About

I’m building this portfolio to document my growth toward a future Data Scientist role. My projects focus on the full workflow: turning messy datasets into clear insights, building baselines, and communicating results in a way that’s useful for decision-making.

My background is in business operations and cross-functional support, which shaped how I approach data work: define the question, choose the right metric, validate assumptions, and deliver something actionable. I care about clarity, reproducibility, and thoughtful trade-offs—not just “getting a model to run.”

This site is a living log of what I’m learning and improving. As I complete new sprints, I update older work (cleaner notebooks, better evaluation, clearer storytelling) and keep my best projects easy to explore.

Background & education

• TripleTen — Data Science program (in progress): Python, SQL, EDA, and ML projects documented in this portfolio
• Business Administration — EBC (Escuela Bancaria y Comercial)

Tools I use beyond data projects

In addition to Python/SQL work, I’m comfortable operating in business environments and supporting cross-functional workflows. I regularly use Microsoft 365 tools and enterprise systems, and I’m increasingly incorporating AI-assisted workflows to work faster and communicate more clearly.

• Microsoft 365 (Excel, PowerPoint, Teams, SharePoint, Power Automate, Word, Outlook) for reporting, documentation, training and stakeholder communication
• Oracle and Dynamo (deal / investor operations workflows)
• AI-assisted productivity (prompting, summarization, drafting, and workflow support)

What I’m learning now

• Stronger EDA: data quality checks, edge cases, and insight-driven visuals
• Model evaluation: baselines, cross-validation thinking, and error analysis
• Communicating results: tighter conclusions, business framing, and next-step recommendations
• Lightweight demos: sharing results via simple apps (e.g., Streamlit) when it adds value

Core tools

Python • Pandas • NumPy • SQL • Git/GitHub • Jupyter