Part A: Strategy Driven Human Resources Management
Module 1 - Context setting for Data-Driven Strategic HR:
- Understanding Strategic Goals: Explore how business objectives influence HR priorities and decisions.
- Creating Data-Driven Metrics: Develop HR metrics aligned with organizational strategy using insights from business models.
- Data-Driven HR Decision Making: Apply analytical tools to assess and support strategic HR choices and strategic organization goals.
- Navigating Emerging HR Challenges: Address contemporary workforce challenges, ensuring HR strategies remain adaptive to evolving business needs.
Module 2 - New Age Strategic HRM practices across Sectors:
- Exploring Sector-Specific HR Trends: Investigate how various industries leverage data to enhance employee management.
- Customizing HR Metrics by Industry: Identify critical HR metrics tailored to specific industries like technology, finance, and manufacturing, and their role in strategic decision-making.
- Sector-Specific HR Challenges: Understand the unique HR challenges across different sectors and explore data-driven strategies to address these challenges effectively.
Module 3 - HR-Driven Strategic Transformation:
- Data-Driven Transformation Frameworks: Design frameworks that use data insights to lead strategic organizational changes.
- Aligning Organizational and Individual Goals: Focus on aligning company-wide objectives with team and individual targets for cohesive performance.
- HR as a Catalyst for Strategic Change: Position HR as a critical enabler of business turnarounds, using HR strategies to drive transformational outcomes.
Part B: Harness the Future of HR: Key Areas to Implement Analytics and Drive Organizational Success!
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.