Human Resource Analytics Training Course

Participants can define consistent workforce, talent and performance measures for a metric dictionary.

• Metric dictionary fields: definition, formula, source, owner, refresh and exclusions
• Headcount, FTE, turnover, tenure and internal-mobility measures calculated consistently
• Flow metrics reconcile hires, exits, transfers and opening workforce stocks
• Performance distribution metrics distinguish ratings, goals, calibration and completion
• Talent-pipeline measures track readiness, bench strength, critical roles and moves
• Metric owners, refresh frequency, thresholds and decision actions documented

Participants can map HR data sources, keys and relationships needed for reliable analysis.

• HRIS, applicant-tracking, learning, payroll and survey data-source mapping
• Employee identifiers, effective dates and organisational hierarchies aligned across systems
• Star-schema concepts applied to employee, event, position and organisational tables
• Master-data controls for job families, locations, grades and cost centres
• Slowly changing dimensions preserve historical reporting structures and manager relationships
• Data lineage records source systems, transformations, owners and approved uses

Participants can assess and improve the quality of HR data before analysis.

• Completeness, validity, consistency, timeliness and uniqueness quality dimensions assessed
• Missing-value patterns examined before deletion, imputation or exclusion decisions
• Duplicate employee records identified using identifiers, dates and matching rules
• Outlier checks applied to pay, tenure, absence and performance values
• Reconciliation tests compare HR totals with payroll and finance control totals
• Data-quality scorecard assigns defects, business impact, owners and remediation dates

Participants can apply privacy, fairness and governance controls to people analytics work.

• GDPR principles applied to lawful, fair and transparent people-data processing
• Purpose limitation and data minimisation tested against proposed analytical questions
• Role-based access controls protect sensitive employee and candidate information
• Anonymisation, pseudonymisation and aggregation selected for reporting requirements
• Protected-characteristic analysis uses disparate-impact ratios and fairness review procedures
• Ethics review documents benefits, harms, affected groups and escalation routes

Participants can prepare, calculate and validate HR datasets using spreadsheet techniques.

• Structured tables standardise employee data ranges, fields and calculated columns
• XLOOKUP, INDEX-MATCH and Power Query join HR source extracts
• IF, COUNTIFS and SUMIFS calculate workforce measures with explicit rules
• PivotTables segment turnover, absence and performance results by workforce attributes
• Date functions calculate tenure, service bands and rolling period measures
• Audit formulas trace calculation logic, exceptions and source-data dependencies

Participants can use SQL queries to extract, join and aggregate HR data for analysis.

• SELECT, WHERE and ORDER BY retrieve targeted employee-record populations
• INNER JOIN and LEFT JOIN combine employee, job and event tables
• GROUP BY and HAVING calculate workforce metrics across organisational segments
• CASE expressions classify employees into tenure, grade and performance bands
• Window functions calculate rankings, running totals and cohort retention measures
• Query validation checks row counts, join duplication and unexpected null values

Participants can design dashboards that communicate HR findings clearly and responsibly.

• Dashboard purpose defined through audience, decisions, actions and reporting frequency
• Chart selection matches comparisons, trends, distributions, relationships and geographic patterns
• Executive scorecards separate headline measures from diagnostic drill-down indicators
• Control charts distinguish normal variation from material workforce performance changes
• Colour, labels and annotations guide interpretation without distorting reported results
• Power BI dashboard pages organise workforce, talent and performance decision views

Participants can analyse workforce supply, demand and structural risk using planning measures.

• Workforce segmentation combines role criticality, skill scarcity and business contribution
• Span-of-control analysis identifies managerial layers and organisational design signals
• Age, tenure and retirement-risk profiles inform workforce continuity planning
• Demand scenarios translate service volumes, productivity and staffing ratios into requirements
• Supply forecasts incorporate hiring, attrition, mobility and development assumptions
• Gap analysis prioritises build, buy, borrow and automate workforce responses

Participants can assess recruitment funnel performance, source quality and selection outcomes.

• Recruitment funnel metrics calculate conversion, time-to-fill and offer-acceptance rates
• Source-of-hire analysis compares volume, quality, cost and retention outcomes
• Recruiter workload measures assess requisition loads, ageing and process bottlenecks
• Candidate journey analysis identifies stage delays, withdrawals and adverse outcomes
• Quality-of-hire proxy measures combine performance, retention and manager assessments
• Selection validation examines predictor relevance, fairness and post-hire performance evidence

Participants can diagnose voluntary attrition patterns and identify appropriate retention responses.

• Voluntary, involuntary, regrettable and early-tenure attrition definitions separated clearly
• Cohort analysis compares retention by hire period, role and location
• Survival curves estimate employee retention probabilities over periods of service
• Exit-interview coding identifies recurring drivers while recognising response bias
• Segmentation isolates turnover concentration by manager, grade, role and tenure
• Retention actions matched to diagnosed causes, affected populations and ownership

Participants can analyse absence and employee experience data without making unsupported claims.

• Absence rate formulas distinguish calendar days, working days and FTE bases
• Bradford Factor limitations assessed before using absence-frequency escalation thresholds
• Engagement survey scales tested for response rate, reliability and item meaning
• Driver analysis separates association from evidence of employee-experience causation
• Heat maps identify teams requiring manager review rather than automatic intervention
• Comment coding combines thematic categories, sentiment signals and confidentiality controls

Participants can evaluate performance management data for quality, fairness and managerial action.

• Rating distributions examined by function, grade, manager and demographic group
• Calibration analysis detects rating compression, inflation and inconsistent manager patterns
• Goal-setting completion measures distinguish administrative compliance from goal quality
• Performance-versus-potential matrices test talent segmentation assumptions and data limitations
• Rater-effect analysis separates employee performance signals from manager scoring tendencies
• Performance outcome trends connected cautiously with promotion, pay and retention data

Participants can measure pipeline health, internal movement and succession risk for critical roles.

• Critical-role criteria combine business impact, scarcity, risk and replacement difficulty
• Succession coverage calculates ready-now, ready-later and emergency successor depth
• Nine-box data reviewed for calibration quality, movement and demographic representation
• Internal mobility flows track lateral moves, promotions and cross-functional transitions
• Talent-pipeline conversion measures identify blockages between development and appointment
• Bench-strength dashboards highlight single-incumbent roles and successor development needs

Participants can analyse representation, progression and pay patterns using defensible comparison methods.

• Representation measures compare workforce composition across grades, functions and locations
• Hiring, promotion and attrition flow analysis identifies progression differences
• Adverse-impact ratio tests compare selection outcomes between protected groups
• Pay-gap calculations distinguish mean, median, adjusted and unadjusted comparisons
• Regression analysis controls role, grade, tenure and location in pay review
• Inclusion measures interpret survey differences alongside sample size and confidentiality

Participants can build and assess suitable predictive models for defined HR decisions.

• Prediction use cases framed around decisions, actions, timing and intervention capacity
• Training, validation and test datasets prevent overstated model performance claims
• Logistic regression estimates attrition probability using interpretable predictor variables
• Decision trees identify interaction patterns and communicate conditional risk segments
• Precision, recall, ROC-AUC and calibration assess classification-model usefulness
• Feature selection excludes prohibited, unstable or ethically unsuitable employee attributes

Participants can distinguish correlation from causation and evaluate people interventions credibly.

• Correlation, confounding and reverse causality tested before recommending interventions
• Theory of change maps inputs, activities, mechanisms, outcomes and assumptions
• Randomised controlled trials establish counterfactuals where operationally and ethically feasible
• Difference-in-differences compares intervention and comparison-group outcome changes over time
• Propensity-score matching improves comparison groups when random assignment is unavailable
• Evaluation plans define baseline, outcome measures, sample, timing and decision criteria

Participants can present people analysis as a focused decision narrative with accountable recommendations.

• Pyramid Principle structures executive messages from conclusion to supporting evidence
• Decision brief identifies issue, evidence, options, recommendation, owner and timing
• Confidence statements communicate data limitations, uncertainty and material assumptions
• Scenario analysis compares likely workforce outcomes under alternative management actions
• Recommendation registers assign actions, accountabilities, milestones and success measures
• Stakeholder objections anticipated through evidence, alternatives and implementation constraints

Participants can establish reporting routines and governance that turn analysis into sustained action.

• Monthly people-review agendas connect workforce signals with agreed management actions
• RACI matrices clarify analyst, HR, manager, executive and data-owner responsibilities
• Analytics intake process screens requests for value, feasibility, privacy and urgency
• Dashboard adoption measures track usage, decisions, actions and realised outcomes
• Model monitoring checks drift, fairness, accuracy and continued decision relevance
• Benefits tracking links interventions with baseline measures and outcome ownership

Participants can build, review and present a complete people analytics decision pack for a defined workforce or talent issue.

• Business question, stakeholder decision and analytical scope stated precisely
• Metric dictionary and data-lineage summary document measure definitions and sources
• Data-quality findings identify material limitations, remediation actions and residual risks
• Dashboard and analysis combine workforce evidence with clear visual interpretation
• Recommendations specify interventions, owners, milestones, measures and review cadence
• Peer review tests logic, ethics, feasibility and executive presentation quality