Welcome to My Portfolio

Overview

I’m Emilio Nahuel Pattini, a Data Analyst and Web Developer passionate about transforming complex datasets into actionable business insights and building responsive, accessible websites.

This portfolio showcases my dual expertise through projects like the Olist Brazilian E-Commerce Analysis (RFM segmentation, cohort retention, revenue forecasting with Prophet, interactive Power BI dashboard) and the Ecobici 2024 Urban Mobility Analysis, as well as this very website.

Olist Brazilian E-Commerce Analysis: RFM segmentation, cohort retention, Prophet forecasting, and interactive Power BI dashboard with actionable business insights.

Ecobici 2024: Urban Mobility Analysis focusing on bike-sharing usage patterns and station performance.

Personal Portfolio Website: Responsive design, interactive components, and polished branding.

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Skills Highlighted

• Data Analysis: Python (pandas, numpy, seaborn, plotly, prophet), R (tidyverse, lubridate, leaflet), SQL (DuckDB, MySQL, PostgreSQL, DB2), RFM segmentation, cohort analysis, forecasting (Prophet), statistical methods
• Web Development: HTML5, CSS3, JavaScript (interactive components, responsive design), WordPress, multilingual content (EN/ES/IT)
• Visualization & Dashboarding: Power BI (KPIs, Gauges, scatter, heatmap, bubble maps), Plotly (interactive plots), Tableau, Matplotlib, Seaborn, R Markdown
• Workflow & Communication: data cleaning & feature engineering, reproducible reporting (Jupyter, RMarkdown), business storytelling, actionable recommendations
• Languages: Spanish (Native), English (Fluent), Italian (Intermediate), Portuguese (Basic)

Featured Projects

Olist Brazilian E-Commerce Analysis

Full end-to-end analysis: RFM segmentation, cohort retention, revenue forecasting with Prophet, interactive Power BI dashboard. Key result: identified 97% one-time buyers and massive retention opportunity.

Ecobici 2024 Urban Mobility Analysis

An in-depth analysis of urban bike-sharing data to understand usage patterns, peak times, and station performance. This project involved cleaning large datasets, performing exploratory analysis, and visualizing key trends to inform urban planning decisions.