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Certified HR Analyst
DATE
2024-05-01;
LOCATION
To Be Determined;
Why Attend?
Data is crucial for any business. This is not an argument anymore, but rather a matter of fact. Therefore, HR professionals need to develop their data analysis competency in order to inform and transform their current HR practices and most importantly, business decisions.
This certified course offers participants a hands-on approach on collecting, structuring, and analyzing HR-related data in areas like workforce planning, performance management, diversity and inclusion, learning and development, recruitment, retention, leadership, and employee engagement. The practices shared come from highly renowned organizations around the world like Amazon, UPS, Nielsen, Westpac, and the Metropolitan Police.
This data-driven approach ensures that HR professionals bring high-value input to people-related decisions and shape the future of their organizations using a scientific and unbiased approach.
Course Objectives
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This Course Objective Hasn't Been Provided Yet
Target Audience
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.
- 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 interve
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