Prompt Engineering for Data Scientists
AI prompt templates for data scientists: pandas code generation, statistical analysis, and data cleaning.
AI-Assisted Data Analysis
Generate Python/R scripts, explain complex algorithms, and automate data cleaning with structured prompts. The key difference between a vague prompt and a productive one is specifying the data schema upfront — include column names, data types, expected ranges, and known quality issues in your Situation block. This eliminates back-and-forth and produces code that runs on the first try.
Exploratory Data Analysis with STCO
Structure your EDA prompts by defining the Situation (dataset description, business question), Task (generate visualisations, calculate summary statistics, identify outliers), Constraints (use pandas and matplotlib, handle missing values with median imputation), and Output (commented Python code with inline explanations). This approach produces analysis scripts that are not only functional but also documented well enough for peer review.
Statistical Modelling and Validation
When prompting AI for statistical models, include the hypothesis you're testing, the assumptions you've already validated (normality, independence, homoscedasticity), and the evaluation metrics you care about. A well-constrained prompt like "build a logistic regression with cross-validation, report AUC-ROC and confusion matrix, flag any class imbalance" will produce production-quality code rather than a textbook example.
Data Cleaning and Pipeline Automation
Data cleaning consumes up to 80% of a data scientist's time. Use AI prompts to generate reusable cleaning functions by specifying exact transformation rules: how to handle nulls, string normalisation standards, date parsing formats, and deduplication logic. The output becomes a documented ETL module rather than a throwaway notebook cell.
FAQs
Should I trust AI-generated statistical code without review?
Never. AI can produce syntactically correct code with logically wrong assumptions — for example, applying a parametric test to non-normal data or silently dropping NaN rows. Always validate the statistical approach against your domain knowledge, check edge cases, and verify outputs against known baselines before using results in production or presentations.
How specific should my data prompts be?
As specific as possible. Include the dataframe schema (column names and types), sample rows if feasible, the exact library versions you're using, and any business rules that constrain the analysis. Vague prompts like "analyse this dataset" produce generic boilerplate; precise prompts produce usable code.
Can AI help with feature engineering?
Yes — AI is excellent at suggesting feature transformations when you describe the target variable and the raw features available. Prompt it with the prediction task, the feature list, and domain context (e.g., "this is retail transaction data, the target is churn within 30 days") to get meaningful feature ideas like recency-frequency-monetary aggregations.
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