
Data Cleaning & Standardization
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I transform messy, unreliable spreadsheets into clean, standardized datasets. As the founder of Mississippi Data Works, my core focus is taking raw CSV and Excel files—often plagued by missing values, broken columns, and formatting errors—and using Python to engineer them into pristine, functional files. If your business relies on spreadsheets that are currently too disorganized to use, I provide the precise formatting needed to make your data reliable again.
While I am in the early stages of my dedicated data science career, I bring a strong, structured foundation to my work. I am currently advancing my technical expertise through a Master’s degree program in Data Science, with a concentration in AWS Cloud Computing. This rigorous academic environment ensures that my Python coding is built on modern best practices. I approach data cleaning not just as a manual task of finding and replacing text, but as a programmatic process. By writing custom Python scripts to handle your CSV and Excel files, I can automate the cleanup of repetitive errors, ensuring a level of consistency and accuracy across thousands of rows that would be impossible to guarantee by hand.
My technical skills are grounded in the demanding reality of the financial services sector. With extensive professional experience in loan processing and loan operations, I understand that data is rarely just numbers on a screen; it represents real-world financial constraints and strict regulatory compliance. In the financial sector, a single formatting error, duplicate record, or misplaced decimal can have significant consequences. This daily reality has trained me to spot the critical anomalies that others miss and to treat data integrity as a non-negotiable standard.
Beyond simply cleaning rows and columns, I understand how data needs to function in the real world. I have practical experience writing Python code to solve specific financial problems, including building and testing custom calculator applications designed to estimate complex metrics like interest rates and Truth in Lending Act (TILA) restrictions. This hands-on problem-solving experience means I know exactly how to structure your Excel and CSV files so they are ready for immediate analysis or reporting.
Whether you have a single chaotic Excel workbook or a batch of disorganized CSV files, I have the discipline to deliver a clean, standardized product.
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The Problem: Manual auditing of financial constraints—such as APR limits and doc prep fee structures—is highly susceptible to human error and creates massive operational bottlenecks in legacy banking systems. The Solution: I architected and deployed a custom Python-based compliance engine that fully automates this process and runs dynamic mathematical logic loops to instantly calculate compliance, including complex edge cases like automated "fee haircuts." Technical Architecture & Deployment: • Engineered the backend logic using Python and structured the variables to handle dynamic loan inputs. • Built a modern, mobile-responsive Graphical User Interface (GUI) using Streamlit for cloud-hosted access. • Compiled the application into a standalone executable (.exe) file. The Impact: Replaced a manual, multi-step calculation process with an instant, automated script. This drastically reduced queue processing time, eliminated manual calculation errors, and guaranteed strict regulatory compliance across all processed files.
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