Objectives

Upon completion of this course, participants will be able to:

• **Understand** the foundational concepts of data science and how they apply to modern HR challenges.
• **Identify** and gather relevant HR data points for strategic analysis and predictive modeling.
• **Apply** statistical methods to analyze workforce data and derive meaningful insights into talent acquisition, retention, and performance.
• **Develop** predictive models to forecast future HR trends, such as turnover risk and skill gaps.
• **Translate** complex data findings into clear, actionable, and persuasive business recommendations for executive leadership.
• **Design** and implement an HR analytics roadmap to foster a data-driven culture within the HR function.
• **Evaluate** the ethical considerations and data privacy challenges inherent in using advanced HR analytics.
• **Measure** the return on investment (ROI) of HR programs using quantitative data and metrics.

Target Audience

• HR Directors and VPs looking to transition their function to a strategic, data-driven model.
• HR Business Partners (HRBPs) seeking to use data for more effective consulting and decision-making.
• HR Analysts and Specialists interested in advanced statistical and predictive modeling techniques.
• Talent Acquisition and Retention Managers focused on optimizing strategies with quantitative insights.
• Business Intelligence (BI) professionals who want to apply their skills specifically within the HR domain.

Case studies, Hands-on data analysis exercises (using dummy data and tools like Excel/R/Python notebooks), Group problem-solving activities, Expert lectures, Interactive discussions on ethical dilemmas, Presenting data insights.

Outline

Unit 1: The Foundations of HR Data Science

Introduction to People Analytics

• Defining HR Data Science and its strategic value.
• The evolution from traditional HR metrics to predictive analytics.
• Identifying key HR business questions suitable for data science.
• Understanding the maturity model for HR analytics capabilities.

Data Sourcing and Quality

• Sources of HR data: HRIS, performance, engagement, etc.
• Data cleaning, normalization, and preparation techniques.
• Ensuring data integrity and reliability for accurate analysis.
• Overview of relevant data science tools and platforms.

Unit 2: Descriptive and Diagnostic Analytics

Core Descriptive Statistics in HR

• Calculating and interpreting central tendency and dispersion.
• Using visual analytics to explore workforce data patterns.
• Benchmarking HR metrics internally and externally.
• Creating effective HR dashboards and scorecards.

Root Cause Analysis and Diagnosis

• Techniques for diagnosing the 'why' behind HR trends.
• Analyzing correlation and causation in employee data.
• Deep-dive on turnover and engagement drivers.
• Using statistical tests to validate hypotheses about HR outcomes.

Unit 3: Predictive Modeling in Talent Management

Predicting Employee Turnover

• Building and interpreting a basic employee flight risk model.
• Identifying key features that predict voluntary separation.
• Evaluating model performance metrics (e.g., precision, recall).
• Developing targeted retention strategies based on model output.

Forecasting Workforce Needs

• Techniques for predicting future talent supply and demand.
• Modeling the impact of business growth on HR capacity.
• Using time-series analysis for resource planning.
• Integrating predictive models into the Strategic Workforce Plan.

Unit 4: Prescriptive Analytics and Ethical AI

Optimization and Scenario Planning

• Introduction to prescriptive analytics: recommending actions.
• Using optimization models for compensation and deployment.
• Creating 'what-if' scenarios to test strategic decisions.
• Automating HR decisions using data science principles.

Ethical and Privacy Considerations

• Understanding bias in HR algorithms and data sets.
• Mitigation strategies for ensuring fairness and equity in AI-driven HR.
• Data privacy laws (e.g., GDPR) and best practices for HR data.
• Establishing ethical governance for HR data science projects.

Unit 5: Building a Data-Driven HR Function

Communicating Insights to Stakeholders

• Storytelling with data: transforming analysis into narrative.
• Developing compelling visualizations and presentations.
• Tailoring data communication to different business audiences.
• Measuring the impact and ROI of HR analytics initiatives.

Implementation and Continuous Improvement

• Defining the HR analytics team structure and required capabilities.
• Developing an HR data science roadmap and governance framework.
• Strategies for integrating data science into daily HR operations.
• Fostering a culture of data literacy and curiosity in HR.