Plan J: AI Data Analysis / Research Report

Python / Code Interpreter

Handle missing values, outliers, duplicates, types and formats. Have the AI write cleaning scripts and output before/after stats.

💡Inspect distributions (describe / value_counts) before deciding how to treat missing values — don't blindly drop rows.

pandas + visualization

Look at distributions, correlations, group comparisons and trends; let the AI generate charts to surface patterns. This step yields the 'leads worth digging into'.

💡Correlation ≠ causation; describe the phenomenon first, then design a validation.

scikit-learn / statistical methods

Pick the method for the question (regression / clustering / significance test / prediction), split train/test, check metrics and watch for overfitting. Have the AI write the model code and explain the result.

💡Don't throw a complex model at a small sample; run a simple baseline first and add complexity gradually.

dataviz / matplotlib

Use KPI cards plus clear charts to tell what happened, why, and what it means. Keep axes and palette consistent to avoid misleading visuals.

💡Start axes at zero, label units and sample sizes; every chart needs a one-line takeaway, not silence.

ChatGPT / Claude

Package findings as 'conclusion + evidence + recommendation + limitations', stating assumptions and data limits explicitly.

💡Stating limitations and confidence beats overselling — it reads more credible, not less.