“data analysis”

Data analysis and visualization.

High-performance data analysis using Polars - load, transform, aggregate, visualize and export tabular data.

Profile and aggregate a bundled sales dataset - group revenue by region, category, channel or month, apply filters, and compute sums, means, medians and ranges.

Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables…

End-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures.

Specialized instructions for exploratory data analysis, statistics, and pandas processing

End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures.

Scientific data analysis including data cleaning, exploratory data analysis (EDA), statistical testing, regression, and reporting.

Handle statistical data analysis tasks: t-tests, regression, correlation analysis, data visualization, CSV processing, hypothesis testing, effect size…

Use on Desktop when profiling, cleaning, comparing, or analyzing local CSV, TSV, JSON, or spreadsheet-like data with reproducible evidence.

Analyze structured data or create visualizations from data.

对 CSV、Excel、ZIP 或其他附件数据执行可复现的数据清洗、统计、建模、图表和报告工作；用户要求 data science 报告、探索性分析、预测或从数据生成交付文件时使用。

Practical workflow for exploring tables, CSV, JSON, logs, or survey data — sanity checks, aggregation, descriptive statistics, and honest reporting of patterns.

This is default workflow for data analysis.

Analyse tabular or structured data with short sandboxed Python scripts and report verified figures.

Use for exploratory analysis, statistical analysis, trends, cohorts, segmentation, variance drivers, forecasting inputs, analytical visualisation or decision…

Answer a business question from a database, warehouse or spreadsheet: SQL, EDA, a decision memo.

Analyses a dataset correctly and reports what it shows.

Analyze archived public data reproducibly with provenance and uncertainty.

Explore, clean and summarize tabular data (CSV/XLSX/SQL) and report findings.

用于数据分析任务，提供统计方法和可视化建议

分析上传的 CSV，生成可复查的结论、图表和报告

Analyse a dataset like a data scientist — profile it, find the signal, explain it.

Use when analysing tabular data — CSV, Excel or JSON — computing aggregates, finding anomalies, cleaning messy data, or answering questions about a dataset…