Which process involves cleaning, transforming, and formatting raw data for analysis?

The process of cleaning, transforming, and formatting raw data for analysis is known as data preparation. This step is crucial in the data analysis pipeline, as it ensures that the data is suitable for modeling and analysis.

Data preparation involves several tasks, such as removing duplicates, handling missing values, normalizing or standardizing data formats, and encoding categorical variables, among others. The goal is to enhance the quality of the data and facilitate accurate insights and predictions when applying analytical methods or machine learning algorithms.

Other processes mentioned in the choices focus on different aspects of data management. For instance, data privacy pertains to protecting personal information from unauthorized access or breaches, and data quality management involves maintaining data accuracy, consistency, and reliability over time. Meanwhile, a data science notebook typically refers to a programming environment where data scientists document their analyses and manipulations or run code, rather than a phase in preparing data itself.

Thus, data preparation stands out as the correct answer, as it specifically addresses the essential tasks associated with preparing raw data for effective analysis.