Learn Key Metrics in People Analytics | Introduction to People Analytics with Python

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Understanding the key metrics in people analytics is essential for making informed decisions about your workforce. Three of the most important metrics are turnover rate, average tenure, and headcount. Turnover rate measures how many employees leave an organization during a specific period, helping you identify potential retention issues. Average tenure provides insight into how long employees typically stay at a company, reflecting workforce stability. Headcount simply refers to the total number of employees, which is crucial for tracking growth or contraction over time.


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# Calculate turnover rate
employees_left = ["Alice", "Bob", "Charlie"]
total_employees = ["Alice", "Bob", "Charlie", "David", "Eve", "Frank", "Grace"]

turnover_rate = len(employees_left) / len(total_employees)
print("Turnover Rate:", round(turnover_rate * 100, 2), "%")

To calculate turnover rate, you first count the number of employees who left within a given period, then divide by the total number of employees during that period. In the example above, three employees left out of a total of seven, resulting in a turnover rate of 42.86%. A high turnover rate can signal problems such as low job satisfaction, poor management, or uncompetitive compensation. Organizations monitor this metric closely to identify areas for improvement and to benchmark against industry standards.


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import pandas as pd

# Create a DataFrame with employee tenures
data = {"employee": ["David", "Eve", "Frank", "Grace"], "tenure_years": [2, 5, 3, 4]}
df = pd.DataFrame(data)

# Calculate average tenure
average_tenure = df["tenure_years"].mean()
print("Average Tenure:", round(average_tenure, 2), "years")

1. Which metric helps organizations understand employee retention?

2. Fill in the blank: To calculate average tenure, you sum all tenures and ____ by the number of employees.

3. What does a high turnover rate typically indicate?

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