What is the correct order for the five-step process in the data science life cycle?

The five-step process in the data science life cycle begins with gathering data. This step involves collecting relevant data from various sources, which serves as the foundation for further analysis. Following data collection, cleaning the data is essential to ensure its integrity and quality by removing inaccuracies, duplicates, or irrelevant information.

The next step is exploring the cleaned data. This exploratory analysis helps in understanding the data's structure, patterns, and anomalies, which informs subsequent analysis. After exploring the data, modeling it becomes necessary, where statistical techniques or machine learning algorithms are applied to draw insights or make predictions. Finally, interpreting the data allows data scientists to extract meaningful findings and insights that can inform decisions or strategies.

This sequence reflects a logical flow, progressing from raw data collection to analysis and insight generation, serving as a systematic approach to tackling data-driven problems. The other options do not follow the correct order of these essential steps in the data science life cycle.