In the context of data science, what does 'data preparation' specifically refer to?

Data preparation is a critical step in the data science workflow that involves transforming raw data into a format suitable for analysis. This process encompasses various techniques necessary to make the data ready for model training and validation. Applying transformation and encoding techniques is a key aspect of this preparation.

Transformation techniques can include normalization, scaling, and aggregation, which all serve to standardize the data for better model performance. Encoding techniques, such as one-hot encoding or label encoding, convert categorical variables into a numerical format that machine learning models can process. This ensures that the information captured in the data is effectively utilized during model training.

By focusing on these transformation and encoding techniques, data preparation enhances the quality and usability of the dataset, ultimately leading to more accurate and reliable insights during the analysis phase.