HR analytics makes use of data to enlighten HR procedures. It reveals key insights that inspire smarter decisions by analyzing numbers. This data-driven strategy improves recruitment, performance management, and employee satisfaction tactics, resulting in a more effective and aligned team. Unlike traditional HR, which is based on intuition, HR analytics is based on data. It replaces gut feelings with evidence-based insights. Rather than making assumptions, HR analytics employs concrete data to forecast trends, streamline processes, and build a more strategic and impactful HR function.

HR analytics taps into diverse data types; descriptive data covers demographics, while quantitative data measures performance. Qualitative data gauges sentiments via surveys. Metrics like turnover rates and engagement scores add depth. This mix helps HR comprehend trends, make informed decisions, and craft strategies that resonate with employees. Data-driven working and analytics skills have evolved significantly in the past five years. HR workers must learn to harness the power of data analytics to make more informed decisions.

This course is designed to educate the HR Manager on how to use Excel as a better tool for their Office work. The course addresses the topic of HR analytics with a hands-on and practical focus, dealing with the difficult topic of analytics for HR professionals, both statistical and computing points dimensions.

Course Objectives

By the end of the course, participants should be able to:

• Work with Excel formulas for analysis and visualization of HR Metrics
• Work with Excel for HR roles such as payroll management, risk management, and decision making.
• Use MS Excel to create and automate the calculation of HR metrics.
• Create HR Dashboards and understand all the charts that you can draw in Excel.
• Implement predictive ML models such as simple and multiple linear regression to predict outcomes to real-world HR problems.
• Use pivot tables filtering and sorting options in Excel to summarize and derive information out of the HR data.

Duration:       10 Days

Who Should Attend

This training course is suitable for many professionals but will greatly benefit:

• HR Professionals
• General Managers
• Office Administrators

Course Content

Introduction to Advanced Excel

• Introduction Excel shortcuts
• Understand various Excel settings.
• Understand the Excel layout.
• Fundamentals and numeric data
• Saving Excel in different formats
• Importing data into Excel

Advanced Excel Formulas and Macros

• Work with various advanced Excel data functions
• Learn how to work with arrays and lists.
• Advanced logical formulas in Excel
• Advanced text formulas in excel.
• Understanding the basics of VBAs and Macros

Types of HR Metrics

• Staffing Metrics
• Training and Development Metrics
• Performance Metrics
• Other Metrics
• Data and Problem statement
• Introduction to HR Charts and Dashboards

Excel Charts

• Excel Charts - Categories of messages that can be conveyed.
• Elements of charts
• The Easy way of creating charts.
• Bar and column charts
• Formatting charts
• Line Charts
• Area Charts
• Pie and Doughnut Charts
• Why we should avoid Pie charts.
• Scatter plot or XY chart
• Frequency Distribution and Histograms
• Sparklines

Mini Project- HR Dashboard

• Age Distribution
• Hiring source
• Gender distribution
• Department distribution

Pivot Tables, Formatting data and tables

• Pivot Tables
• Pivot Charts
• Formatting data and tables
• Named Ranges
• Indirect Function
• Shortcuts

The HR dashboard and Analytics for presentation and reports

• Pivot charts
• Formatting Pivot charts
• Introduction to data index and page layouts
• Work with predictive attrition and animated macro charts
• Understand the Vlookup and Hlookup analysis.
• Learn BVG and leave analysis, FNF and Exit Analysis
• Learn how to group data and understand the Bradford Factor
• Learn the HR update and HR Update calendar.
• Develop operational dashboards including business KPI’s.
• Use pivot tables and pivot charts to efficiently perform automated report writing and analysis.

Predictive Analytics - Introduction

• Linear Regression - Introduction

Basics of Statistics

• Types of Data
• Types of Statistics
• Describing data Graphically
• Measures of Centers
• Measures of Dispersion

Machine Learning -Introduction

• Introduction to Machine Learning
• Building a Machine Learning Model

Getting Data Ready for Regression Model

• Gathering Business Knowledge
• Data Exploration
• The Data and the Data Dictionary
• Univariate analysis and EDD
• Descriptive Data Analytics in Excel
• Outlier Treatment
• Identifying and Treating Outliers in Excel
• Missing Value Imputation
• Identifying and treating missing values in Excel
• Variable Transformation in Excel
• Dummy variable creation: Handling qualitative data
• Dummy Variable Creation in Excel
• Correlation Analysis
• Creating Correlation Matrix in Excel

Creating Regression Model

• The Problem Statement
• Basic Equations and Ordinary Least Squares (OLS) method
• Assessing accuracy of predicted coefficients
• Assessing Model Accuracy: RSE and R squared
• Creating Simple Linear Regression model
• Multiple Linear Regression
• The F - statistic
• Interpreting results of Categorical variables
• Creating Multiple Linear Regression model

Mini Project - Linear Regression

• Introduction
• Data processing
• Preprocessing
• Results interpretation
• Prediction using LR.

Methodology

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience, working as professionals and trainers in these fields.

Key Notes

• The participant must be conversant with English.
• Course duration is flexible, and the contents can be modified to fit any number of days.
• The course fee includes facilitation training materials and a certificate upon successful completion of Training.
• One-year post-training support Consultation and Coaching provided after the course.