Work and Mental Health

📑 Project Overview
This project analyzes the results of a survey of 5,000 employees from diverse regions, industries, ages, genders, and work experiences. The survey data, sourced from open platforms, investigates the relationships between various factors affecting work and mental health. The goal is to uncover correlations or prove their absence, visualize trends, and showcase different analytical tools and methods.

💡Motivation
This project was inspired by the growing recognition of the connection between work environment, workload, and mental health. With many workers shifting to remote or hybrid setups, understanding how different work conditions affect psychological well-being has never been more important.

💾 Data Sources
Excel folder: "Impact_of_Remote_Work_on_Mental_Health"
SQL folder: "Creating Tables"

Goals 📝

1. Analyze the impact of work location, age, gender, work experience, region, and profession on psychological health and job satisfaction.
2. Study the effect of access to health resources on mental health.
3. Examine the influence of physical activity on psychological well-being.
4. Identify and interpret significant relationships based on survey data.
5. Visualize data and trends using SQL, Python, Excel and Tableau.

Tools Used 🛠️

• SQL:
  • Aggregation Functions
  • Joins
  • Subqueries
  • Window Functions
  • CASE Statements
  • CTEs
• Python:
  • Pandas, NumPy
  • Statsmodels (scipy.stats)
  • Seaborn, Matplotlib
• Tableau:
  • Quick Table Calculations
  • Parameters and Calculated Fields
  • Dashboards and Stories
• Excel:
  • Pivot Tables
  • Charts

Files and Folders 📂

🗄️ SQL Queries: Scripts for data aggregation.
🐍 Python Analysis: Statistical analysis and visualizations.
📊 Tableau Dashboards: Interactive data visualizations.
📈 Excel Data: Raw data, pivot tables and charts.
🔍 Images for visualisation

💡Each folder contains a README file that provides a description of its contents.

Key Findings 🔬

• Distribution of Surveyed Employees: An uneven distribution of interviewed employees by age, with a certain preference for people older than middle age.
  Details:
  📁 Excel
  🗄️ SQL File
  🐍 Python
• Remote Work Satisfaction: Remote work slightly improves satisfaction for women but shows no major impact on overall satisfaction. Women: 33.33% satisfied with remote work; Men: 32.02%; Non-binary/Prefer not to say: ~28%.
  General satisfaction: 30.28%, dissatisfaction: 34.54%.
  Details:
  📊 Interactive Tableau Visualization
  📁 Download Tableau Workbook
  🗄️ SQL File
• Work Life Balance: Best balance: Age 51+ in hybrid work, in retail, IT, and education (best region: Asia). Potential bias due to sample representation.
  Details:
  📊 Interactive Tableau Visualization
  📁 Download Tableau Workbook
  🗄️ SQL File
• Work and Mental Health: 76.08% of respondents reported psychological issues, with onsite employees slightly more affected (77.03%). Access to mental health resources showed no direct correlation with mental health conditions.
  Details:
  📊 Interactive Tableau Visualization
  📁 Download Tableau Workbook
  🗄️ SQL File
  🐍 Python
• Working Hours: The manufacturing sector has the highest global average of weekly working hours (40.24), with South America leading at 41.82 hours. In contrast, Europe’s IT sector has the lowest average (40.20), emphasizing work-life balance and efficiency.
  Details:
  📊 Interactive Tableau Visualization
  📁 Download Tableau Workbook
  🗄️ SQL File
• Physical Activity: Remote workers are the most active: 34.19% exercise daily. Extremely weak negative correlation between physical activity and stress levels.
  Details:
  🗄️ SQL File
  🐍 Python
• Impact of Work Location: No significant correlation between work location and indicators like work-life balance rating, stress level, productivity change, social isolation, physical activity, and sleep quality.
  Details:
  🐍 Python

🧮 For the full report, read the Detailed Conclusions.

Broader Implications 🌍

This research, while limited in scope, provides actionable insights for employers and policymakers:
Workplace Policies: Use the findings to shape flexible work arrangements, mental health support programs, and workload planning tailored to regional and sector-specific needs.
Future Research: Address gaps such as the underrepresentation of certain demographic groups to obtain more accurate results.
For a detailed view of the methodology and analysis, refer to the respective files in the Python, SQL, Excel and Tableau folders.

How to Access

📂 GitHub Repository 🌐 Tableau Visualization

License

This project is licensed under the MIT License - see the LICENSE file for details.