Course Objectives
This training course is designed to be interactive and practical. It includes various learning methodologies that enable participants to immediately implement all the tools they learn during the training program. The training course delivered by Gulf Training and Consultation Center contains many Case studies from the best multinational companies followed by practice sessions dominate the learning methods in this course which aims at strengthening participants’ analytical skillset. In addition, mindset-changing will build participants’ conviction about the paramount importance of data in all aspects of HR practices. By familiarizing the participants with the following knowledge and skills:
- Demonstrate deep understanding of the use of data analytics in HR disciplines
- Implement data analytics tools and strategies to improve recruitment decisions, and predict employee turnover
- Analyze the impact of learning and development provision on employee motivation using linear regression
- Promote a culture of diversity and inclusion within their organization through significance statistical tests
- Predict employee performance using data from employee engagement surveys
- Apply HR data analysis strategies and tools in their own business environment
Content
- Data-driven HR analytics
- Definition of HR analytics
- The analytics process – using data to influence business decisions
- Data
- Metrics
- Analytics
- Action
- Information sources – HR data are not only found in HR departments
- The most commonly used HR information systems and data analysis platforms
- Basic statistics
- Types of variables
- Statistical significance
- Descriptive data vs. data analysis
- Modelling and predictive analysis
- How data are reinventing the HR functions
- HR professionals and data – how to synergize for the best of the business
- Data analysis of recruitment and prediction of employee turnover
- Dependent and independent variables
- Categorical and continuous variables
- Logistic regression analysis methodology – building predictive models
- Removing guesswork from recruitment decisions – data-informed candidate selection decisions
- Testing validity and reliability of candidate selection methods
- Predicting rejection and shortlisting of candidates
- Predicting employee turnover in your organization
- Data-driven learning and development – the impact of training provision on employee motivation
- Transforming answers of questionnaires into continuous data to expand analysis opportunities
- Questionnaire design – testing internal consistency of questionnaires – Cronbach’s alpha measure
- Removing irrelevant answers from respondents (outliers) to questionnaires
- Testing if your data is representative using normality test
- Understanding the nature of relationship between business variables using Pearson’s correlation
- Examining the impact of training provision variables on employee motivation using linear regression
- Simulating an alternative model to Kirkpatrick’s model for evaluating training impact
- Deep analysis of diversion and inclusion in the organization
- The importance of diversion and inclusion (ethnic and gender) in organizations
- Wrong ways of using descriptive data to present a case of organization bias
- Significance p value and degrees of freedom
- t-tests and chi square test – a simple mathematical notion
- Analyzing gender bias in workforce and job grades using frequency tables and chi square
- Exploring ethnic diversity across teams using descriptive statistics
- Reporting gender-biased promotions using t-tests
- Using multiple linear regression to model and predict ethnic diversity variation across teams
- Exploring relationships between employee performance, employee engagement, and profitability
- How to measure employee engagement
- Factor analysis to test the reliability of questions in an employee engagement survey
- Analyzing data to explore the relationship between customer loyalty levels and employee engagement levels
- Stepwise multiple regression – an effective tool to explore relationships among business variables
- Using stepwise multiple regression to model employee performance
- Revisiting multiple regression to predict employee sickness
- Modeling change in performance of employees over time using stepwise multiple regression
- Application of HR data analysis in business context – An eight-step methodology
- Step 1: Linking business strategies to people strategies
- Step 2: Identifying business challenges
- Step 3: Forming your business hypothesis
- Step 4: Gathering your data
- Step 5: Choosing analysis tools and strategies
- Step 6: Findings and decisions – turn data to insights
- Step 7: Communicating your conclusion with storytelling and visualization
- Step 8: Evaluating your analytical intervention
Course Target
This course is targeted at HR professionals from all practices: learning and development, talent management, organization development, workforce planning, performance and rewards. HR business partners, and generalists would also benefit greatly from this workshop.
Human Resources and Training Management
Certified HR Analyst
Duration: 5 Days
Start Date: 2025/06/15
Location: Casablanca
Course Fees:
