Exploring data, content, and the tools that connect them.

I'm curious about what makes a video connect with people, what the numbers can actually tell us, and how to turn that into something useful. My projects bring together TikTok analytics, Python, SQL, and applications you can try for yourself.

This is where I share what I build and learn along the way: from cleaning messy exports to creating reports and interactive learning tools.

Explore TikTok Analytics · Try HABLA

My main focus: turning TikTok account exports into a clearer picture of content performance.

A Python toolkit that takes JSON or CSV exports from Apify, cleans and deduplicates the data, and generates a standalone HTML report plus tables ready for Power BI. It explores publishing frequency, hashtags, sounds, video duration, and engagement, while making data-quality issues visible.

Python · Data cleaning · Content analytics · HTML reports

A Spanish-language app for learning Python and SQL through practice. It brings together executable exercises, feedback, and persistent progress, with a web edition and a local edition.

The idea is simple: learn by writing code, seeing what happens, and trying again.

Try the web app · Explore the architecture

Python · SQL · JavaScript / TypeScript · SQLite · WebAssembly

A real-estate content analytics project exploring which video characteristics are associated with useful commercial interest. It starts with a business question: which content attracts interest beyond views?

The repository currently contains the project framing and a data-cleaning script; the analysis and findings are still to come.

Languages: Python, SQL, JavaScript, TypeScript, HTML, CSS.

Tools & platforms: Git / GitHub, Apify, SQLite, React, Cloudflare Workers, and Pyodide.

I use these across different projects: Python for analytics and automation, SQL for working with data, and web technologies for making the results easier to explore.

• Content performance: how posting patterns, video formats, and engagement relate to each other.
• Better analysis: reproducible reports, clearer metric definitions, and checks that catch messy data early.
• Useful interfaces: turning scripts and technical ideas into tools people can actually use.
• Learning by doing: building projects that connect data, code, and practical questions.

Start with a question. Build something you can try. Check what the data supports. Keep improving from there.

For analytics, that means keeping associations separate from causal claims. For applications, it means paying attention to the whole experience: setup, documentation, feedback, and what happens when something goes wrong.

If a project sparks an idea or you spot something that could work better, feel free to open an issue in its repository.