Systems Analysis & Development · Data Engineering · Cloud Computing

Focused on learning Python, SQL, and AWS through hands-on projects. Building a strong foundation in data and cloud technologies.

I'm looking for my first internship in the technology field — specifically in Data Analysis, Data Engineering, or Cloud Computing roles — where I can contribute with structured thinking, clean code, and a commitment to best practices, while learning rapidly from experienced teams.

Languages

Data & Analysis

Cloud & Infrastructure

Version Control & Workflow

An end-to-end data pipeline extracting, cleaning, and visualizing football match events using the StatsBomb API and spatial coordinate mapping.

Stack:  

📥 Step 1 — API Consumption & Extraction

• Queried and filtered raw event data directly from the StatsBomb API, isolating specific matches from the Bundesliga.

🔍 Step 2 — Data Treatment & Feature Engineering

• Processed complex nested data structures using Pandas, extracting and splitting location arrays into structured x and y spatial coordinates.

💡 Step 3 — Spatial Visualization

• Rendered a digital pitch using mplsoccer and mapped shot locations to identify offensive patterns and spatial dominance.

🔗 View Repository on GitHub →

A complete data analysis project exploring the 2022 FIFA World Cup through Python and Jupyter Notebook — from raw CSV to structured insights.

Stack:  

📥 Step 1 — Data Collection & Preparation

• Loaded raw CSV datasets and cleaned them with Pandas — handling nulls, fixing data types, and standardizing column names

🔍 Step 2 — Exploratory Data Analysis (EDA)

• Investigated match stats (goals, attendance, team rankings) using Jupyter Notebook as a single reproducible environment combining code, visuals, and narrative

💡 Step 3 — Insights & Conclusions

• Extracted key findings on top-scoring teams and match patterns, structured as a clear data story from raw input to final conclusions

🔗 View Repository on GitHub →