👉 Conversion engineering is the process of transforming data from one format or structure to another, enabling it to be utilized more effectively in various applications. This involves understanding the source data's characteristics and requirements, then applying appropriate techniques to convert it into a desired format or structure. This could include data normalization, transformation of data types, encoding categorical variables, or even converting between different data models (e.g., relational to NoSQL). The goal is to enhance data usability, improve model performance, and facilitate integration across different systems or platforms. Conversion engineering plays a crucial role in data science workflows by ensuring that data is consistent, accessible, and optimized for analysis or machine learning tasks.
