Which step is NOT part of the data science project lifecycle?

Model optimization is indeed a key aspect of the data science project lifecycle. However, it can sometimes be viewed differently than the other steps mentioned in the choices. While data collection, data preprocessing, and exploratory data analysis are fundamental steps that focus on preparing and understanding the data, model optimization is more about refining and enhancing a model after it has been built.

Data collection involves gathering relevant data from various sources, which is essential to ensure that the project has a strong foundation. Data preprocessing follows, where the collected data is cleaned and transformed to make it suitable for analysis. Exploratory data analysis is critical for understanding the underlying patterns and insights in the data, shaping the direction of further analysis.

Model optimization, while important in the context of building effective models, is typically considered a later stage of the project that follows initial modeling and evaluation. Therefore, it fits less neatly within the core steps of the initial phases of a data science project lifecycle.