D

data analysis

Finds properties of Greenland fjords (e.g., length, width, bathymetry) and aggregates these along the length of the fjord at specified intervals (e.g., 10 km). Additional raster data can be aggregated along the fjord, such as sea-ice, chlorophyll-a, or turbidity.

See a snapshot of the fjords here: https://os.zhdk.cloud.switch.ch/spi-greenfjord-public/FjordProperties/fjord_polygons.html

Building a pandemic simulation program inspired by the model SIR (susceptible, infectious, removed).

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

memory.fm is a web application for exploring music listening history from Last.fm and Spotify.

An open-source data journalism platform for acquiring, processing, and analyzing leaked datasets and public records.

Projet académique de Master 2 en Python portant sur l’analyse de la qualité de l’air en France et en Île-de-France. Le projet combine scraping de données, traitement de données environnementales, analyse statistique des concentrations de polluants atmosphériques et visualisation cartographique des indices de qualité de l’air. Les analyses portent notamment sur les polluants NO₂, O₃ et PM10, avec une étude temporelle des données Airparif de 2014 à 2017.

Mots-clés :

python data-science web-scraping data-analysis data-visualization air-quality environmental-data geospatial-analysis time-series pandas

A modular, object-oriented notebook designed to perform deep-dive analysis on over 70 years of Formula 1® racing history. Moving beyond basic statistics, this project processes complex datasets to uncover insights into global sport expansion, circuit characteristics (including altitude), driver efficiency, and constructor dominance

Documentos referentes al curso de Introducción a la bioestadística y programación:

Hojas de cálculo Bioestadística Programas bioinformáticos Bases de datos biosanitarias Lenguajes de programación Lenguaje de programación R

The project focuses on developing a Hotel Revenue Optimization and Guest Analytics Dashboard using Power BI to analyze booking, customer, and room data. The approach began with data collection, cleaning, and transformation using Power Query to ensure accuracy and consistency. A star schema data model was created with booking data as the fact table and Date, Customer, Room, and Hotel Branch as dimension tables. Calculated columns such as booking duration, stay type, and lead time were added to enhance analysis. Key performance indicators like Occupancy Rate, Average Daily Rate (ADR), and Revenue Per Available Room (RevPAR) were developed using DAX to measure hotel performance. The project was implemented through modules including occupancy and revenue analysis, guest segmentation, forecasting and cancellation trends, and a revenue strategy dashboard. Interactive dashboards with filters and visuals were created to present insights clearly. Technologies used include Power BI, Power Query, DAX, and data modeling techniques. This project has real-world impact by helping hotel management improve pricing strategies, optimize occupancy, reduce cancellations, and make data-driven decisions.

DARTS/qemu-web-desktop: A web service that launches remote desktop virtual machines and displays them in your browser.

Analysis of Kilter Board data, along with predictive models for V-grades based on holds and angle.

Analysis of Tension Board 2 data, along with predictive models for V-grades based on holds and angle.

Dashboard Comercial desenvolvido do zero com modelagem relacional no DBeaver (SQL), pipeline de transformações no Power Query e visualizações interativas no Power BI. Projeto de estudo para portfólio.

Companion repository to the paper with the same title.

Fundamental theory and practice in Data Science (DS). 🧮

Projet d’analyse du comportement des utilisateurs Instagram à partir d’un large dataset synthétique (plus d’un million d’utilisateurs).

Le projet explore :

Analyse exploratoire des données (EDA) Prédictions de variables comportementales (stress, âge, clics publicitaires, revenu) Identification de profils utilisateurs avec clustering (KMeans) Interprétation des résultats statistiques en lien avec la vie réelle

Méthodologie : visualisation, corrélations, sélection de modèles, entraînement, évaluation et interprétation.

Technologies : Python, Pandas, NumPy, Matplotlib, Scikit-learn, Jupyter.

Projet réalisé dans le cadre de la formation Développeur en Intelligence Artificielle (Simplon).

Tracks and heatmaps using the GPS tracks.

Walkthroughs for Capture the Flag challenges on the HTB Cybersecurity Platform.