Business Data Analysis Using Excel Training Course

Participants can prepare imported datasets and identify data-quality issues before analysis.

• Excel Tables establish structured references, consistent formulas and expandable analysis ranges
• Data type checks identify text dates, stored numbers and invalid codes
• TRIM, CLEAN, SUBSTITUTE and VALUE standardise imported text and numeric fields
• Duplicate detection uses composite keys, COUNTIFS and conditional formatting rules
• Missing-value treatment distinguishes blanks, zeros, unknowns and not-applicable records
• Outlier screening applies sorted distributions, IQR thresholds and business rules
• Power Query steps import, profile, transform and document repeatable data preparation

Participants can build auditable Excel calculations for common business analysis requirements.

• Relative, absolute and mixed references control formula copying behaviour across tables
• IF, IFS and SWITCH encode classification rules and exception handling
• SUMIFS, COUNTIFS and AVERAGEIFS calculate segmented operational performance measures
• Date functions derive fiscal periods, ageing bands and working-day intervals
• TEXT, LEFT, RIGHT and MID parse identifiers into analytical dimensions
• Dynamic arrays with FILTER, SORT and UNIQUE create refreshable extracts
• Formula auditing traces precedents, dependents, errors and inconsistent calculations

Participants can use descriptive statistics to assess patterns, variation and unusual results.

• Frequency distributions reveal concentration, missing categories and unusual transaction patterns
• Mean, median and mode distinguish typical values in skewed datasets
• Standard deviation, quartiles and IQR measure spread and identify outliers
• Percentiles and ranking expose service performance relative to peer groups
• Correlation analysis tests association without claiming causation between business measures
• Sampling considerations distinguish population summaries from uncertain sample estimates
• Control charts separate common-cause variation from special-cause operational signals

Participants can reconcile datasets and explain material differences using structured Excel methods.

• XLOOKUP retrieves attributes using exact matches, fallback values and named ranges
• INDEX and MATCH support flexible two-way lookups in legacy workbooks
• Reconciliation checks compare record counts, totals and unmatched key values
• Boolean tests flag exceptions against policy thresholds and expected relationships
• Variance bridges decompose budget-to-actual differences into price, volume and mix
• Waterfall calculations explain changes between opening and closing business measures
• Error handling with IFERROR distinguishes missing data from calculation failures

Participants can create PivotTable reports that support drill-down analysis and management review.

• PivotTables summarise transactions by hierarchy, period, segment and calculated measure
• PivotTable grouping creates months, quarters, ageing bands and numeric ranges
• Slicers and timelines provide controlled filtering for business review discussions
• Show Values As calculates shares, running totals, differences and rankings
• Calculated fields and measures extend PivotTables while preserving source records
• Drill-through checks validate aggregate findings against underlying transaction detail
• PivotChart selection matches comparisons, composition, trend and distribution questions

Participants can design clear Excel dashboards that communicate performance, drivers and exceptions.

• Chart selection aligns visual form with comparison, trend, composition or distribution
• Data-ink principles remove decorative clutter and preserve analytical signal
• Axis scales, labels and reference lines prevent visual misinterpretation
• Conditional formatting highlights exceptions using rules, data bars and icon sets
• Dashboard layouts organise KPI cards, trends, drivers and drill-down views
• Interactive controls connect slicers, PivotTables and charts through report connections
• Accessibility checks improve colour contrast, titles and interpretation for reviewers

Participants can build assumption-based forecasts and test the effects of changing inputs.

• Trendlines and moving averages estimate baseline demand from historical observations
• FORECAST.LINEAR projects values from time-series data and defined assumptions
• Seasonality assessment compares recurring monthly patterns before projecting future periods
• Scenario Manager documents base, upside and downside assumption sets
• Data Tables test one-variable and two-variable sensitivity assumptions
• Goal Seek solves input values required to meet target outcomes
• Forecast accuracy uses absolute error, percentage error and bias measures

Participants can combine recurring data sources and create connected Excel analyses.

• Query folding concepts distinguish source operations from Excel-side transformation steps
• Power Query parameters support controlled file paths, dates and refresh settings
• Append queries combine like-structured monthly files into a single dataset
• Merge queries join reference tables using validated keys and join types
• Star schema design separates fact tables from descriptive dimension tables
• Data Model relationships prevent duplicate counting across linked Excel tables
• Basic DAX measures calculate filtered totals, ratios and period comparisons

Participants can present analysis credibly and plan controlled application in their workplace.

• Executive summaries state finding, business implication, evidence and recommended action
• Claim-evidence reasoning distinguishes observed facts, assumptions and interpretation
• Management-ready workbooks separate raw data, calculations, outputs and documentation
• Quality assurance checklists test refreshes, formulas, filters and chart labels
• Peer review identifies ambiguous metrics, unsupported claims and presentation risks
• Action plans assign decision owner, next step, date and evidence
• Workbook handover notes record sources, assumptions, refresh steps and limitations

You should already be comfortable with worksheets, basic formulas, sorting and filtering. You do not need programming experience, advanced statistics knowledge or prior experience with Power Query, PivotTables or DAX.

A laptop with a recent desktop version of Microsoft Excel is recommended, preferably Microsoft 365, as some functions and features are version-dependent. Course datasets and working materials are provided, and you may bring a non-confidential workplace reporting example for application planning.

The course suits analysts, managers, reporting specialists, finance professionals, project staff and risk personnel who work with business data in Excel. It is designed for people who need to produce reliable analysis rather than simply format spreadsheets.

This course concentrates on the full business analysis process in Excel: defining questions, preparing data, calculating metrics, testing results and communicating conclusions. Advanced Excel courses may focus more heavily on functions or automation, while Power BI courses concentrate on enterprise data models and interactive reporting.

Participants leave with templates for data checks, metric definitions, dashboards, forecasting and workbook handovers. The final module includes an application plan that identifies a suitable workplace dataset, decision question, data sources and quality controls.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours, with breaks and lunch scheduled separately. The five days progress from data preparation and formulas through analysis, reporting, forecasting, data modelling and workplace application.