Data analysis tool

The tool accepts a tabular dataset in CSV format and computes core analytics without writing code. It generates per-column descriptive statistics, flags data quality issues, and can compute pairwise correlations for numeric columns. This accelerates initial data profiling and prepares results for reporting. Conceptually, the system treats each numeric column as a candidate variable, computes central tendency and dispersion metrics, and cross-compares numeric columns to reveal relationships. Beneficiaries include researchers validating hypotheses, students learning statistics, and analysts performing quick data checks before deeper modeling. Unique value lies in delivering repeatable, export-ready summaries from raw data without scripting, while offering optional imputation and column filtering to tailor the analysis. Core features include: (1) descriptive statistics per column (mean, median, standard deviation, min, max, non-missing count); (2) data quality indicators (missing value counts, duplicate rows); (3) correlation matrix for selected numeric columns; (4) exportable outputs for downstream tools. Use cases include profiling a dataset of experimental measurements, classroom scores, or sales figures to surface key statistics and relationships. Outputs are structured to support automated reporting and reproducible analysis pipelines.