Which sequence best represents the development process for data scientists on an experimentation platform?

The development process for data scientists on an experimentation platform follows a structured approach that ensures comprehensive understanding and effective problem-solving. The correct sequence starts with problem definition. This step is critical as it clarifies the objectives and the specific questions that need to be answered through the analysis.

Next is data selection, wherein the relevant datasets are identified based on the problem outlined earlier. This is followed by data exploration, which involves analyzing and understanding the data characteristics, identifying trends, relationships, and potential anomalies. This phase is crucial for informing the next steps in the process.

Feature engineering comes next, where relevant features are selected or created from the raw data to enhance the model's performance. This stage can significantly influence the quality of the models produced.

After feature engineering, model prototyping occurs, where preliminary models are built to test the ideas and hypotheses formed earlier. This is an important aspect of iteratively refining and improving model performance.

Finally, model validation is conducted to evaluate the effectiveness and accuracy of the models, ensuring they meet the defined objectives and perform well against the validation dataset. This step is essential for establishing confidence in the models before deployment.

This sequence incorporates systematic and logical steps that allow data scientists to iteratively refine their models while ensuring alignment with the original problem defined