I'm a Materials Engineer and Plant Manager transitioning into Data Analytics, with hands-on experience in manufacturing, quality, process engineering and production operations.

I build practical analytics solutions with Python, SQL, PostgreSQL, Power BI and Streamlit, combining data skills with real manufacturing domain knowledge.

• 🏭 Manufacturing, quality and plant operations experience
• 📊 Building end-to-end Data Analytics projects
• 🐍 Python · Pandas · NumPy · SciPy
• 🗄️ SQL · PostgreSQL
• 📈 Power BI · DAX · Excel
• 🌐 Streamlit
• 🔧 Git · GitHub · Jupyter Notebook
• 📐 SPC · Control Charts · Cp/Cpk · Pp/Ppk
• 🔎 Defect analysis · Pareto analysis · Root Cause Analysis
• 🎯 Target roles: Data Analyst · Manufacturing Data Analyst · Quality Analyst · Operations / Process Data Analyst

Exploring: Snowflake · Data Warehousing · Advanced SPC · Anomaly Detection · Predictive Quality

Python · Pandas · SQL · PostgreSQL · Power BI · Streamlit

End-to-end manufacturing analytics focused on production performance and quality.

What I built

• Data cleaning and validation
• SQL KPI views and analysis
• Production and defect-rate analysis
• Pareto analysis
• Downtime analysis
• Interactive Power BI and Streamlit dashboards
• Data-quality and reconciliation checks

🔗 View the project on GitHub
🌐 Open the Live Dashboard

Python · PostgreSQL · SQL · SPC · Statistical Analysis

Manufacturing-focused statistical process monitoring for a powder-coating process.

What I built

• SQL-based data preparation
• Control charts
• Process capability analysis with Cp/Cpk and Pp/Ppk
• Quality measurement workflow
• Statistical monitoring of process variation

Why it matters

The project connects shop-floor measurements with statistical analysis to identify process shifts, assess stability and support data-driven improvement.

🔗 View the project on GitHub

Python · Tkinter

A collection of early Python projects built while developing programming fundamentals, including games, randomization and a GUI calculator.

🔗 View the project on GitHub

My portfolio is evolving from core manufacturing analytics toward statistical and predictive analytics:

Manufacturing Data
      │
      ▼
Descriptive & Diagnostic Analytics
      │
      ▼
SPC & Process Capability
      │
      ▼
Predictive Quality
      │
      ▼
Production Optimization
      │
      ▼
Manufacturing AI Applications

The underlying goal is to combine engineering domain knowledge, analytics and modern data technologies to solve practical industrial problems.

Before moving into data analytics, I worked in materials engineering, manufacturing, quality and plant operations.

Relevant experience includes:

• Materials Engineering
• Manufacturing & Process Engineering
• Plant Management
• Quality Engineering
• Non-Destructive Testing (NDT)
• Ultrasonic Testing
• Root Cause Analysis
• PFMEA / APQP
• Production Data Analysis
• Industrial Automation

This background helps me understand not only the data, but also the production processes behind the data.

I'm currently developing toward roles such as:

Data Analyst · Manufacturing Data Analyst · Quality Analyst · Operations / Process Data Analyst · BI Analyst

I'm particularly interested in environments where data analytics, manufacturing knowledge and process improvement come together.

🌐 Live Portfolio: https://theofil87.github.io/bela-pager-portfolio/
💻 GitHub: https://github.com/Theofil87

I'm interested in projects and roles involving:

• Data Analytics
• Manufacturing Analytics
• Quality Analytics
• Business Intelligence
• Python & SQL
• Statistical Process Control
• Data-driven process improvement
• Data Engineering and modern data platforms
• AI applications for manufacturing

Béla Páger