Business Analytics Using Tableau Training Course

Participants can connect Tableau to business data and select structures that preserve intended analytical meaning.

• Connect to Excel, CSV, relational databases and published data sources
• Live connections and extracts selected using freshness, performance and governance criteria
• Joins, unions and relationships matched to intended analytical grain
• Data Interpreter and field renaming used to standardise imported tables
• Custom SQL risks assessed before introducing database-side transformations
• Extract filters and aggregation configured to reduce workbook processing load
• Validate record counts, nulls and key uniqueness before visualisation

Participants can build repeatable Tableau Prep flows that produce documented, analysis-ready outputs.

• Tableau Prep flow stages used to clean, pivot, split and aggregate data
• Profile pane distributions used to identify outliers, nulls and invalid categories
• Calculated cleaning steps standardise dates, labels and numeric measures
• Pivoted monthly columns converted into analysis-ready transactional records
• Joins in Tableau Prep reconciled using matched, unmatched and duplicate records
• Output extracts documented with refresh assumptions and source ownership
• Data quality rules defined for completeness, validity, consistency and timeliness

Participants can create controlled calculations for common business measures and interactive analysis.

• Row-level calculated fields create margins, bands, flags and date parts
• IF, CASE, IIF and NULL handling applied to business rules
• Date functions calculate year-to-date, rolling periods and prior-period comparisons
• Table calculations compute running totals, moving averages and percent differences
• Quick table calculations inspected for addressing and partitioning errors
• Parameters drive scenario assumptions, metric selection and user-controlled thresholds
• Calculation naming and comments support reuse, audit and peer review

Participants can control aggregation, build meaningful segments and interpret results in their proper context.

• FIXED, INCLUDE and EXCLUDE expressions control aggregation independently of view detail
• Order-level profitability calculated despite line-level transaction records and returns
• Customer cohorts derived using first purchase dates and fixed expressions
• Context filters distinguished from dimension filters through Tableau order of operations
• Sets, groups and bins segment customers, products and operational performance
• Reference lines, bands and distributions contextualise targets and variation
• Aggregation choices tested against Simpson's paradox and denominator changes

Participants can select and design charts that communicate findings accurately and accessibly.

• Chart selection matches comparison, composition, distribution, relationship and geographic questions
• Bar charts, line charts and scatter plots designed for accurate reading
• Dual-axis charts and synchronised scales used only when interpretation remains clear
• Colour, preattentive attributes and visual hierarchy direct attention to evidence
• Accessible palettes and labels support colour-vision differences and screen readability
• Maps use geographic roles, filled maps and symbol maps appropriately
• Reference annotations explain exceptions, turning points and decision implications

Participants can construct responsive dashboards that support defined user tasks and decisions.

• Dashboard layout uses containers, device layouts and consistent visual hierarchy
• Actions filter, highlight, open sheets and pass parameters between connected views
• Interactive controls restrict choices without obscuring metric definitions or context
• Dashboard performance recorder identifies slow queries, calculations and rendering bottlenecks
• Design for executive, operational and analytical audiences using task-based views
• Tooltips, titles and captions communicate definitions, caveats and recommended actions
• Mobile layouts prioritise essential metrics, filters and readable touch targets

Participants can investigate performance drivers and communicate analytical findings without overstating evidence.

• Descriptive, diagnostic, predictive and prescriptive questions distinguished before chart construction
• Trend, variance and contribution analysis isolate material performance drivers
• Percent-of-total and contribution calculations identify concentration and Pareto patterns
• Forecasting options assessed using seasonality, trend, outliers and confidence intervals
• Tableau trend lines interpreted with R-squared, residuals and model assumptions
• Clustering segments observations using selected variables and business interpretability
• Correlation findings separated from causation claims and unsupported recommendations

Participants can publish Tableau content with controls for trust, access, maintenance and performance.

• Published data sources centralise definitions, calculations, permissions and refresh management
• Tableau Cloud projects organised by audience, sensitivity and content lifecycle
• Role-based permissions apply least privilege to workbooks, data sources and projects
• Data source certification and warnings signal approved content and known limitations
• Refresh schedules, extract failures and subscriptions monitored through operational ownership
• Workbook documentation records metric logic, source lineage and refresh frequency
• Peer review checklist tests accuracy, usability, performance and data confidentiality

Participants can define, prioritise and plan a Tableau deliverable that addresses a real reporting requirement.

• Stakeholder interview questions establish decisions, users, metrics and action thresholds
• KPI definitions specify formula, grain, owner, target, cadence and caveats
• Requirements prioritised using MoSCoW, effort estimates and decision value
• Dashboard acceptance criteria cover accuracy, usability, performance and access
• Release plan sequences prototype feedback, validation, publication and adoption support
• Personal application plan identifies a Tableau use case and first deliverable
• Post-publication review measures usage, decision impact and improvement requests