exploratory-data-analysis

Exploratory data analysis uncovers patterns, anomalies, and relationships in your data through statistical summaries, data quality checks, and visualizations. This Skill analyzes datasets across CSV, Excel, JSON, Parquet, TSV, Feather, HDF5, and Pickle formats end-to-end, generating insights and markdown reports automatically.

Data quality assessment identifies missing values, duplicates, outliers, and inconsistencies that affect analysis validity. This Skill performs automated quality checks as part of its statistical analysis workflow, flagging issues before visualization and report generation.

Yes. This Skill creates visualizations from analyzed datasets and compiles markdown reports that summarize statistical findings, data quality results, and visual insights, enabling you to share structured analysis output directly.

This Skill processes CSV, Excel, JSON, Parquet, TSV, Feather, HDF5, and Pickle formats, allowing you to run end-to-end analysis and visualization workflows on datasets regardless of their storage format.

Statistical summaries describe distribution, central tendency, and relationships within data; quality checks assess completeness, consistency, and anomalies. This Skill performs both as complementary steps in a structured exploratory workflow.