HR Data Analytics Training Course

Participants can map source systems and define a usable HR data structure.

• HRIS, applicant tracking, learning and performance system data sources
• Employee master data fields, keys, effective dates and record grain
• Fact tables and dimension tables for workforce reporting models
• Unique employee identifiers matched across fragmented HR source systems
• Data dictionaries documenting fields, definitions, owners and refresh frequency
• Source-to-report lineage for headcount, turnover and recruitment metrics

Participants can assess HR data quality and prepare analysis-ready datasets.

• Data profiling checks for completeness, uniqueness, validity and consistency
• Missing-value patterns assessed before deletion, imputation or exclusion decisions
• Duplicate employee records identified through keys, dates and matching rules
• Standardising job titles, departments, locations and employment status values
• Outlier detection using ranges, percentiles, z-scores and business rules
• Reconciliation checks between HR dashboards, payroll totals and HRIS extracts
• Audit trails recording transformations, assumptions, exceptions and approval decisions

Participants can use spreadsheet techniques to analyse workforce data accurately.

• Excel Tables structure employee data for reliable formulas and refreshes
• XLOOKUP and INDEX-MATCH join employee attributes across data files
• PivotTables summarise headcount, hires, exits and workforce composition
• COUNTIFS and SUMIFS calculate segmented workforce measures accurately
• Date functions calculate tenure, time-to-fill and rolling reporting periods
• Power Query imports, cleans and combines recurring HR data extracts
• Conditional formatting flags missing values, threshold breaches and unusual patterns

Participants can define and calculate workforce measures with consistent denominators.

• Point-in-time headcount distinguished from full-time equivalent workforce measures
• Average headcount calculated using opening, closing and periodic population counts
• Joiner, leaver and internal mobility rates with explicit denominator rules
• Voluntary and involuntary turnover separated using consistent exit classifications
• Retention cohorts tracked by hire month, business unit and employee segment
• Span of control and manager ratio calculated for organisation design
• Workforce composition segmented by grade, contract type, location and tenure

Participants can analyse recruitment pipeline performance and hiring outcomes.

• Recruitment funnel conversion rates calculated from application through accepted offer
• Time-to-fill and time-to-hire measured from clearly defined start dates
• Source-of-hire analysis compares volume, conversion, cost and retention
• Candidate drop-off diagnosed by stage, role family and recruitment channel
• Recruiter workload assessed through requisition volume, ageing and closure rates
• Quality-of-hire proxy measures combine performance, retention and hiring-manager feedback
• Recruitment dashboards distinguish pipeline activity from completed hiring outcomes

Participants can identify retention patterns and prioritise areas for investigation.

• Turnover rates segmented by tenure, grade, manager, location and role
• Survival curves show employee retention patterns over successive employment periods
• Cohort analysis compares retention for hiring groups across comparable periods
• Regretted attrition classifications separated from routine employee movement categories
• Exit interview themes coded into structured categories for trend analysis
• Retention risk indicators prioritised without treating correlation as causation
• Cost-of-turnover estimates state assumptions for vacancy, hiring and onboarding costs

Participants can analyse absence patterns while protecting sensitive employee information.

• Absence rate calculated using available workdays and lost workdays
• Frequency and duration measures distinguish repeated short-term absence patterns
• Bradford Factor limitations considered before any absence management use
• Seasonal absence trends analysed using monthly, weekly and cohort comparisons
• Workforce capacity estimates combine headcount, schedules, absence and vacancy data
• Sensitive health-related data restricted through purpose, access and retention controls
• Absence findings framed for support interventions rather than individual surveillance

Participants can evaluate performance data quality and rating distribution patterns.

• Performance rating distributions compared across functions, grades and manager groups
• Calibration analysis identifies inconsistent ratings after workforce composition adjustments
• Rating inflation and compression detected through distributional comparison methods
• Goal completion measures separated from performance outcomes and business results
• Manager effects investigated using team size, tenure and rating patterns
• Performance-potential matrices assessed for definition consistency and decision use
• Missing review completion records analysed by population, cycle and manager

Participants can assess learning participation, completion and business relevance.

• Learning participation rates segmented by role, grade, location and manager
• Completion data separated from demonstrated capability and workplace application measures
• Kirkpatrick model distinguishes reaction, learning, behaviour and results evidence
• Pre- and post-assessment comparisons account for baseline score differences
• Talent programme cohorts tracked for promotion, retention and mobility outcomes
• Skills inventories mapped against role requirements and strategic capability gaps
• Learning dashboards identify access gaps, completion risks and critical populations

Participants can interpret employee listening data and identify action priorities.

• Survey response rates assessed for coverage, nonresponse and confidentiality risks
• Likert-scale results summarised using distributions, favourable scores and benchmarks
• Driver analysis distinguishes correlation findings from proven intervention effects
• Open-text comments coded through themes, sentiment and recurring employee concerns
• Small-group suppression rules protect confidentiality in survey reporting outputs
• Heatmaps compare engagement patterns across functions, locations and employee groups
• Action planning links survey findings to accountable owners and follow-up measures

Participants can analyse representation and progression patterns using appropriate comparisons.

• Representation measures compare workforce composition with relevant labour-market availability
• Hiring, promotion and turnover rates disaggregated by protected characteristic groups
• Adverse impact ratio calculated for selection process monitoring
• Four-fifths rule applied cautiously alongside statistical and legal review
• Pay gap measures distinguish mean, median and like-for-like comparisons
• Intersectional analysis balances insight value against small-group disclosure risks
• DEI dashboards document definitions, comparison groups and interpretation limitations

Participants can summarise HR data and select visualisations that support decisions.

• Mean, median, mode and percentiles selected for appropriate workforce distributions
• Standard deviation and interquartile range describe variation in HR measures
• Distribution shape examined using histograms, boxplots and density charts
• Rates, counts and percentages labelled with consistent comparison denominators
• Time-series charts distinguish trends, seasonality, events and reporting artefacts
• Chart selection matches the decision question, audience and data structure
• Annotation highlights material findings, thresholds, uncertainty and recommended actions

Participants can test HR data differences and state findings with appropriate caution.

• Sampling concepts distinguish population measures from survey-based statistical estimates
• Confidence intervals express uncertainty around rates, averages and group differences
• T-tests compare average outcomes between two defined employee groups
• Chi-square tests assess association between categorical HR variables
• ANOVA compares average outcomes across several workforce groups
• P-values interpreted alongside effect sizes, sample sizes and business materiality
• Multiple comparisons controlled to reduce false-positive findings in segment analysis

Participants can interpret common predictive methods and evaluate model usefulness.

• Correlation coefficients measure linear association without proving causal relationships
• Scatterplots inspect relationship patterns, outliers and potential nonlinearity
• Multiple regression estimates associations while controlling for selected explanatory variables
• Logistic regression models binary outcomes such as voluntary employee exit
• Train-test splits assess model performance on unseen employee records
• Precision, recall and confusion matrices evaluate classification model results
• Model explainability requirements documented before workforce decisions use predictions

Participants can build evidence-based workforce demand and supply projections.

• Workforce planning connects strategic priorities, operating plans and talent demand
• Demand forecasts use workload drivers, productivity assumptions and staffing ratios
• Supply projections account for headcount, attrition, promotions and planned hires
• Scenario analysis compares baseline, growth, constraint and disruption assumptions
• Gap analysis identifies shortages, surpluses and critical role exposures
• Rolling forecasts updated using actual movements and revised business assumptions
• Workforce plans specify interventions, owners, milestones and outcome measures

Participants can create decision-focused HR dashboards in Power BI.

• Power BI data model connects workforce tables through defined relationships
• Star schemas separate employee events, dimensions and reporting measures
• DAX measures calculate headcount, turnover and period comparisons dynamically
• Date tables support monthly trends, year-to-date totals and prior-period analysis
• Slicers and drill-through pages support controlled investigation by user groups
• Dashboard wireframes prioritise decisions, exceptions, trends and accountable actions
• Row-level security restricts sensitive views by manager, function or geography

Participants can apply governance controls to responsible HR analytics work.

• Purpose limitation defines permitted uses for employee data analysis
• Data minimisation removes fields unnecessary for the stated analytical question
• Role-based access controls restrict sensitive workforce data to authorised users
• Retention schedules specify disposal requirements for extracts and analytical outputs
• Re-identification risk assessed before sharing aggregated employee data externally
• Bias audits test data, measures, model outputs and decision consequences
• ISO 30414 metrics considered alongside local reporting and disclosure obligations

Participants can communicate evidence, limitations and recommended actions to decision-makers.

• Executive storylines state the decision, evidence, implication and recommended action
• Pyramid Principle structures findings from headline conclusion to supporting evidence
• Analytical assumptions disclosed using concise methods notes and data caveats
• Recommendations distinguish immediate actions, investigations and longer-term capability investments
• Risk registers capture privacy, bias, implementation and stakeholder acceptance issues
• Management questions anticipated through evidence appendices and metric definitions
• Success measures define how actions will be monitored after implementation

Participants can build, review and present a complete HR analytics project for a defined workforce decision.

• Business question scoped with sponsor, decision owner and measurable outcome
• Data preparation log records sources, transformations, exclusions and quality checks
• Metric pack defines formulas, denominators, segments and reporting periods
• Analysis applies descriptive, diagnostic or predictive methods appropriate to evidence
• Power BI dashboard presents trends, exceptions, drivers and decision prompts
• Recommendations prioritise actions, owners, risks, costs and success measures
• Peer review tests analytical validity, clarity, ethics and implementation feasibility
• Final presentation communicates findings, limitations and decision-ready recommendations