IIM Nagpur | Post Graduate Certificate Programme for Advanced HR Analytics (Batch 01)

Course Modules

Module 1: Introduction to HR Analytics

• Understand the domain of HR analytics and its application in modern organizations.
• Aligning HR Analytics to Organizational Goals, Objectives and HR Strategy
• Explore the HR analytics continuum and its impact on decision-making.
• Understand the basics of Analytics and various tools used
• Learn to design and implement HR analytics projects using evolving HR technologies.
• Challenges in implementing HR Analytics

Module 2: Descriptive Analytics and Data Cleaning

• Identify sources of HR Data
• Capture relevant HR data from various sources and clean the data for analysis.
• Analyze HR metrics to measure organizational performance and effectiveness.
• Develop customized HR metrics tailored to organizational needs.
• Utilize data visualization techniques to communicate HR insights effectively.
• Implement data governance practices to ensure data quality and integrity.
• Explore emerging trends in HR analytics and their implications for data management.
• Case studies and practical exercises on HR data analysis and interpretation

Module 3: Predictive Analytics and Modelling

• Choose appropriate predictive analytic models for quantitative HR data.
• Work with qualitative HR data and interpret predictive analytic results.
• Implement machine learning algorithms and AI for HR prediction tasks, such as employee turnover or performance forecasting.
• Evaluate the accuracy and performance of predictive models using metrics such as precision, recall, and ROC curves.
• Incorporate time-series analysis techniques to forecast HR trends and patterns.

Module 4: Prescriptive Analytics and Optimization

• Apply prescriptive analytics techniques to address HR challenges and optimise processes.
• Customise solutions based on contextual requirements and stakeholder needs.
• Implement decision support systems to facilitate data-driven decision-making in HR.
• Utilize simulation modelling to predict the impact of HR policies and interventions

Module 5 - Workforce planning, Talent Acquisition and Development Analytics

• Utilise analytics to improve workforce planning, talent acquisition processes and strategies.
• Analyse employee development data to identify skill gaps and training needs.
• Predictive Analytics using NLP
• Develop strategies for talent development and succession planning.

Module 6 - Performance Management and Rewards Analytics

• Analyse employee performance and potential using predictive analytics models.
• Employee Engagement Surveys with NLP Sentiment Analysis
• Understand the relationship between performance, potential, and rewards.
• Design and implement performance management systems based on analytics insights

Module 7 - Employee Engagement and Retention Analytics

• Measure and track employee engagement levels using analytics tools.
• Identify factors influencing employee engagement and satisfaction.
• Develop strategies to enhance talent engagement and retention.
• Utilize predictive modelling to forecast employee turnover and attrition rates.
• Implement employee feedback mechanisms for continuous improvement of engagement initiatives.

Module 8: Culture Fit and Organisational Wellness Analytics

• Assess value congruence between organisational culture and employee values.
• Analyse induction processes to ensure cultural alignment and employee engagement.
• Analyse employee wellness data to identify trends and patterns.
• Implement strategies for fostering diversity and inclusion within the organizational culture.
• Utilize sentiment analysis techniques to gauge employee perceptions of organizational culture

Module 9: Introduction to R for HR Analytics

• Overview of R programming language
• Data manipulation and analysis with R
• Statistical analysis and visualization in HR analytics using R
• Explore machine learning and AI techniques for predictive HR analytics using R.
• Apply R packages specific to HR analytics

Module 10: Capstone Project

• Apply learned concepts and techniques to a real-world HR analytics project.
• Collect, analyse, and interpret HR data using Python, R, and advanced Excel
• Develop recommendations for improving HR processes and practices.
• Present findings and insights using Power BI and Tableau.