Claude accelerates every phase of the data science workflow — from initial exploration to final presentation. It writes analysis code, suggests statistical approaches, generates visualizations, and helps translate technical findings into business language. The 200K context window means you can feed it entire datasets, notebooks, and documentation in a single conversation.
Describe your dataset and Claude generates a systematic EDA plan with code. It checks distributions, correlations, missing data patterns, and potential data quality issues.
Example prompt:
I have a dataset with these columns: [COLUMNS WITH TYPES]. Write a complete EDA in Python (pandas + matplotlib). Check distributions, correlations, missing patterns, and outliers. Highlight anything unusual.
Describe your research question and Claude recommends the appropriate statistical test, explains the assumptions, writes the code, and interprets the results in plain English.
Example prompt:
I want to know if [TREATMENT] significantly affects [OUTCOME]. Groups: [DESCRIPTION]. Which statistical test should I use, why, and what are the assumptions? Write the code and interpret the results.
Claude helps you choose the right model for your problem, explains the tradeoffs between approaches, and writes evaluation code with proper cross-validation and metric selection.
Example prompt:
I'm predicting [TARGET] from [FEATURES]. Data: [SIZE] rows, [CLASS DISTRIBUTION]. Compare: logistic regression, random forest, and gradient boosting. Write code for training, cross-validation, and evaluation. Which model should I choose and why?
Claude generates publication-quality visualization code and helps you choose the right chart type for the story you want to tell. It considers audience, message, and design principles.
Example prompt:
I need to present these findings to executives: [FINDINGS]. Create 4 visualizations in matplotlib/seaborn that tell the story clearly. Use appropriate chart types, clean styling, and clear labels.
Claude writes complex SQL queries, optimizes slow ones, and explains query execution plans. It handles joins, window functions, CTEs, and performance tuning.
Example prompt:
This query is running slowly on a 50M row table: [QUERY]. Explain why it's slow, suggest index strategies, and rewrite it for better performance. Database: PostgreSQL.
Claude translates technical findings into executive summaries, slide narratives, and stakeholder-appropriate language. It knows which details to include and which to leave out.
Example prompt:
Translate these technical findings into an executive summary: [FINDINGS]. Audience: non-technical C-suite. Focus on business impact, recommended actions, and confidence level. Keep it to one page.