Enroll in HR Analytics

Core HR Functions and the Employee Lifecycle

Explore recruitment, onboarding, development, performance, and exit as stages of the employee lifecycle. Identify the questions HR teams ask at each stage.

From Reporting to Analytics: The HR Analytics Maturity Model

Learn the difference between descriptive, diagnostic, predictive, and prescriptive analytics in an HR setting. Assess where real organizations sit on the maturity curve.

HRIS, ATS, and Other HR Systems

Tour the common systems that store people data, including HRIS, applicant tracking, payroll, and learning platforms. Understand what each system captures and how they relate.

Data Types, Structures, and Key HR Datasets

Review categorical, numerical, and time-based data, and examine typical HR tables such as headcount, hires, and exits. Practice reading a data dictionary.

Cleaning and Validating HR Data

Find and fix missing values, duplicates, inconsistent labels, and date errors in a sample HR dataset. Build a repeatable data checklist.

Privacy, Ethics, and Legal Considerations

Discuss confidentiality, consent, anonymization, and regulations such as GDPR as they apply to employee data. Evaluate ethical dilemmas in people analytics.

Cleaning and Structuring HR Data in Excel

Learners prepare raw HRIS exports by removing duplicates, fixing formats, and standardizing fields such as job titles and departments. They practice using text functions, data validation, and Power Query basics.

Lookup and Logical Functions for Employee Records

Learners combine employee, payroll, and department tables using XLOOKUP, INDEX-MATCH, and IF logic. They learn to build reliable formulas for tenure bands, eligibility flags, and status checks.

SQL Basics: Selecting and Filtering Employee Data

Learners are introduced to relational HR database structures and write SELECT queries with WHERE, ORDER BY, and LIMIT. They practice pulling targeted employee lists while respecting data privacy.

Aggregations and Grouping for HR Metrics

Learners use COUNT, SUM, AVG, GROUP BY, and HAVING to calculate metrics such as average salary, headcount by department, and absence rates. They learn to validate results against Excel summaries.

Designing an HR Reporting Dashboard in Excel

Learners build an interactive dashboard with charts, slicers, and KPI tiles covering headcount, turnover, and hiring. They apply layout and visual design principles for non-technical audiences.

Combining SQL Outputs with Excel Reports

Learners connect Excel to SQL query results and refresh reports without manual copy-paste. They practice parameterized queries and scheduled updates for monthly reporting cycles.

Getting Started with Power BI Desktop

Tour the Power BI interface and import HR datasets from Excel files and SQL databases. Learn how reports, pages and fields fit together.

Cleaning and Shaping HR Data with Power Query

Use Power Query to fix data types, remove duplicates, handle missing values and merge employee, payroll and attendance tables. Build repeatable cleaning steps for monthly refreshes.

DAX Essentials for HR Measures

Learn the difference between calculated columns and measures, and write basic aggregations such as headcount, average tenure and total compensation cost.

Attrition, Turnover and Retention Measures

Build measures for attrition rate, voluntary versus involuntary turnover and retention using CALCULATE and filter context. Validate results against known figures.

Choosing Visuals and Designing for Clarity

Select the right chart for each HR question and apply layout, colour and labelling principles. Avoid cluttered or misleading visuals.

Interactivity with Slicers, Drill-Through and Tooltips

Add slicers, filters, drill-through pages, bookmarks and tooltips so users can explore workforce data by department, location and tenure band.

Python and Pandas Essentials for HR Data

Set up a notebook environment and use Pandas to import, filter, group, and summarise employee records. Practise translating familiar Excel and SQL tasks into Python.

Cleaning and Transforming Workforce Data

Handle missing values, duplicates, inconsistent categories, and outliers in HRIS exports. Create new fields such as tenure bands and promotion gaps.

Employee Attrition Prediction with Logistic Regression

Prepare features, split data, and build a logistic regression model to estimate the likelihood of employee exits. Interpret coefficients in plain business language.

Tree-Based Models for Performance and Promotion Insights

Train decision trees and random forests to predict performance outcomes and identify key drivers. Compare feature importance across models.

Ethics, Bias, and Privacy in People Analytics

Examine how bias enters HR data and models, and test predictions for fairness across groups. Review data protection principles and transparency expectations.

Turning Predictions into HR Action Plans

Convert model outputs into retention strategies, risk segments, and cost-benefit cases. Practise presenting findings and limitations to HR leaders and executives.