class DataScienceStudent:
    def __init__(self):
        self.name = "Elias"
        self.age = 21  
        self.university = "ESCOM - Instituto Politécnico Nacional MX"
        self.field = "Data Science"
        self.interests = [
            "Data Engineering",
            "Data Analytics",
            "Machine Learning & AI",
            "Data Science"
        ]
        self.motto = "Question Everything. Measure What Matters."

    def current_goal(self):
        return "Build practical solutions and learn from real-world data."

I'm a Data Science student at ESCOM-IPN, interested in transforming raw data into meaningful insights and building practical, data-driven solutions.

My interests include data engineering, analytics, machine learning, and the development of tools that solve real-world problems.

I believe that learning happens by building, experimenting, measuring results, and continuously improving.

Currently focused on:

• 📊 Exploring and analyzing data to uncover useful insights.
• 🛠️ Developing data processing workflows and backend applications.
• 🗄️ Strengthening my SQL, PostgreSQL, and data engineering skills.
• 🤖 Learning more about machine learning and artificial intelligence.
• 🚀 Building projects that connect programming, data, and real-world needs.

Estudio Ciencia de Datos en ESCOM-IPN. Me interesa desarrollar soluciones que conecten la programación con el análisis de datos, desde su extracción y procesamiento hasta la generación de información útil para tomar decisiones.

Mi objetivo es seguir construyendo proyectos prácticos, fortalecer mis fundamentos técnicos y crecer profesionalmente en el ecosistema de datos.

A project focused on automating the processing of delivery tickets and assigning addresses to geographic sectors.

The goal is to transform operational information into structured data that can be processed, mapped, and analyzed.

Key features:

• 📄 Extract and process information from delivery ticket PDFs.
• 📍 Geolocate addresses and associate them with geographic sectors.
• 🗺️ Work with geographic data using GeoJSON.
• ⚡ Build a backend API to support the processing workflow.
• 📊 Prepare structured outputs for further analysis and reporting.

Technologies used:

Areas of application: data processing, automation, geospatial analysis, and operational analytics.

📌 Repository: ...

Completed professional training in data analytics through Google's certificate program.

...

I believe good data work starts with good questions.

Question assumptions. Measure outcomes. Learn from evidence. Build solutions that make a difference.

Always learning, always building.