Claude Code for Data Analysis: Practical Examples and Workflows

Data analysts and business intelligence teams are using Claude Code not just for writing Python scripts, but as a full analysis partner that understands data, suggests approaches, and generates insights. Here's how Claude Code fits into real data workflows, with practical examples. ## Why Claude Code for Data Analysis Traditional workflow: 1. Get data question from stakeholder 2. Write SQL or Python to explore data 3. Debug errors and edge cases 4. Create visualizations 5. Interpret results 6. Format for presentation 7. Repeat for follow-up questions With Claude Code: 1. Describe the question in natural language 2. Claude writes and executes the analysis code 3. Interprets results and suggests follow-ups 4. Generates presentation-ready outputs 5. Iterates based on findings You focus on business logic, Claude handles implementation. ## Core Data Analysis Workflows **1. Exploratory Data Analysis** You: "I have sales data in sales_data.csv. Give me an overview - what's in here, any quality issues, and what looks interesting?" Claude Code: - Reads the file - Generates summary statistics - Checks for missing values, duplicates, outliers - Creates distribution plots - Flags interesting patterns - Suggests analysis directions You get a complete EDA report without writing pandas boilerplate. **2. Cohort Analysis** You: "Analyze customer retention by signup month. Show me cohort retention curves and identify which cohorts have the best 6-month retention." Claude Code: - Structures data into cohorts - Calculates retention rates by time period - Generates cohort heatmap - Produces retention curves - Identifies high-performing cohorts - Suggests hypotheses for differences **3. Business Metrics Dashboards** You: "Create a weekly business review showing revenue, new customers, churn, and MRR trend. Highlight anything unusual." Claude Code: - Calculates metrics from raw data - Generates time series visualizations - Identifies anomalies and outliers - Adds context and interpretation - Exports formatted report Repeatable weekly with updated data. **4. Predictive Modeling** You: "Build a model to predict customer churn. Use our customer data and show me which features matter most." Claude Code: - Prepares data (encoding, scaling, splits) - Trains multiple models (logistic regression, random forest, gradient boosting) - Evaluates performance metrics - Generates feature importance analysis - Explains model predictions - Suggests improvements ## Real Examples from Data Teams **Sales Analytics Team:** *Use case:* Weekly pipeline analysis for sales leadership *Before Claude Code:* - 4 hours to pull data from CRM - 3 hours writing Python scripts for analysis - 2 hours creating visualizations - 1 hour formatting report - Total: 10 hours/week *With Claude Code:* - Export CRM data (same 4 hours - Claude can't help here) - "Analyze this pipeline data. Show deal velocity by stage, rep performance, and forecast accuracy. Flag deals at risk." - Claude generates complete analysis in 30 minutes - Review and refine: 30 minutes - Total: 5 hours/week *Savings:* 5 hours/week = 260 hours/year for one analyst **Product Analytics:** *Use case:* Feature adoption analysis for product decisions *Workflow with Claude Code:* 1. "Load user_events.csv and show me adoption of the new dashboard feature by user segment." - Claude generates adoption curves by segment 2. "The enterprise segment adoption is lower. Dig into why - are they not discovering it, or trying and abandoning?" - Claude analyzes funnel: discovery → trial → adoption - Shows where enterprise users drop off 3. "Create a presentation slide showing this analysis with key insights." - Claude generates matplotlib visualization with annotations - Adds bullet points of insights End-to-end analysis in one conversation instead of multiple scripts. **Finance Team:** *Use case:* Monthly variance analysis *Prompt:* "Compare this month's expenses to budget. Show me variances by department and flag anything over 10% off budget." Claude Code: - Reads actual vs budget data - Calculates variances - Creates variance report with conditional formatting - Highlights departments over threshold - Generates drill-down analysis for flagged items Turns 2-day manual process into 30-minute analysis. ## Advanced Workflows **Multi-Source Data Integration:** "I have sales data in PostgreSQL, customer data in Salesforce export, and product usage in events.parquet. Join them and analyze which customer segments have the highest lifetime value." Claude Code: - Connects to database and reads files - Handles different data formats - Joins on appropriate keys - Performs segmentation analysis - Generates LTV calculations by segment You don't write join logic or format conversion. **Time Series Forecasting:** "Forecast next quarter's revenue using the last 2 years of monthly data. Account for seasonality and show confidence intervals." Claude Code: - Loads and prepares time series data - Tests for stationarity and seasonality - Fits appropriate model (SARIMA, Prophet, etc.) - Generates forecast with confidence bands - Evaluates model accuracy on holdout data - Explains assumptions and limitations **A/B Test Analysis:** "We ran an A/B test on pricing page. Test data in experiment_results.csv. Tell me if there's a significant difference and what the expected impact is." Claude Code: - Performs statistical significance test - Checks sample size and power - Calculates effect size and confidence intervals - Projects business impact - Flags potential issues (imbalanced groups, outliers) ## Tools and Libraries Claude Code Uses **Data Manipulation:** - pandas for dataframes - numpy for numerical operations - polars for large datasets **Visualization:** - matplotlib for standard plots - seaborn for statistical visualizations - plotly for interactive charts **Statistics:** - scipy for statistical tests - statsmodels for regression and time series - scikit-learn for machine learning **Database Connectivity:** - sqlalchemy for SQL databases - pymongo for MongoDB - Various API clients You don't need to know which library to use - describe what you want, Claude picks the right tools. ## Best Practices **1. Start with Data Understanding** Before asking for analysis, have Claude examine the data: - "Describe this dataset - columns, types, sample values" - "Check for data quality issues" This helps Claude give better analysis. **2. Be Specific About Business Context** "Analyze revenue" is vague. "Analyze monthly recurring revenue, segment by customer size, and show growth rate trends" is specific. Context helps Claude make appropriate analysis choices. **3. Iterate on Findings** When Claude shows results, dig deeper: - "Why did revenue drop in Q2?" - "Which products drove that growth?" - "What customer segments churned most?" Claude can drill down without restarting. **4. Save Reusable Analysis as Scripts** Once you've refined an analysis: "Save this as a script I can run weekly with updated data" Claude generates a standalone Python script you can automate. **5. Validate Critical Results** For important decisions, verify Claude's analysis: - Check calculations manually for key numbers - Review statistical assumptions - Ensure joins and filters are correct Claude is very good but not infallible. ## Quick Takeaway Claude Code transforms data analysis from "write code to explore data" to "describe what you want to learn, get immediate analysis." Data teams use it for exploratory analysis, regular reporting, predictive modeling, and business intelligence - anywhere you'd previously write Python or SQL scripts. The value isn't just faster coding - it's the ability to iterate on questions naturally, get immediate feedback, and focus on interpretation rather than implementation. If you do any regular data analysis with Python, Claude Code belongs in your workflow.