Publications

Mortgage Trading Analysis

This project focuses on analyzing mortgage trading data using Power BI, applying various data analytics techniques to uncover insights into market trends, trading behaviors, and financial performance. Throughout the project, I have leveraged a combination of skills, including data modeling, data transformation with Power Query, and advanced DAX calculations, to structure and interpret the data effectively. The dataset includes detailed information on mortgage transactions, interest rates, trading volumes, and other financial metrics. I started by Leer más…

Por Valentino Agazzi Mandozzi, hace

Analyzing Healthcare Data

In this project, I analyzed real hospital discharge data from New York State, categorized by county and hospital. The goal was to understand the factors contributing to extended hospital stays, which in turn increase healthcare costs. After cleaning and preparing the dataset, I developed interactive Power BI dashboards to visualize key metrics such as length of stay, patient demographics, diagnosis types, and treatment procedures. The analysis revealed that patients with more severe conditions and those Leer más…

Por Valentino Agazzi Mandozzi, hace

Atlas Lab | Employee Overview

Atlas Lab is a fictitious software company. In this project, I analyzed data provided by the HR Department with the aim of understanding the factors leading to employee turnover. After cleaning and preparing the data, I created interactive Power BI dashboards to visualize key metrics like attrition rates, job satisfaction, and work-life balance. The analysis revealed that certain job roles and employees with low satisfaction scores had higher attrition rates. Employees with less than three Leer más…

Por Valentino Agazzi Mandozzi, hace

How Public Expenditure Affects Unemployment

In this article, I developed a linear regression model to help us understand how different public expenditure programs implemented by the government affect labor market dynamics, particularly the unemployment rate. The model was built using Python, primarily with the statsmodels library, along with other supporting libraries such as pandas and numpy, which were essential for cleaning and manipulating the data to prepare it for modeling. Moreover, the model was corrected for heteroscedasticity and autocorrelation, ensuring Leer más…

Por Valentino Agazzi Mandozzi, hace

Climate Change Explained

This article reviews the most reliable data on climate change and transforms it into information through a wide and varied set of visualizations created using Python. I utilized matplotlib and seaborn, along with supporting libraries such as pandas, numpy, and pycountry, to clean and prepare the data for analysis. Throughout the article, I honed my storytelling and data communication skills, making it accessible for readers with no prior knowledge to fully grasp the fundamentals of Leer más…

Por Valentino Agazzi Mandozzi, hace