Data on the 11,500+ athletes and 306 events at the Rio Olympics. Includes medals tallies

Find out which countries have won the most medals and how the participation of nations has changed over time, with R

Olympic Games Module for MagicMirror²

A database with olympic games data from 1896 to 2016, built for postgrsql.

This is an Exploratory Data Analysis project to analyze the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016.

A Project that I developed with my partner AymenMog, as our first C project, if you are just beginning with C check it !

Olympics Data Analysis using R

Using data taken from the modern era of the Olympic games we were able to perform certain analysis to give insight into correlations and numerical patterns regarding the participants of these sporting events.

東京2020オリンピック・パラリンピックの開催前に、日本オリンピックミュージアムで展示された聖火。撮影日：2020年9月12日 【場所：東京都新宿区】

The documentation content for the jopedia Antora example

Visualization of historical data about the Olympic Games

Analysis of historical Olympic performance and GDP and population. Using Python: Pandas, Flask, SQLite; JavaScript: Leaflet, Highcharts, D3; HTML. Deployed using Heroku.

Assignment for Big Data Processing: A collection of programs for analysing tweets related to the 2012 Olympics.

Défiez vos amis aux jeux olympiques !

An interactive Olympic Data Dashboard designed to visualize country performance, athlete demographics, and medal distribution using dynamic charts and maps. Built with Power BI, this dashboard showcases insights from the 2024 Olympic Games, making data exploration engaging and accessible

[BUT S2] Exploitation de BDD

Exploratory Data Analysis of Olympic games, using the Seaborn library. The first step was to clean the data, then to examine it and finally to visualize the data in order to make the necessary conclusions.

Python visual analysis of the Olympic Games history. Kaggle gold medal with 15000+ views, 200+ upvotes and 100+ comments.

An interactive data visualization built with Shiny and Plotly R. For the course Interactive Data Visualization (offered by University of Helsinki's Master's Programme in Data Science), spring 2021.

The visualisation has used preattentive attributes such as shape, size, orientation, colour, the position and Gestalt principles such as proximity, similarity, continuity throughout the process. For the final analysis, all the three graphs were connected together. In the first graph, the continuity principle was used to show the trend in the par…