DATA ANALYTICS FOR HUMAN RESOURCE STUDENTS
DATA ANALYTICS FOR HUMAN RESOURCE STUDENTS
Introduction to Data Analytics
Data Analytics is the process of examining raw data to uncover trends, patterns, and insights that help organizations make informed decisions. By using various statistical and computational techniques, data analysts transform complex data sets into actionable knowledge. This field is essential for businesses, as it aids in improving operations, predicting future trends, and solving problems. Whether you're just starting out or looking to expand your skills, mastering data analytics opens up opportunities to drive success in a data-driven world. Join our training program to dive into the fundamentals of data analytics and learn how to turn data into valuable insights
Overview of Popular Data Analytics tool
1. Microsoft Excel
2. Tableau
3. Power BI
4. Google Analytics
5. Python
6. R
7. SPSS
8. Hadoop
Descriptive Statistics
1. Measures of Central Tendency (Mean, Median, Mode)
2. Measures of Variability (Range, Variance, Standard Deviation)
3. Data Visualization (Histograms, Box Plots, Scatter Plots)
Inferential Statistics
1. Hypothesis Testing (Z-test, T-test, ANOVA)
2. Confidence Intervals (CI)
3. Regression Analysis (Simple, Multiple, Logistic)
HR Metrics and Analytics
1. Employee Turnover Rate
2. Time-to-Hire and Time-to-Fill
3. Training Evaluation Metrics (Kirkpatrick Model)
4. Diversity and Inclusion Metrics
5. Employee Engagement and Satisfaction Metrics
Workforce Planning and Forecasting
1. Regression Analysis for Forecasting
2. Time Series Analysis (TSA)
3. Exponential Smoothing (ES)
4. ARIMA Models
5. Monte Carlo Simulations
Talent Management and Development
1. Performance Metrics (KPIs)
2. 360-Degree Feedback Analysis
3. Succession Planning Metrics
4. Leadership Development Metrics
5. Employee Potential Assessment
Compensation and Benefits Analysis
1. Regression Analysis for Salary Modeling
2. Benefit Cost Analysis
3. Compensation Ratio Analysis
4. Total Rewards Analysis
5. Employee Value Proposition (EVP) Analysis
Diversity, Equity, and Inclusion (DEI) Analysis
1. Diversity Metrics (Representation, Inclusion)
2. Equity Metrics (Pay Equity, Promotion Equity)
3. Inclusion Metrics (Employee Engagement, Sense of Belonging)
4. Bias Detection and Mitigation
5. DEI Scorecards and Dashboards
Advanced Statistical Techniques
1. Machine Learning Algorithms (SVM, K-Nearest Neighbors)
2. Generalized Linear Models (GLM)
3. Generalized Additive Models (GAM)
4. Bayesian Statistics
5. Propensity Score Analysis
These statistical models and techniques are essential for HR students to understand and apply in various HR disciplines, Including
1. Recruitment and Selection
2. Talent Management and Development
3. Compensation and Benefits
4. Diversity, Equity, and Inclusion
5. Workforce Planning and Analytics
Expected Out Come
Mastering these statistical concepts will enable HR students to analyze complex HR data, make informed decisions, and drive organizational success.
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