Clean and Analyze Employee Dataset

[ahsancloudcode]

The employee dataset requires cleaning and analysis. Here are the key tasks and initial observations:

Data Cleaning Tasks Needed:

1. Handle Missing Values

   • Several rows have missing first names
   • Some rows have missing gender information
   • Multiple rows have missing team information
   • Missing values in Senior Management (boolean field)
   • Standardize empty values to proper NULL/NA format

2. Date Format Standardization

   • Start Date is in MM/DD/YYYY format - needs standardization
   • Last Login Time needs parsing into proper datetime format

3. Data Type Conversions

   • Convert Salary to numeric
   • Convert Bonus % to numeric
   • Convert Senior Management to boolean
   • Convert dates to datetime objects

Initial Analysis Tasks:

1. Workforce Demographics

   • Gender distribution
   • Team distribution
   • Senior management ratio
   • Average tenure based on start dates

2. Compensation Analysis

   • Salary distribution and statistics
   • Bonus percentage analysis
   • Gender pay gap analysis
   • Team-wise salary comparisons

3. Temporal Analysis

   • Employee start date patterns
   • Login time patterns
   • Length of service distribution

4. Team Analysis

   • Team size comparisons
   • Team-wise gender distribution
   • Average compensation by team
   • Senior management distribution across teams

Initial Data Summary:

• Total Records: ~500
• Columns: 8
• Time Range: Start dates from 1980 to 2016
• Teams: Marketing, Finance, Legal, Product, Distribution, etc.
• Salary Range: ~$35,000 to $150,000
• Bonus Range: ~1% to 20%

Tools/Technologies Needed:

• Python with pandas for data cleaning and analysis
• Matplotlib/Seaborn for visualizations
• Jupyter Notebook for documentation

Expected Deliverables:

1. Cleaned dataset in standardized format
2. Summary statistics report
3. Demographic analysis report
4. Compensation analysis report
5. Visualizations of key metrics
6. Documentation of cleaning methodology
7. Recommendations based on findings

Next Steps:

1. Set up analysis environment
2. Import and create initial data backup
3. Begin cleaning process
4. Conduct exploratory data analysis
5. Generate reports and visualizations
6. Document findings and recommendations