A passionate Data Analyst who love to transform data into actionable insights with reports and dashboards to help business make data-driven informed decisions. My journey began with a foundation in graphics design, where I cultivated an eye for aesthetics and the power of visual communication. Driven by a curiosity for uncovering insights, I transitioned into the world of data analytics, armed with proficiency in Excel, SQL, Tableau and Python. Eager to apply my analytical and problem-solving skills to address real-world challenges and deliver effective solutions.
This project involves Identify key factors influencing customer churn, determine the high-risk segments and Develop data-driven recommendations for the financial institution to reduce churn, improve customer retention, and increase overall customer.
Tool used: Excel (power query, pivot tables and data visualization)
PostgreSQL was used to perform data cleaning and exploratory data analysis on six datasets (tables) in other to provide effective data insights about the customers to the stakeholders of the business.
Some business questions were answered to get insights on the business sales.
This project explores global trends and patterns in intentional homicides using data sourced from the United Nations Office on Drugs and Crime (UNODC). The analysis examines key indicators such as the total counts of victims across different regions, demographic breakdowns (gender, age), and global variations. Through this data-driven approach, I identified critical insights into which regions are most affected and highlighted demographic trends over time.
Performed an exploratory data analysis on a Prosper Loan Data gotten from Udacity to explore and convey findings through visualizations. Data wrangling was performed on a subset dataset of 77,543 loans with 20 variables using Python libaries (pandas, matplotlib, numpy and seaborn libraries).
Recommendations was given to help optimize the loan's data and provided a better scheme of loan's to the customer's.
This project involves analyzing a dataset of financial consumer complaints to uncover insights into customer grievances and company responses. Using SQL, the data was cleaned, transformed, and analyzed to identify trends in consumer complaints across different categories.
