In today’s ever-shifting digital landscape, key decisions in human resources cannot rely on instinct alone. As companies grapple with evolving workforce expectations, hybrid work models, and increasing competition for skilled professionals, traditional HR practices are no longer sufficient. Therefore, companies are now turning to HR Analytics to understand their people better, make smarter hiring choices, improve engagement and reduce attrition. It helps connect workforce data with business results, allowing HR professionals skilled in analytics to be better equipped to drive impact and stay ahead in a competitive talent landscape. This HR Analytics certification programme empowers professionals with data-driven decision-making skills. It also covers aspects of HR analytics course.
LearninGT by Grant Thornton Bharat is pleased to offer its Certificate in HR Analytics to equip you with the analytical tools and techniques used in the HR function. Through a blend of theoretical knowledge and practical application, this programme will guide you in data interpretation and visualisation tailored for HR aspects like performance management, talent acquisition, training and development and many more. It also covers aspects of HR analytics training.
Learning objectives
- To understand the role and scope of HR analytics across key HR functions including talent acquisition, performance management and more
- To gain hands-on expertise with tools such as Python, MS Excel, Tableau and Power BI for HR data analysis
- To use analytics to predict hiring needs, identify skill gaps, and plan workforce with precision
- Learn how to collect, clean, analyse, and visualise data that helps solve real HR challenges like attrition and employee engagement issues It also covers aspects of HR analytics programme.
- To interpret data and leverage it to take key decisions around human resource functions
Who should attend?
- HR and learning and development (L&D) professionals
- Entrepreneurs
- Data analysts
- Graduates and postgraduates specialising in Human Resources (HR)
Programme coverage
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Introduction to HR Analytics
- Turning data into success
- Challenges and considerations in HR analytics
- Overview of HR functions and applications of HR analytics
- Performance management, compensation and benefits, employee engagement, diversity management
- Key metrics and key performance indicators (KPIs)
- Data collection and data sources in HR analytics
- Data privacy and confidentiality
- Pandas library, NumPy library
- Data visualisation with Matplotlib and Seaborn
- Matplotlib and Seaborn
- Advanced statistical testing with SciPy
- Machine Learning with Scikit-learn
- Introduction to MS Excel for data analysis
- Microsoft Excel functions
- Case study: Ethical data
- Bias detection
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Talent acquisition analytics
- Introduction to talent acquisition analytics
- Talent acquisition strategy
- What is talent acquisition analytics?
- Job posting optimisation using Python
- Introduction to candidate source analysis
- Indeed job posting
- Recruitment sourcing channels
- Conclusion of candidate source analysis
- Understanding and evaluating time-to-hire metrics
- Calculating time-to-hire metrics
- Introduction to data analysis
- Displaying output for each metrics
- Visualisation of time-to-hire metrics data
- Introduction to predictive analytics
- Predictive analytics demonstration
- Case study:
- Case study: Talent acquisition
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Performance management analytics
- Introduction to performance management analytics
- Performance appraisal analysis part
- Skill gaps and development plans
- Setting data-driven performance goals
- 360-degree feedback analysis
- Behavioral analytics and its role across several HR functions
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Training and development analytics
- Introduction for training and development analytics
- Importance and data collection methods of training needs analysis (TNA)
- Analysing training cost, training hours and budget utilisation
- Analysing training completion by gender and age gap
- Tailoring personalised training programmes based on performance data
- Identifying competency gap score
- Impact of training on performance
- Long-term outcomes of our training programmes
- Scikit-learn predictive model
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Compensation and benefits analytics
- Introduction for compensation and benefits analytics
- Overview of pay equity
- Data cleaning and calculating median salary metrics
- Salary distribution by job level and age group
- Employee benefits
- Introducing key metrics using employee benefits
- Employee benefits analysis
- Practical demonstration of total rewards and employee value proposition (EVP)
- Total rewards and cost efficiency analysis
- Market data analysis for setting competitive compensation packages
- Sources of market compensation data and introduction to practical demonstration
- Predictive model
- Visualising cost to company (CTC) and rewards by job level
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Employee engagement and retention analytics
- Introduction to engagement and retention analytics
- Key drivers of employee engagement
- Challenges in designing and analysing surveys
- Cleaning data and calculating review scores
- Overview of absenteeism and turnover
- Causes and risk factors of absenteeism and turnover
- Turnover and absenteeism metrics
- Overview of stay and exit interviews
- Data analytics metrics from stay and exit interviews
- Analysis of stay and exit interviews
- Designing data-driven employee engagement programmes
- Participation rate analysis and predictive model
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Analytics and metrics in diversity management
- Introduction to analytics and metrics in diversity management
- Key concepts of diversity management
- Challenges in collecting and analysing diversity data
- Types of diversity metrics
- Introduction to practical analysis gender and ethnicity representation
- Gender, ethnicity and age representation
- Importance of inclusion and equity metrics
- Inclusion and participation analysis
- Industry benchmarks in setting diversity goals
- Introduction to performance-related diversity metrics
- Performance analysis by diversity
- Designing a data-driven diversity and inclusion strategy
- Retention analysis by diversity
- Power BI – demonstration
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HR decision-making with data
- Introduction and advantages of data-driven decision making
- Ethical considerations and potential biases
- HR decision-making frameworks
- Optimising recruitment strategies to solve HR challenges
- Identifying performance gaps and unequal impact of training programmes
- Evaluating training effectiveness to enhance workforce development
- Ensuring fairness in compensation to foster inclusion and reduce turnover
- Analysis of compensation data to address pay equity and retention
- Trends in HR analytic
- Case study: Company decision-making
Programme deliverables
- Extensive course material and case studies
- Quizzes and assessments
- Access to Grant Thornton Bharat LMS for one year
- Query resolution from an expert within 24 hours
- Certificate of completion post final assessment
Fees and schedule
Programme fee: 15,000 + GST
Cancellation and refund policy
- 50% refund for cancellations up to five days before the start date
- No refunds for cancellations within five days of the start or no-shows
- Cancellations must be emailed; participants may join future batches at no extra cost upon early request