SQL Data Wrangling on Employee Gender Distribution 📊

Harnessing the power of SQL, we embarked on a data wrangling journey to understand the gender dynamics within a company. Our primary aim was to dissect the distribution between male and female employees on a yearly basis, starting from the year 1990.

Steps Undertaken:

1. Data Cleaning:

• Null Values: Identified and addressed null values in relevant columns, ensuring data integrity.
• Data Types: Ensured that the 'gender' and 'joining_date' columns were of the appropriate data types.

2. Filtering Data:

• Used WHERE clauses to focus solely on records from 1990 onwards.

3. Gender Breakdown:

• Deployed a GROUP BY clause on the 'joining_date' and 'gender' columns.
• Used the COUNT function to get the number of male and female employees for each year.

4. Results Presentation:

• Used ORDER BY to arrange results chronologically.
• Presented the data in a structured format with columns: Year, Male Employees, and Female Employees.

5. Advanced Analysis (Optional):

• Calculated the Gender Ratio for each year to gauge gender parity.
• Highlighted years where there was a significant disparity between male and female hires.

Key Insights Gained:

1. Yearly Trends: Detected certain years where hiring was skewed towards a particular gender.
2. Peak Hiring Periods: Identified specific years where hiring surged for both genders, possibly indicating business expansion or high attrition periods.
3. Gender Parity Progress: Observed whether the company was progressing towards achieving gender parity in its hiring practices over the years.

Conclusion:

Through SQL data wrangling, we provided a clear, year-by-year breakdown of gender distribution within the company. This not only sheds light on the company's hiring practices but also serves as a foundation for more advanced gender-related analyses in the future. The insights derived from this can guide HR policies and diversity inclusion strategies for the company.