This prompt helps data science teams identify suitable models for multi-class classification problems. It focuses on aligning the model’s capabilities with the dataset’s complexity, class distribution, and specific use case requirements.

This prompt helps data science teams choose visualization techniques to explore relationships between variables, focusing on uncovering correlations, trends, and interactions within the dataset.

This prompt helps data science teams create test cases for handling schema changes in datasets over time. It focuses on ensuring backward compatibility, identifying breaking changes, and validating the integration of new fields or data structures.

This prompt helps data science teams choose the most appropriate models for time-series forecasting. It focuses on understanding trends, seasonality, and other temporal features to select models that deliver accurate predictions while accommodating the dataset’s constraints.

This prompt helps data science teams choose visualization techniques for exploring relationships between three or more variables. It focuses on uncovering patterns, interactions, and trends in multivariate datasets.

This prompt helps data science teams choose visualization techniques for time-series data, focusing on uncovering trends, seasonality, and temporal relationships. It emphasizes clarity and insights for datasets with a temporal component.

This prompt helps data science teams translate complex statistical test results into clear, actionable insights for stakeholders with minimal technical background. It focuses on breaking down terminology, explaining significance, and emphasizing the real-world implications of findings.