Analyze scientific data files
exploratory-data-analysisskillsetup L2★27,559
K-Dense-AI/scientific-agent-skills ↗What it does
Perform statistical analysis and visualization on datasets
Best for
Quickly understanding data distribution, correlations, and anomalies before modeling when you need visual and statistical summaries.
Inputs
- · CSV/Parquet/JSON data file
- · columns to analyze
- · analysis type (histogram, correlation, etc.)
Outputs
- · statistical summaries
- · matplotlib/plotly visualizations
- · anomaly reports
Requires
- · pandas
- · matplotlib/plotly
- · scipy/numpy for stats
Preconditions
Python 3.8+; data in standard tabular format; numeric/categorical columns identified
Failure modes
Missing values require imputation strategy; outliers may skew stats; large datasets (>1GB) slow in memory
Trust signals
- · Multi-plot visualization
- · Descriptive statistics
- · Correlation and PCA
- · Anomaly detection