Work Experience Data Analysis

工业安全数据分析与可视化项目集

A collection of Python-based data analysis and visualization projects developed from real industrial safety management experience. These projects demonstrate the practical application of data engineering and visualization skills to solve real-world EHS (Environment, Health & Safety) challenges.

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Background

As a safety engineer in the chemical/manufacturing industry, I identified several pain points in daily operations:

1. Contractor Management: Manual verification of 3,000+ contractor personnel is time-consuming and error-prone
2. Hazard Tracking: Hazard data scattered across weekly reports makes it difficult to identify high-risk areas
3. Compliance Monitoring: Certificate expiration tracking relies on manual spreadsheet management

This project collection transforms these operational challenges into automated data analysis solutions, demonstrating the value of combining domain expertise with programming skills.

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Projects Overview

1. Contractor Access Automation

承包商人员准入自动化系统

Automates contractor personnel access control and compliance verification for large industrial projects.

Metric	Value
Data Scale	3,000+ personnel records
Processing Time	~10 seconds (vs. 2 days manual)
Accuracy	99.9% (vs. 95% manual)
Key Features	Blacklist check, Certificate validation, Training compliance

Technical Highlights:

• Hash-based O(1) blacklist lookup
• Multi-level alert system (7/30/90 days)
• Phase-aware training requirements (construction vs. operation)
• Interactive HTML dashboard with Plotly

View Project Details →

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2. Hazard Risk Dashboard

隐患排查与风险可视化系统

Transforms scattered hazard data into actionable risk insights through advanced visualization.

Metric	Value
Data Scale	4,500+ hazard records
Time Range	2024-01 to Present
Key Features	Factory heatmap, Pareto analysis, 3D surface plot
Visualization	9-zone polygon-based risk mapping

Technical Highlights:

• Weighted data generation following Pareto principle
• 3D surface plot for multi-dimensional risk analysis
• Client-side sortable tables
• Quarterly rectification trend vs. 85% target

View Project Details →

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Tech Stack

Category	Technologies
Language	Python 3.8+
Data Processing	Pandas, NumPy
Visualization	Plotly (3D, Treemap, Heatmap), Matplotlib
Data Generation	Faker (realistic simulation)
Export	openpyxl, HTML report generation
Version Control	Git, GitHub

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Project Structure

Work_experience_data_analysis/
├── README.md                         # This file
│
├── Contractor-Access-Automation/     # Project 1
│   ├── README.md
│   ├── main.py
│   ├── contractor_analysis_demo.ipynb
│   ├── src/
│   ├── data/
│   └── outputs/
│
└── Hazard-Risk-Dashboard/            # Project 2
    ├── README.md
    ├── main.py
    ├── risk_analysis_demo.ipynb
    ├── src/
    ├── data/
    └── outputs/

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Quick Start

Clone Repository

git clone https://github.com/SeasonCake/Work_experience_data_analysis.git
cd Work_experience_data_analysis

Run Contractor Access System

cd Contractor-Access-Automation
pip install -r requirements.txt
python main.py

Run Hazard Risk Dashboard

cd Hazard-Risk-Dashboard
pip install -r requirements.txt
python main.py

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Career Context

These projects represent a bridge between industrial safety engineering and data engineering/analysis:

Previous Role	Transition Skills	Target Role
Safety Engineer	Domain Knowledge + Python + Data Viz	Data Analyst/Engineer
EHS Compliance	Process Automation	Business Intelligence
Risk Assessment	Statistical Analysis	Data Science

Key Competencies Demonstrated

Technical Skills:

• Python programming (OOP, data structures)
• Data manipulation with Pandas/NumPy
• Interactive visualization with Plotly
• Report automation and generation
• Git version control

Domain Knowledge:

• Industrial safety regulations and compliance
• Contractor management processes
• Hazard classification and risk assessment
• Certificate and training management

Problem-Solving:

• Identifying automation opportunities
• Translating manual processes to code
• Designing user-friendly dashboards
• Balancing accuracy with performance

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Future Enhancements

• Database integration (SQLite/PostgreSQL)
• Web application deployment (Flask/Streamlit)
• API development for system integration
• Machine learning for risk prediction
• Real-time data streaming

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License

MIT License - see individual project directories for details.

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Contact

For questions, collaboration opportunities, or career discussions:

• GitHub: @SeasonCake