Human Resource Analytics Course

Lesson 1 • The HR Analytics Lifecycle

Maps the end-to-end process from question formulation to insight delivery. Provides a repeatable framework students apply in every subsequent chapter.

Lesson 2 • Defining HR Analytics and Its Scope

Establishes what HR analytics is, how it differs from traditional HR reporting, and why it matters. Grounds the chapter by clarifying terminology used throughout the course.

Lesson 3 • Ethics and Data Governance Basics

Introduces ethical principles and governance responsibilities specific to people data. Sets the compliance mindset carried through all analytical work in the course.

Lesson 1 • Assessing and Measuring Data Quality

Defines the six dimensions of data quality and applies them to common HR datasets. Enables students to diagnose quality problems before they distort analysis.

Lesson 2 • Data Cleaning and Preparation Techniques

Covers practical methods for handling missing values, duplicates, and outliers in HR data. Directly enables the analysis work introduced in the next chapter.

Lesson 3 • Data Types and Structures in HR

Distinguishes structured, semi-structured, and unstructured HR data and their analytical implications. Prepares students to choose appropriate tools for each data type.

Lesson 1 • Correlation and Regression Analysis

Introduces correlation coefficients and simple linear regression to quantify relationships between HR variables. Lays the groundwork for predictive modeling in the next chapter.

Lesson 2 • Descriptive Statistics for People Data

Reviews measures of central tendency, dispersion, and distribution shape applied to HR variables. Builds the statistical vocabulary needed for all inferential work ahead.

Lesson 3 • Probability and Sampling Concepts

Explains probability fundamentals and sampling methods relevant to workforce surveys and studies. Ensures students design statistically sound data collection efforts.

Lesson 1 • Benchmarking and External Comparisons

Introduces methods for comparing internal metrics against industry benchmarks and peer organizations. Adds competitive context to descriptive analysis outputs.

Lesson 2 • Building HR Dashboards

Applies visualization principles to construct interactive, role-specific HR dashboards. Reinforces metric framework design by translating it into a live reporting artifact.

Lesson 3 • Designing an HR Metrics Framework

Guides students in selecting metrics aligned to strategic HR objectives rather than measuring everything available. Connects metric selection to stakeholder needs identified in Chapter 1.

Lesson 4 • Core Workforce Metrics

Defines and calculates foundational headcount, turnover, and absence metrics. Provides the baseline measurement vocabulary used in all subsequent analytical work.

Lesson 1 • Introduction to Predictive Modeling

Defines predictive analytics, its HR use cases, and the model-building process. Bridges statistical foundations from Chapter 4 to applied machine learning concepts.

Lesson 2 • Employee Attrition Prediction

Builds logistic regression and decision tree models to predict voluntary turnover risk. Demonstrates the full modeling pipeline on one of HR's highest-value use cases.

Lesson 3 • Talent Supply Modeling

Models internal talent pipelines and external labor supply to identify future gaps. Pairs with demand forecasting to produce a complete workforce gap analysis.

Lesson 4 • Workforce Demand Forecasting

Applies time-series and regression methods to project future headcount and skill requirements. Connects predictive outputs directly to strategic workforce planning processes.

Lesson 1 • Selection Quality and Predictive Hiring

Evaluates the predictive validity of selection tools and links hiring decisions to performance outcomes. Builds evidence-based hiring practices grounded in statistical analysis.

Lesson 2 • Employer Brand and Candidate Experience Analytics

Measures employer brand perception and candidate experience quality using survey and digital data. Extends retention analytics outward to the pre-hire talent relationship.

Lesson 3 • Retention Risk Identification

Uses predictive models and stay interview data to identify flight-risk employees before they resign. Translates attrition models from Chapter 5 into targeted retention interventions.

Lesson 4 • Recruiting Funnel Analytics

Measures conversion rates at each recruiting stage to identify bottlenecks and inefficiencies. Applies descriptive and diagnostic analytics to the talent acquisition process.

Lesson 1 • Analyzing Engagement Survey Results

Applies statistical techniques to identify engagement drivers, segment results, and detect trends. Builds on Chapter 4 statistics to extract meaningful patterns from survey data.

Lesson 2 • Continuous Listening and Sentiment Analysis

Introduces always-on feedback channels and text analytics for monitoring employee sentiment. Extends traditional survey analytics to real-time people intelligence.

Lesson 3 • Linking Engagement to Business Outcomes

Quantifies the relationship between engagement scores and productivity, quality, and financial results. Provides the ROI narrative needed to justify engagement investment to executives.

Lesson 4 • Designing Effective Engagement Surveys

Covers survey design principles, question types, and sampling strategies for measuring engagement. Ensures data collected is valid, reliable, and actionable for HR decisions.

Lesson 1 • Workforce Segmentation and Prioritization

Applies clustering and segmentation techniques to identify critical workforce segments requiring targeted investment. Builds on predictive models to prioritize planning resources.

Lesson 2 • Strategic Workforce Planning Framework

Defines the components of strategic workforce planning and how analytics enables each phase. Synthesizes forecasting, gap analysis, and talent strategy into a unified process.

Lesson 3 • Calculating HR Analytics ROI

Quantifies the financial return of HR analytics investments using cost-benefit and ROI frameworks. Provides the executive-ready financial narrative that secures ongoing analytics funding.

Lesson 4 • Building an Analytics-Driven HR Function

Outlines the capabilities, roles, and governance structures needed to sustain an analytics-driven HR team. Closes the course by translating individual skills into organizational capability.