HR Analytics in MS Excel for personnel management

Course overview

Description generated based on course syllabus and open data.

HR Analytics and MS Excel for personnel management: what is covered

The materials focus on workforce analytics (people analytics) in MS Excel: data collection and cleaning (Power Query), data modeling, HR KPI calculations (turnover, hiring, time-to-fill, retention, absenteeism), dashboard building (PivotTables, charts, slicers), and basic ML approaches (regression and classification for HR forecasting) using built‑in Excel tools.

HR Analytics in Excel: who it suits and who it does not

Suitable for:

• HR specialists and HRBPs working with personnel management and metrics.
• Workforce analysts and recruiters who build reports and dashboards in MS Excel.
• People operations managers needing people analytics without coding.

Not suitable for:

• Projects requiring advanced ML and big data pipelines (prefer Python/R/BI).
• Teams that do not use Excel or restrict macros/add‑ins by policy.

Problem → outcome in workforce analytics

• Scattered HR data → a single data model in Excel (tables, relationships, Power Query).
• Manual KPI math → reproducible formulas and metric templates.
• Static reports → interactive dashboards with filters, slicers, and trend visuals.
• Hidden drivers of turnover → correlations, regression, and simple risk models.
• Hard data reconciliation → standardized lookups and quality control (data validation).

Comparison with people analytics alternatives

• MS Excel for HR Analytics: quick start, transparent formulas, familiar to HR teams; limited in large‑scale automation.
• BI tools (Power BI, Tableau): richer visuals and refresh; require setup and access governance.
• Python/R: advanced ML and scalability; higher learning curve.
• Manual reports: minimal setup; low reproducibility and higher error risk.

Covered outcomes from working through HR Analytics in MS Excel

• Standardized HR KPI templates and guidelines for interpretation.
• Interactive workforce dashboards using PivotTables and modern charts.
• Examples of basic ML models in Excel (regression/classification) for HR forecasts.
• Data preparation practices: cleaning, normalization, validation, quality control.
• Transparent documentation of people analytics for stakeholders.