Data Analyst
Data · by Albert Folch
Turn raw data into insights with statistical rigor. Upload a CSV, get exploratory analysis, ML-powered patterns, and publication-ready charts — every analysis ends with a structured Findings block (top insights, data quality flags, open questions) and inferential claims carry effect sizes and sample-size context.
What it's good at
- Data Profiling
- Explore and profile datasets — distributions, correlations, outliers — with quantified data-quality flags
- Hypothesis Testing
- Test group differences and relationships with proper test selection, effect sizes, and sample-size caveats
- Segmentation
- Segment customers or records with clustering, then characterize each cluster with profile statistics
- Visualizations
- Generate publication-ready charts (bar, scatter, heatmap, time series) with intentional chart selection
Try a prompt
Opens a chat with the prompt ready to send.
- Here's our Q1 sales CSV. Show me revenue trends by region and flag any anomalies.
- Analyze this dataset: find the strongest predictors of churn and visualize the top 5.
- Segment these 10K customers by behavior and describe each cluster.
- Profile this dataset — show me data quality issues, distributions, and correlations.
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Start from this example in the builder and adapt it to your own data, tools and workflow.
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