This project focuses on the critical task of cleaning and standardizing approximately 9,000 automotive parts records, which include units, subcomponents, armatures, drives, cores, and endplates. These records are spread across seven spreadsheets and require meticulous attention to detail to ensure consistency and accuracy. The goal is to streamline the data to facilitate efficient inventory management and enhance the e-commerce platform's functionality. Students will apply their classroom knowledge of data management and analysis to assign unique SKUs, ensuring each part is easily identifiable. Additionally, the project involves scraping or developing a scraper to gather market data, which will inform pricing strategies and assembly processes. This will include collecting information on pricing, conditions, and OEM cross-references, such as Bosch and Delco numbers. The project is designed to be completed in two parts, with the initial focus on data cleaning, allowing students to build a strong foundation before moving on to market data analysis.

The project deliverables include a comprehensive set of standardized automotive parts records, complete with unique SKUs for each item. Students will also deliver a functional data scraper or a detailed report on market data, including pricing, conditions, and OEM cross-references. These deliverables will be presented in a clear and organized format, ensuring they are ready for integration into the e-commerce platform. The final submission will demonstrate the students' ability to manage and analyze large datasets effectively.