Why should I take HR Analytics Certification? Analytics involves data collection, processing, analysis and interpretation for better decision making and gain insights. Increased competition has put more focus on optimizing business processes and make informed business decisions. Analytics provide informed insights for effective decision making by data collation processing and analysis.
HR analytics has emerged as major contributor for understanding the effectiveness of HR processes and factors affecting employees motivation, productivity and performance.
After completing the course you will be able to understand
Basics of Analytics and statistics Using MS-Excel for analytics Employee Retention Recruitment and Talent Management Analytics Learning, Training and Development Analytics Attrition Prediction HR Balanced Scorecard
How will I benefit from HR Analytics Certification? The course is intended for HR professionals, managers, HR consultants and graduates wanting to excel in HR analytics. It is also well suited for those who are already working and would like to take certification for further career progression.
Earning Vskills Certified HR Analytics Professional Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.
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TABLE OF CONTENT Chapter 1. Analytics Introduction Evolution of HR Analytics & People Analytics: From Reporting to Predictive Data Management and Types Descriptive, Diagnostic, Predictive & Prescriptive Analytics Framework Problem Solving and Decision Making HR Analytics Maturity Model (Levels 1–4: Operational Reporting → Predictive) Business Case for HR Analytics: ROI and Stakeholder Buy-in HRIS & HR Tech Ecosystem Overview (SAP SuccessFactors, Workday, Darwinbox) Chapter 2. Using Spreadsheets for Analytics Excel Formulas Excel Functions Excel Add-Ins for Analytics: Power Query, Analysis ToolPak, Solver What is Spreadsheet Modeling Key HR Functions in Excel: VLOOKUP/XLOOKUP, COUNTIFS, AVERAGEIFS, IFERROR Introduction to Power BI / Tableau for HR Dashboards Introduction to Python / R for HR Analytics (optional/advanced track) Chapter 3. Visualizing Data in Spreadsheets Excel Data Visualization Tools Data Queries in Excel Data Summarization in Excel PivotTables and Pivot Charts HR-Specific Chart Types: Headcount Waterfall, Attrition Funnel, Tenure Histogram Data Storytelling: Designing Insights for HR Leadership & Board Presentations Chapter 4. Descriptive Statistical Measures Measures of Location (Mean, Median, Mode) Measures of Dispersion (Variance, Std Dev, Range) Skewness/Kurtosis overly theoretical for target audience; remove or make supplementary Measures of Association (Correlation) Frequency Distributions Excel Descriptive Statistics Tools Applying Descriptive Stats to HR Data: Salary Bands, Age/Tenure Distribution Chapter 5. Probability Distributions Probability Basics
Discrete Probability Distributions (e.g., modelling offer acceptance rates, headcount scenarios)
Continuous Probability Distributions (e.g., time-to-fill, performance score distributions
Distribution Fitting
Chapter 6. Sampling and Estimation Sampling Methods Sampling Distributions Estimation & Confidence Intervals Employee Survey Sampling: Sample Size Calculation for Engagement Surveys Chapter 7. Statistical Inference Hypothesis Testing One-Sample Hypothesis Tests Two-Sample Hypothesis Tests ANOVA Chi-Square Tests for HR: Testing Pay Equity, Promotion Parity by Gender/Grade A/B Testing in HR: Pilot Programs, Recruitment Campaign Testing Chapter 8. Regression Analysis Simple Linear Regression Residual Analysis and Regression Assumptions Multiple Linear Regression Regression with Categorical Independent Variables Nonlinear & Logistic Regression Regression for HR: Predicting Salary, Performance Scores, Time-to-Fill Multicollinearity, Overfitting & Model Validation in HR Contexts Chapter 9. HR Management Functions of Human Resource Management Strategic HRM: Aligning People Strategy with Business Goals Strategic Planning and Human Resource Practices HR Operating Models: HR Business Partner, Centre of Excellence, Shared Services Chapter 10. HR Analytics HR Analytics Basics Talent Life Cycle HR Metrics (Time-to-Fill, Cost-per-Hire, Turnover Rate, Absenteeism Rate, Revenue-per-Employee, eNPS, Offer Acceptance Rate) HR Data Collection (HRIS data, survey tools (Qualtrics, Culture Amp), ATS data, payroll data, LMS data HR Data Analysis (a) Workforce Segmentation Analysis and (b) Trend & Cohort Analysis Data Quality in HR: Missing Data, Inconsistent Records, Master Data Governance Workforce Planning Analytics: Headcount Forecasting, Supply-Demand Modelling Chapter 11. Recruitment and Talent Management Analytics Recruitment and Talent Acquisition Workforce Capability Job Analysis and Design Compensation Management Performance Appraisal, Planning and Execution Rewards and Recognition Attrition & Churn Analysis: Voluntary vs Involuntary, Regrettable vs Non-Regrettable Talent Development Career Progression Employee Retention Employee Engagement Recruitment Funnel Analytics: Source of Hire, Conversion Rates, Pipeline Health Predictive Attrition Modelling: Flight Risk Scoring Compensation Benchmarking & Pay Equity Analysis 9-Box Grid & Succession Planning Analytics Chapter 12. Learning, Training and Development Analytics L&D Landscape: Instructor-Led, e-Learning, Blended & On-the-Job Learning Introduction to Learning and Development Models of Evaluation (Kirkpatrick Model (all 4 levels), Phillips ROI Model, Net Promoter Score for Training) LMS Data Analytics: Completion Rates, Assessment Scores, Learning Hours per Employee Linking L&D Investment to Business Outcomes (Skills Gap → Performance → Revenue) Skills Analytics & Competency Mapping Chapter 13. Advanced HR Analytics KPI and HR KPI Dashboards Talent Analytics Maturity HR Cost Benefit Analysis Social Media & Talent Intelligence (LinkedIn Data, Employer Brand Analytics, Glassdoor Insights) HR Balanced Scorecard Attrition Prediction Machine Learning in HR: Classification Models (Random Forest, Logistic) for Attrition Natural Language Processing (NLP) for HR: Sentiment Analysis of Exit Interviews & Surveys Organisational Network Analysis (ONA): Collaboration Patterns & Influence Mapping AI-Powered HR Tools: Chatbots, Resume Screening, Predictive Scheduling Chapter 14. DEI Analytics (New Section) Diversity, Equity & Inclusion (DEI) Metrics: Representation, Pay Gap, Promotion Parity Measuring Inclusion: Belonging Index, Psychological Safety Scores Bias Detection in Hiring & Performance Data Reporting DEI Progress: Dashboard Design & Stakeholder Communication Chapter 15. HR Data Ethics, Privacy & Compliance (New Section) Data Privacy in HR: GDPR, India's DPDP Act 2023, Employee Consent Ethical Use of People Analytics: Surveillance vs Insight Algorithmic Bias in HR AI Tools: Detection & Mitigation HR Audit & Compliance Analytics: Labour Law Reporting, Statutory Compliance Tracking
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