Isaac Vélez Aguirre
Data Science & Business Analytics Student
University of London & Forward College | Berlin, Germany
About Me
I am Isaac Vélez Aguirre, a Colombian-Spanish third-year student studying Data Science & Business Analytics at the University of London and Business & Leadership at Forward College. I am passionate and curious, constantly seeking opportunities for personal growth and learning.
Currently, I am applying to master’s programs for the Fall of 2026 to expand my skills and become a more qualified professional. I have experience in the tech field as a Software Engineer and Data Scientist, working with AI/ML technologies and Large Language Models.
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Featured Projects
Explore some of my recent work in data science and machine learning
Grandma vs. Data Scientist Student: Information-Theoretic Wordle Solver
This project is an intelligent Wordle solver that uses information theory and optimization algorithms to play the New York Times Wordle game with high accuracy and efficiency. It models each guess as an information-gathering step, selecting words that maximize expected information gain and minimize the number of guesses needed to find the correct answer. I originally built it to compete playfully with my grandmother, a retired English professor and lifelong word-game enthusiast, and it has become a fun way for us to connect, compare strategies, and talk about language from two very different perspectives: hers as a human expert in words and mine as a data science student building algorithms.
Handwritten Digit Recognition with Neural Networks
This project is a neural network implementation from scratch for handwritten digit recognition. Built entirely using fundamental machine learning principles, it demonstrates the core concepts of feedforward neural networks, backpropagation, and gradient descent without relying on high-level deep learning frameworks. The project includes an interactive graphical user interface that allows users to draw digits on a canvas and receive real-time predictions from the trained model, making it both an educational tool and a practical demonstration of neural network capabilities.
Programming for Data Science Coursework: MCMC Algorithms & Flight Data Analysis (University of London)
A comprehensive statistical computing project completed for ST2195 (Programming for Data Science) at the University of London, consisting of two parts: (1) implementation and analysis of the Metropolis-Hastings MCMC algorithm for simulating random numbers from a Laplace distribution, and (2) analysis of commercial flight data from the 2009 ASA Statistical Computing and Graphics Data Expo. The project demonstrates proficiency in both R and Python, covering topics from Bayesian statistics and convergence diagnostics to logistic regression modeling and large-scale data analysis.