Andmeteadlane · Spordianalüütika · Valencia

Muudan andmed selgeteks otsusteks

Olen sild andmeanalüüsi ja eliitspordi vahel: muudan tulemuslikkuse mõõdikud strateegiateks, mis teevad väljakul vahet.

Python
R
SQL
Power BI
Machine Learning

Minust

Andmed väljaku, maantee ja võistluse kontekstiga

Olen Mario Álvarez, andmeteaduse üliõpilane UPV-s, kellel on reaalne kogemus professionaalsest the jalgrattaspordist. Ühendan andmeanalüüsi eliitspordi sooritusega, et muuta iga mõõdik otsuseks, mis parandab tulemusi.

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UPV

Haridus

Andmeteaduse bakalaureusekraad, Universitat Politècnica de València.

Eliit

Kogemus

Reaalne töö professionaalses jalgrattameeskonnas.

Data + Sport

Lähenemine

Sporditulemuste parandamisele rakendatud analüütika.

Kogemus

Kaks maailma, üks keel: tulemuslikkus

Olen võistelnud kõrgeimal tasemel ja rakendan täna sama kriteeriumi andmetele: saan aru, mida number sportlase jaoks tähendab, sest olen ise see sportlane olnud.

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Töö

Projektid

Automaatne valik minu avalikest GitHubi repositooriumidest.

Spetsialiseerumised

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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