Datawetenskaplike · Sportanalise · Valencia

Ek verander data in duidelike besluite

Ek is die brug tussen data-analise en elitisport: ek transformeer prestasiemetrieke in strategieë wat die verskil maak op die veld.

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Python
R
SQL
Power BI
Machine Learning

Oor my

Data met baan-, pad- en kompetisieteks

Ek is Mario Álvarez, 'n student in Datawetenskap aan die UPV met werklike ervaring binne professionele fietsry. Ek kombineer data-analise met elitisportprestasie om elke metriek te verander in 'n besluit wat resultate verbeter.

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UPV

Opleiding

Graad in Datawetenskap, Universitat Politècnica de València.

Elite

Ervaring

Egte werk binne 'n professionele fietsryspan.

Data + Sport

Benadering

Analise toegepas op die verbetering van sportprestasie.

Ervaring

Twee wêrelde, dieselfde taal: die prestasie

Ek het op die hoogste vlak meegeding en pas vandag dieselfde kriteria toe op data: ek verstaan wat 'n nommer vir 'n sportman beteken omdat ek daardie sportman gewees het.

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Werk

Projekte

'n Outomatiese seleksie van my openbare bewaarplekke op GitHub.

Spesialisasies

HTML
Power BI
Python
R
SQL

VIS

HTML

This repository contains coursework, practical exercises, and study materials for a university data visualization course. It includes solved programming tasks using R and Python libraries like ggplot2 and Plotly, alongside practice dashboards built in Tableau.

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EVALUACION-DE-MODELOS-

HTML

This repository contains practical assignments for a university model evaluation course, covering topics such as cost-sensitive classification, fairness metrics on the COMPAS dataset, and model interpretability using partial dependence plots. The exercises and analyses are implemented using Python and R across Jupyter Notebooks and R Markdown reports.

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TRABAJO_MDP

An academic data analysis project in R that explores and characterizes a physicochemical crystal dataset. It covers data cleaning, preprocessing, and dimensionality reduction through Principal Component Analysis (PCA) using packages such as FactoMineR, factoextra, and ggplot2.

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TRABAJO_CORREDORES

Python

A Streamlit web application that helps manage cycling teams by scraping race and rider data from ProCyclingStats and FirstCycling to analyze upcoming events and cyclist specialties. It is built with Python using BeautifulSoup, Selenium, Pandas, and the Gemini API to process data and extract UCI points regulations from PDFs.

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COFIDIS

Python

Developed as an academic project, this tool scrapes UCI rankings and race data from ProCyclingStats to calculate race difficulty scores tailored to the Cofidis cycling team's calendar. It is built with Python, using Selenium and BeautifulSoup for web scraping, Pandas for data processing, and Streamlit for the interactive interface.

Sien op GitHub