HR Data Analytics Course

Lesson 1 • What HR Analytics Is and Why It Matters

Defines HR analytics and distinguishes it from traditional HR reporting. Establishes the strategic rationale that motivates the entire course.

Lesson 2 • Types of HR Data

Categorises structured, unstructured, and semi-structured HR data sources. Learners identify which data types are available in their own organisations.

Lesson 3 • Ethical and Privacy Principles in HR Analytics

Covers data privacy obligations, bias risks, and ethical use of employee data. Establishes responsible practices that apply throughout the course.

Lesson 4 • Key HR Metrics and KPIs

Defines essential HR metrics across talent acquisition, retention, and employee performance. Connects metric selection to specific business questions.

Lesson 1 • Surveys and Primary Data Collection

Teaches survey design, pulse checks, and exit interview protocols for collecting primary HR data. Connects survey methodology to data reliability.

Lesson 2 • Data Storage and Security Fundamentals

Covers on-premise vs. cloud storage options and access control principles for sensitive HR data. Prepares learners to make secure infrastructure decisions.

Lesson 3 • Data Governance and Quality Management

Establishes governance frameworks, data dictionaries, and quality controls for HR datasets. Ensures learners can maintain trustworthy data over time.

Lesson 4 • HR Information Systems Overview

Surveys core HRIS, ATS, and LMS platforms as primary data sources. Learners understand how system architecture affects data availability and quality.

Lesson 1 • Feature Engineering for HR Analytics

Introduces the creation of new analytical variables from raw HR fields to improve model performance. Learners build features such as tenure bands and risk scores.

Lesson 2 • Transforming and Reshaping HR Datasets

Teaches pivoting, merging, and aggregating HR tables to create analysis-ready structures. Directly enables the statistical and visualisation work in later chapters.

Lesson 3 • Reproducible Data Preparation Workflows

Establishes practices for scripting, documenting, and versioning data preparation steps. Ensures cleaning work is auditable and repeatable across projects.

Lesson 4 • Handling Missing and Inconsistent Data

Covers imputation strategies, deletion rules, and standardisation techniques for common HR data problems. Learners choose appropriate remedies based on data context.

Lesson 1 • Correlation and Root Cause Analysis

Introduces correlation coefficients and fishbone diagrams to identify drivers of HR outcomes. Learners distinguish correlation from causation in workforce data.

Lesson 2 • Turnover and Attrition Analysis

Applies descriptive and diagnostic methods specifically to employee turnover data. Learners calculate voluntary vs. involuntary attrition and identify high-risk groups.

Lesson 3 • Workforce Segmentation and Cohort Analysis

Teaches grouping employees by demographics, role, or tenure to reveal within-group patterns. Enables targeted HR interventions based on segment-level insights.

Lesson 4 • Remuneration and Pay Equity Analysis

Uses descriptive statistics to audit pay distributions and detect potential equity gaps. Connects findings to compliance obligations and business fairness goals.

Lesson 1 • Data Storytelling for HR Audiences

Teaches narrative structure, annotation, and audience-tailored messaging for HR analytics presentations. Learners craft a complete data story from insight to recommendation.

Lesson 2 • Core Chart Types for HR Data

Demonstrates bar, line, scatter, and heatmap charts using real HR datasets. Learners match each chart type to the specific HR question it answers best.

Lesson 3 • Accessibility and Inclusive Visualisation Design

Addresses colour blindness, screen reader compatibility, and plain-language labelling in HR charts. Ensures visualisations are usable by all stakeholders.

Lesson 4 • Principles of Effective Data Visualisation

Covers Gestalt principles, pre-attentive attributes, and chart selection frameworks for HR data. Establishes the design foundation for all visualisation work in this chapter.

Lesson 1 • Regression Models for HR Outcomes

Covers linear and logistic regression applied to HR variables such as performance scores and attrition probability. Learners interpret coefficients in business terms.

Lesson 2 • Workforce Demand Forecasting

Uses time-series methods and scenario planning to project future headcount needs. Connects forecasting outputs to strategic workforce planning decisions.

Lesson 3 • Classification Models for Attrition Prediction

Applies decision trees, random forests, and gradient boosting to predict employee flight risk. Learners compare model accuracy and select the best performer.

Lesson 4 • Foundations of Predictive Modeling in HR

Introduces supervised learning concepts, train-test splits, and model evaluation metrics in an HR context. Prepares learners for hands-on model building in subsequent sections.

Lesson 1 • Linking Workforce Data to Business Strategy

Translates organisational strategic goals into measurable workforce requirements using analytics. Establishes the strategic context that gives workforce planning its direction.

Lesson 2 • Supply and Demand Workforce Analysis

Models internal talent supply through attrition and promotion flows alongside external demand signals. Learners identify gaps between projected supply and future demand.

Lesson 3 • Scenario Planning and Workforce Simulation

Applies Monte Carlo simulation and what-if modeling to test workforce plan resilience. Learners present scenario outputs to senior stakeholders as decision support.

Lesson 4 • Talent Pipeline and Succession Analytics

Uses readiness scores and bench strength metrics to evaluate succession pipelines. Learners quantify pipeline health and recommend targeted development actions.

Lesson 1 • Continuous Improvement Through Analytics Feedback

Establishes feedback loops that use ongoing data to refine HR programmes iteratively. Connects evaluation findings to the strategic workforce planning cycle.

Lesson 2 • Evaluation Frameworks for HR Programmes

Introduces Kirkpatrick, Phillips ROI, and logic model frameworks for structuring HR programme evaluation. Learners select the appropriate framework for different programme types.

Lesson 3 • Experimental and Quasi-Experimental Designs

Covers randomised controlled trials, pre-post designs, and difference-in-differences for HR evaluation. Learners assess internal validity threats in each design.

Lesson 4 • Calculating HR Programme ROI

Applies cost-benefit analysis and ROI formulas to training, wellness, and engagement programmes. Learners isolate programme effects from confounding organisational factors.