Welcome to my GitHub.

I’m a Financial Engineer with a strong focus on Data Analytics, Data Science, and Applied Machine Learning, particularly in financial and business contexts.
I enjoy turning messy data into structured insights, automating analytical processes, and building models that actually support decision-making.

This profile is both my professional portfolio and my learning lab.

• 📊 Data analysis and statistical modeling
• 🤖 Machine Learning applied to real-world datasets
• 💳 Credit risk modeling and financial analytics
• ⚙️ Process automation using Python & SQL
• 📈 Data visualization and storytelling with Power BI
• 🧪 Continuous learning in data & AI

Programming & Data

• Python: pandas, NumPy, matplotlib, scikit-learn
• SQL

Analytics & BI

• Power BI (Power Query & DAX)
• Excel (advanced formulas & analysis)

Core Concepts

• Data cleaning & feature engineering
• Predictive modeling & validation
• Financial modeling & quantitative strategies
• Reproducible analysis & documentation

Development of predictive models for credit scoring and credit risk assessment, applying machine learning techniques to financial datasets.
Focus on model performance, interpretability, and business relevance.

Quantitative Asset Allocation strategies aimed at optimizing portfolio performance using data-driven approaches and financial theory.

Exploration of financial and economic strategies, combining analytical thinking, structured experimentation, and applied modeling.

My personal learning and practice portfolio.
A growing collection of Python exercises, data problems, and algorithmic challenges designed to strengthen fundamentals and problem-solving skills.

This repository represents my belief that consistent practice beats passive learning.

I’m particularly interested in roles related to:

• Data Analyst
• Data Scientist
• Data Engineer
• AI / ML Engineer
• Applied AI & Generative AI

• 💼 LinkedIn: https://www.linkedin.com/in/diegotita4

Feel free to explore the repositories below, open issues, or reach out.

I see data as a bridge between intuition and evidence.
Every project here is part of a long-term journey: learning deeply, building responsibly, and growing one commit at a time.