Podatkovni znanstvenik · Športna analitika · Valencia

Podatke spreminjam v jasne odločitve

Sem most med analizo podatkov in vrhunskim športom: metrike zmogljivosti spreminjam v strategije, ki naredijo razliko na terenu.

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Python
R
SQL
Power BI
Machine Learning

O meni

Podatki s kontekstom s steze, ceste in tekmovanj

Sem Mario Álvarez, študent podatkovne znanosti na UPV z izkušnjami iz resničnega sveta v profesionalnem kolesarstvu. Združujem analizo podatkov in vrhunsko športno uspešnost, da vsako metriko pretvorim v odločitev, ki izboljšuje rezultate.

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UPV

Izobraževanje

Diplomski študij podatkovne znanosti, Universitat Politècnica de València.

Elita

Izkušnje

Resnično delo znotraj profesionalne kolesarske ekipe.

Data + Sport

Pristop

Analitika, uporabljena za izboljšanje športne uspešnosti.

Izkušnje

Dva sveta, enak jezik: zmogljivost

Tekmoval sem na najvišji ravni in danes uporabljam isto merilo za podatke: razumem, kaj število pomeni za športnika, ker sem bil tisti športnik.

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Delo

Projekti

Samodejni izbor mojih javnih repozitorijev na GitHub.

Specializacije

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.

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