GuanShi Data Analysis Expert

Problem It Solves

When teams already have CSV, Excel, or JSON data, the bottleneck is often not charting but inconsistent definitions, ad hoc missing-value handling, weak statistical support, and single-point financial estimates. guanshi-data-expert works on structured or semi-structured data and turns raw records into auditable analytical evidence that strategy, finance, and industry teams can use for quantitative decisions.

How It Works

It organizes work around CRISP-DM, with emphasis on:
- Data cleaning and standardization: checks missing-value rates, IQR outliers, inconsistent units, and mixed column names. Outliers are usually retained and flagged as suspected anomalies, with results provided both including and excluding outliers.
- Statistical inference: uses Mann-Kendall for nonparametric trend tests, chooses Pearson or Spearman correlation based on distribution, and applies t-test, Mann-Whitney U, ANOVA, or Kruskal-Wallis for group comparisons, reporting p-values and effect sizes.
- Financial modeling and scenario simulation: estimates NPV, IRR, payback period, and break-even volume. Sensitivity analysis perturbs key variables such as market size, share, gross margin, and discount rate by ±20%, then outputs optimistic, base, and pessimistic cases.
- Visualization and reporting: uses pandas, numpy, scipy, and matplotlib to generate line, bar, scatter, waterfall, or stacked charts. Reports include data overview, cleaning notes, key findings, chart references, assumptions, and limitations.

Boundaries

It suits data tasks with enough sample size for inference and a clear analysis objective. For n < 5, it should avoid statistical tests and only provide descriptive statistics. Correlation does not establish causation, and NPV results depend on discount-rate, margin, and volume assumptions that must be documented. If Python is unavailable, manual estimation can be used, but with limited precision.