Giuseppe Lombardia
“Economics taught me models, Data Science taught me how to make them work.”
Data Scientist with an economics background.
Contacts: giuseppe97lomb@gmail.com · github.com/PeppeLomb17 · linkedin.com/in/giuseppe-lombardia-3490411ab/
Featured Project
Master’s Thesis — Global Epidemiological Transition (1990–2021)Analysis of the global epidemiological transition through the NCD/CD DALY ratio. Temporal clustering and statistical validations with pre and post COVID comparison.
Objective: identify robust and interpretable global epidemiological patterns.
Methods: K-Means, DTW, OLS with diagnostics, maps and interactive charts.
Main libraries: Pandas, NumPy, scikit-learn, KMeans, dtw-python (DTW), matplotlib, seaborn, Plotly, Statsmodels.
Projects
- Master’s Thesis in Data Science — Epidemiological Transition
- Sports Image Classification (MATLAB) — GoogLeNet and ResNet-18
- Heart Disease Statistical Learning (R) — LR, RF, NN
- COVID-19 Data Analysis (R) — statistical and multivariate analysis
- AI-UNICT 2024 — Challenge 2 (Python) — Image Classification (8 classes)
- NetLogo Welfare Model — agent-based welfare and taxation
Education
University of Catania · October 2021 – July 2025
The program provided multidisciplinary skills in data analysis, predictive modeling and statistical programming on real-world cases.
University of Catania · October 2016 – March 2021
Micro and macroeconomics, statistics and quantitative methods for decision-making.
Work Experience
June 2016 – Present
- Accounting management, tax procedures and cost analysis.
- Optimization of administrative processes and decision support.
Skills
Core
Python · Pandas · NumPy · scikit-learn · PyTorch · R
Data and Database
SQL (intermediate) · Advanced Excel · Power Query (basic)
Machine Learning and AI
Supervised Learning · Unsupervised Learning · Deep Learning · Feature Engineering · Model Evaluation
Visualization
Matplotlib · Plotly · Tableau · Seaborn
Other
Microsoft Office (Excel, Word, PowerPoint) · Docker (basic) · GitHub Actions (basic) · LaTeX