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Analysis of Success Factors Among Top-Earning Male Employees in India Across Departments

Data sourse Kaggle: Employee Salary Dataset https://www.kaggle.com/datasets/prince7489/employee-salary-dataset?resource=download

Based on a detailed SQL query analysis of the Employee Salary Dataset, utilizing window functions, key profiles of the highest-paid men across five major departments have been identified. The following presents the observed correlations, individual "top-performer" profiles, and critical data anomalies that require attention.

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DataFrameas
top_salary
variable
WITH male_max_sal AS (SELECT 
	Name,
    Department, 
	Experience_Years, 
	Education_Level,
	Age,
    City,     
	Monthly_Salary,
	RANK () OVER (PARTITION BY Department ORDER BY Monthly_Salary DESC) AS top_male_sal,
	FROM employee_salary_dataset.csv
	WHERE Gender = 'Male')
	
SELECT *, 'Max Salary' AS Leadership_Type
FROM male_max_sal
WHERE top_male_sal = 1

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DataFrameas
top_experience
variable
WITH male_max_exp AS(
		SELECT Name, 
		Department, 
		Experience_Years, 
		Education_Level,
		Age,
		City, 
		Monthly_Salary,
		RANK () OVER (PARTITION BY Department ORDER BY Experience_Years DESC) AS top_male_exp
	FROM employee_salary_dataset.csv
	WHERE Gender = 'Male')
SELECT *, 'Max experience' AS Leadership_Type
FROM male_max_exp
WHERE top_male_exp = 1
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DataFrameas
top_age
variable
WITH male_oldest AS(
		SELECT Name, 
		Department, 
		Experience_Years, 
		Education_Level,
		Age,
		City, 
		Monthly_Salary,
		RANK () OVER (PARTITION BY Department ORDER BY Age DESC) AS oldest_male
	FROM employee_salary_dataset.csv
	WHERE Gender = 'Male')
SELECT *, 'Max age' AS Leadership_Type
FROM male_oldest
WHERE oldest_male = 1
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DataFrameas
top_education
variable
WITH highest_education AS(
		SELECT Name, 
		Department, 
		Experience_Years, 
		CASE 
			WHEN Education_Level = 'PhD' THEN 1
			WHEN Education_Level = 'Master' THEN 2
			WHEN Education_Level = 'Bachelor' THEN 3
			WHEN Education_Level = 'High School' THEN 4
			ELSE 5
			END AS Education_number,
		Age,
		City, 
		Monthly_Salary,
		RANK() OVER(PARTITION BY Department ORDER BY Education_number) AS rank_edu
	FROM employee_salary_dataset.csv
	WHERE Gender = 'Male')
SELECT *, 'Max education' AS Leadership_Type
FROM highest_education
WHERE rank_edu = 1
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DataFrameas
df
variable
SELECT * FROM top_salary
UNION ALL
SELECT * FROM top_experience
UNION ALL
SELECT * FROM top_age
UNION ALL
SELECT * FROM top_education
ORDER BY Department, Monthly_Salary DESC, Leadership_Type

​1. Finance Department

​Key Correlations: We observe a clear positive correlation between Salary, Education, and Experience. These factors are the primary drivers for top earnings in this department.

​Top Performer Profile: The top-ranking employee is 44 years old and holds a Master's degree. He has 17 years of experience and live in Hyderabad. His salary is 72,200 rupees.

​Educational Ceiling: It is notable that the company currently has no employees with a PhD level of education in Finance. This suggests that practical expertise (Master's level) may be prioritized over the highest academic credentials.

​2. HR Department

​Key Correlations: We see a strong correlation between Salary, Age and Experience.

Top Performer Profile: The highest-ranked employee in HR holds a Master's degree, he has 15 years of experience. He is 48 years old and lives in Bangalore. His salary is 64,790 rupees. This profile (experienced, mid-to-late career, advanced degree) is typical for leadership roles in human resources.

​3. IT Department

Correlations in Top Tier: Among top-ranked employees, we see correlations involving Age, Education, and Experience.

Salary Anomaly: It is crucial to note that the Salary level do not generally correlate across the department.

​Specific Outlier: An employee with 14 years of work experience earns only 30,600 rupees. This is highly suspicious as the individual is only 24 years old (implying he started working at age 10). This indicates a probable data entry error in the Experience_Years or Age field, which may be skewing departmental averages.

Top Performer Profile: The top employee in salary range is a person who has 13 years of experience, has a Master's degree. He's 53 years old, lives in Bangalore and earns 130,983 rupees.

​4. Marketing Department

Key Correlation: There is a correlation between Salary and Education level.

​Top Performer Profile: The top-ranked employee is 43 years old but has only 8 years of experience. This suggests that high pay in Marketing is driven less by long-term tenure and more by skill acquisition and educational background. He has PhD level of Education, lives in Delhi and earns 141,381 rupees.

​5. Operations Department

Correlations in Top Tier: We observe correlations between Age and Education.

Specific Outlier: Employee 10 has the highest number of years of experience (18 years), yet their age is only 28 years old. Furthermore, their salary is very low compared to others. This is a severe data anomaly and indicates an error in the data source, rendering any average statistics for this department unreliable until the record is corrected.

Top Performer Profile: The highest-ranked employee holds a Master's degree. He's only 23 years old, but already has 9 years of Experience (implying he started working at age 14). He lives in Mumbai and earns 149,123 rupees.