소개
저는 UPV에서 데이터 과학을 공부하고 프로 사이클링 팀에서 실제 경험을 쌓은 마리오 알바레스입니다. 데이터 분석과 엘리트 스포츠 성과를 결합하여 모든 지표를 결과를 개선하는 결정으로 바꿉니다.
더 보기UPV
학력
발렌시아 공과대학교(Universitat Politècnica de València) 데이터 과학 학사.
엘리트
경력
프로 사이클링 팀에서의 실무 경험.
Data + Sport
접근 방식
스포츠 성과 향상에 적용된 분석.
작업
GitHub의 공개 리포지토리 자동 선별.
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.
GitHub에서 보기This 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.
GitHub에서 보기An 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.
GitHub에서 보기A 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.
GitHub에서 보기Developed 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.
GitHub에서 보기
