Overview

No one can dispute that data is an essential component of any organization or company; this is a proven fact, so HR professionals need to develop their data analysis competencies to be able to improve current HR practices and support data-driven business decisions.

This accredited course follows a practical methodology in collecting, structuring and analyzing data related to human resources practices in the areas of workforce planning, performance management, diversity and inclusion, training and development, attraction and retention, leadership, and employee engagement. The course also addresses the best professional practices of major inteational companies such as Amazon, Federal Postal Corporation, Nielsen, Westpac, and the British Metropolitan Police.

A data-driven methodology ensures that HR professionals increase their value to corporate decision makers and improve the future of their organizations by following a scientific and unbiased approach to decision making.

Methodology

This interactive workshop follows many practical leaing methods that help participants immediately apply all the tools leaed during the workshop. The workshop covers many case studies and professional practices adapted from the best inteational companies, in addition to exercises and activities that focus on enhancing the analytical skills of the participants. In addition, it relies on professional change methods and will therefore strengthen participants' conviction about the great importance of data analysis in all aspects of human resources practices.

Course objectives

At the end of the course, participants will be able to:

Demonstrate a deep understanding of the uses of data analytics across HR departments

Applying data analysis tools to improve recruitment and employment decisions and predict Job dropout rates

Analyzing the impact of education and development programs on employee motivation using a linear regression model

Using statistical tests to enhance the culture of diversity and inclusion in the organization

Predicting employee performance level extracted from employee engagement survey data

Apply HR data analysis tools and strategies within their own work environment

Target Groups

This training course targets all professionals specialized in various HR fields such as: training and development, talent management, organizational development, workforce planning, performance and rewards, as well as HR business partners and professionals in general.

Target competencies

Target competencies

Data analysis

Decision making

Storytelling

Data visualization

Recruitment and selection

Employee engagement

Performance Management

Education and development

Diversity and inclusion

Data-driven HR analytics

Definition of human resources analytics

Analytics process – using data to influence business decisions

Data

Metrics

Analytics

Procedures

Information sources – HR data is not limited to the HR department

The most widely used information systems and human resources programs

Basic statistics

Types of variables

Statistical evidence

Descriptive data versus data analysis

Modeling and predictive analysis

How data can help develop human resources functions

HR and Data Professionals – The importance of synergy for business benefit

Analyzing recruitment data and predicting employee dropout rates

Dependent and independent variables

Categorical and continuous variables

Logistic regression analysis methodology - building predictive models

Reduce guesswork in hiring decisions – Drive data when selecting candidates

Testing the validity and reliability of the methods used in selecting candidates

Predicting rejection and filtering candidate lists

Predict the employee attrition rate in your organization

Data-driven leaing and development - The impact of training programs on employee motivation

Converting questionnaire answers into continuous data to expand the scope of analysis opportunities

Questionnaire design - checking the inteal consistency of the questionnaire - Cronbach's alpha scale

Delete irrelevant answers from the questionnaire (outliers)

Check whether your data is representative using a parametric test

Understanding the nature of the relationship between business variables using the Pearson correlation coefficient

Using linear regression analysis to verify the variables of the impact of training programs on employee motivation

Simulating an alteative model to the Kirkpatrick model to evaluate the effect of training

Deep analysis of the organization's diversity and inclusion initiatives

The importance of diversity and inclusion (ethnicity) in institutions

Incorrect ways of using metadata to present institutional bias

Levels of freedom (p value) and statistical significance

T-tests

Chi-square 

Simple mathematical formulas

Analysis of gender bias (male/female) in the workforce and job grades using frequency tables and 

Exploring ethnic diversity among work teams using descriptive statistics

Using a t-test to report on gender-biased promotions

Using multiple linear regression analysis to model and predict racial diversity in work teams

Exploring the relationships between employee performance and engagement and organizational profitability

How to measure employee engagement rate

Factor analysis to check the reliability of questions in an employee engagement questionnaire

Analyze data to explore the relationship between customer loyalty and employee engagement rate

Stepwise multiple regression – An effective tool for exploring the relationship between different business variables

Using stepwise multiple regression to model employee performance

Review of multiple regression analysis to predict employee illness rates

Use stepwise multiple regressions to model change in employee performance over time

Applying HR data analysis within a business context – the eight-step methodology

Step 1: Link business strategies to employee management strategies

Step 2: Identify the challenges facing the business

Step 3: Form your business hypothesis

Step 4: Collect data

Step 5: Choose analysis tools and strategies

Step 6: Results and Decisions - Tu data into insights

Step 7: Communicate your conclusions through storytelling and visual aids

Step 8: Evaluate analytical interventions