical requirements
• Critiquing AI-proposed chart types against task and perception principles
• Generating annotation drafts tied to verified values and calculations
• Converting chart feedback into precise redesign prompts and acceptance tests
• Documenting prompt versions, outputs, edits and reviewer decisions

Participants can build validated Power BI reports with AI-assisted design support.

• Star schema design using fact tables, dimensions and conformed keys
• DAX measures for current values, prior periods and variance analysis
• Report pages structured around executive, manager and analyst questions
• Slicers, drill-through and tooltips supporting controlled user exploration
• AI-assisted drafting of DAX explanations and measure documentation
• Power BI validation against source totals and approved business definitions

Participants can create Tableau views and dashboards for interactive exploratory analysis.

• Tableau dimensions, measures, discrete fields and continuous axes
• Calculated fields for segments, date logic and conditional classifications
• Parameters and sets supporting scenario comparisons and user-controlled views
• Dashboard actions linking filters, highlights and detailed contextual evidence
• AI-assisted worksheet planning from documented business and visual requirements
• Tableau performance checks for extracts, filters and calculated fields

Participants can inspect, adapt and validate AI-generated Python code for data visualisation.

• Pandas dataframes for filtering, grouping, joining and calculating measures
• Matplotlib and Seaborn charts generated from explicit analytical specifications
• Prompting Python code with input schema, output requirements and checks
• Reading generated code for transformations, assumptions and calculation errors
• Reproducible notebooks containing parameters, source references and chart outputs
• Plotly interactive charts for hover details, filters and web sharing

Participants can design dashboard structures that support recurring management decisions.

• Information architecture separating summary, diagnosis and detailed evidence layers
• KPI scorecards using targets, thresholds, trends and owner accountability
• Exception reporting highlighting material variance, risk and required intervention
• 13-week trend views distinguishing temporary movements from sustained performance changes
• Dashboard wireframes tested against priority decisions and meeting routines
• Refresh schedules, metric ownership and change-control responsibilities documented

Participants can turn verified visual findings into concise decision-oriented stories.

• Situation-complication-question-answer structure for executive analytical narratives
• Message titles stating findings rather than merely naming chart subjects
• Annotations connecting evidence, implications, uncertainty and recommended actions
• AI-generated narrative drafts checked against approved metrics and evidence
• Progressive disclosure balancing headline messages with accessible supporting detail
• Briefing notes distinguishing findings, interpretations, recommendations and decisions

Participants can present forecasts and scenarios without overstating model certainty.

• Baseline forecasts distinguished from targets, budgets and scenario assumptions
• Prediction intervals visualised with bands, ranges and plain-language explanations
• Driver trees showing assumptions behind revenue, demand or service forecasts
• What-if parameters testing sensitivity to volume, price and cost changes
• AI assistance for scenario descriptions, assumptions registers and chart annotations
• Forecast back-testing comparing prior predictions with actual observed outcomes

Participants can apply governance controls to protect data and assure AI-assisted outputs.

• Data classification distinguishing public, internal, confidential and restricted information
• PII minimisation, masking and aggregation before external AI tool use
• EU AI Act risk concepts relevant to workplace analytical applications
• NIST AI Risk Management Framework functions for documented controls
• Copyright, licensing and attribution considerations for generated visual assets
• Approval workflows for prompts, datasets, dashboards and published narratives

Participants can conduct structured reviews of visual accuracy, usability and decision relevance.

• Analytical QA checking formulas, filters, totals and date logic
• Visual QA checking scales, labels, sorting, colour and annotation accuracy
• Usability review using task completion, comprehension and error observations
• Red-team questioning of causal claims, omitted variables and narrative bias
• AI output review checklist for unsupported claims and fabricated references
• Stakeholder sign-off criteria covering accuracy, usefulness and governance compliance

Participants can adapt AI-assisted visualisation methods to common sector-specific reporting needs.

• Banking risk dashboards for portfolio exposure, delinquency and concentration
• Government service dashboards for demand, timeliness, backlog and equity
• NGO monitoring views for outputs, outcomes, reach and data completeness
• Retail dashboards linking sales, margin, inventory and promotional performance
• Operations control charts monitoring defects, capacity, throughput and service levels
• Reporting operating models defining analyst, reviewer, owner and approver roles

Participants can plan an AI-assisted visualisation initiative with controlled rollout and measurable value.

• Use-case prioritisation scoring value, feasibility, risk and data readiness
• Minimum viable dashboard scope defined through priority decisions and users
• Pilot plans specifying datasets, tools, controls, milestones and success measures
• Training needs analysis for analysts, managers, reviewers and data owners
• Change management communications addressing trust, limitations and new responsibilities
• Benefits tracking through report cycle time, adoption and decision quality

Participants can build, review and present a complete AI-assisted visual analysis that supports a defined business decision.

• Select a decision question, audience, dataset and measurable success criteria
• Profile source data and document transformations, limitations and validation checks
• Build chart specifications and dashboards using approved AI-assisted workflows
• Create verified calculations, annotations, narrative findings and decision recommendations
• Conduct peer review using analytical, visual, governance and usability criteria
• Present the completed analysis with assumptions, evidence and implementation actions