Welcome to my GitHub profile! I'm a passionate data scientist and developer with a strong focus on artificial intelligence, machine learning, and data visualization. My projects span various domains, including financial analysis, time series forecasting, and environmental data analytics. I enjoy working with R, Python, and Shiny to create insightful tools and applications that help researchers, businesses, and data enthusiasts make informed decisions.
An interactive dashboard that provides an intuitive and dynamic visualization of global greenhouse gas (GHG) emissions. Built using data from the 2024 Emissions Database for Global Atmospheric Research (EDGAR), this project makes complex environmental data more accessible, engaging, and insightful.
Key Features:
- Interactive maps and charts displaying global GHG emissions trends.
- Data exploration tools for different regions, sectors, and emission sources.
- Built with R and Shiny for seamless interactivity and real-time analysis.
An extension designed to enhance Shiny development workflows by providing streamlined tools that simplify common tasks across various Shiny frameworks. This project aims to improve efficiency and reduce development time for R developers working on interactive web applications.
A collaborative project that brings Spanish translations of fundamental books on R and data science to the community. Initially started as a personal learning initiative, this project has evolved into an open-access resource for Spanish-speaking learners and professionals.
Translated Works:
- Programación Práctica con R - Garrett Grolemund
- R para la Ciencia de Datos - Hadley Wickham, Mine Çetinkaya-Rundel & Garrett Grolemund
- Git & GitHub con R - Jenny Bryan & collaborators
- Modelado Ordenado con R - Max Kuhn & Julia Silge
- R Avanzado - Hadley Wickham
- Paquetes de R - Hadley Wickham & Jenny Bryan
- ggplot2: Gráficos Elegantes para Análisis de Datos - Hadley Wickham, Danielle Navarro & Thomas Lin Pedersen
Objective:
- Facilitate access to high-quality learning materials for Spanish speakers.
- Promote best practices in R programming and reproducible research.
- Strengthen the global R community by encouraging knowledge sharing.
A collection of repositories containing my Final Master's Thesis, where I explore the application of artificial neural networks and quadratic programming in financial portfolio management.
Key Topics Covered:
- Characterization of financial time series for improved forecasting.
- Integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to enhance predictive capabilities.
- Application of quadratic programming for optimal asset allocation.
- Challenges and opportunities in AI-driven portfolio management.
A project dedicated to automating a methodology for implementing long short-term memory (LSTM) networks in time series forecasting. This repository serves as a central hub for structured experimentation, model evaluation, and deployment strategies, enabling efficient and reproducible forecasting processes.
- Programming Languages: R, Python
- Machine Learning & AI: TensorFlow, Keras, LSTMs, CNNs
- Data Visualization: Shiny, ggplot2, Plotly, D3.js
- Finance & Optimization: Portfolio Theory, Quadratic Programming
- Version Control: Git & GitHub
I'm always open to collaboration, discussions, and learning opportunities. If you're interested in any of my projects or have ideas for new initiatives, feel free to reach out!