Hey there! I'm Barbara. I believe the keys to understanding our world are hidden in the everyday moments we often overlook. That is why I am passionate about exploring and analysing data to uncover hidden patterns and trends that transform information into valuable insights. My goal is to provide data-driven solutions to real-world problems and also help businesses make impactful decisions to maximize growth. My skillset spans across | Excel | PowerBI| SQL| Python| Connect with me on LinkedIn
I developed an interactive Power BI dashboard for AdventureWorks to track key metrics like sales, revenue, profit, and returns. Using raw CSV data, I built a relational model and applied DAX to calculate performance metrics. The dashboard provides insights into regional performance, product trends, and high-value customers, helping the management team make informed decisions.
Here, I demonstrated practical data cleaning techniques and advanced SQL functionalities for enhancing data quality and preparing datasets for analysis. I employed various SQL statements and also created CTE to standardize the data and perform several other data cleaning tasks.View documentation here
In this project, I analyzed employee attrition trends to uncover key factors influencing workforce turnover. Using Power BI for data visualization, I examined attrition by demographics, department, and salary range, providing actionable insights to improve employee retention strategies. View details here
I conducted a comprehensive Transborder Freight Analysis using Python on 5 years of U.S.–Mexico–Canada trade data, uncovering performance patterns and operational inefficiencies across billions of dollars in freight movement. The analysis quantified trade flows, freight charges, and estimated CO₂ emissions, enabling data-driven insights into cost drivers and efficiency gaps. I applied advanced data cleaning, feature engineering, visualization, and analytical reasoning to translate raw transportation data into actionable business intelligence. This project demonstrates my ability to analyze complex, real-world logistics data, uncover operational risks, and deliver insights that support strategic decision-making in supply chain, trade, and transportation operations.
In this project, I utilized Python to analyze data collected through supermarket membership cards, aiming to segment customers based on demographic characteristics. The analysis revealed insights such as the prevalence of single and male customers, educational patterns across occupations, and correlations between education levels and income. These findings were instrumental in crafting targeted marketing strategies to enhance customer engagement and satisfaction.
In this project, I established a comprehensive database to analyze Walmart sales data, aiming to identify top-performing branches and products, discern sales trends, and understand customer behavior. Utilizing SQL for detailed data analysis and Power BI for interactive visualizations, I uncovered key insights including peak sales periods, profitable product lines, and customer purchasing patterns. These findings informed actionable recommendations to optimize sales strategies and enhance overall performance. View documentation here