Which process converts data from one type to a coded value of a different type?

The process that converts data from one type to a coded value of a different type is known as Data Encoding. This involves taking raw data and transforming it into a specific format that is suitable for processing or storage, often simplifying or concealing the original data value in the process. Encoding is particularly useful when it involves categorical data that need to be represented numerically so that algorithms can utilize it effectively.

For instance, in machine learning, converting categorical variables such as 'Male' and 'Female' into numerical values (e.g., 0 and 1) helps algorithms to interpret the data correctly without ambiguity. This process is essential in preparing data for model building and ensures that the models can function with numeric input efficiently.

In contrast, the other processes mentioned are related but serve different purposes. Data Transformation refers to the broader scope of altering the structure or format of data, which can include encoding among other methods. Data Aggregation involves summarizing data, usually to provide insights at a higher level, whereas Data Extraction focuses on retrieving data from various sources. While these processes may intersect, they do not specifically address the conversion of data to coded values as Data Encoding does.