# Matlab Data Analysis

> MATLAB R2026a data analysis workflow for tables, timetables, statistics, fitting, optimization-assisted analysis, visualization, report artifacts, and reproducible paper figures. Use whenever the user asks MATLAB to analyze data, plot results, fit models, process CSV/Excel/MAT files, or reproduce figures.

- Skill: `wzyn20051216/matlab-data-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add wzyn20051216/matlab-data-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/wzyn20051216/matlab-data-analysis/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: wzyn20051216 (https://skillmd.com/u/wzyn20051216)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/wzyn20051216/matlab-data-analysis

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# MATLAB Data Analysis

Use this skill for MATLAB analysis pipelines that start with data and end with validated numbers, figures, or reports.

## Workflow

1. Inventory input files and schemas before coding.
2. Load data with typed APIs: `readtable`, `readtimetable`, `matfile`, `datastore`, or toolbox importers.
3. Normalize units, time zones, missing values, categorical fields, and outliers explicitly.
4. Build analysis as functions plus a thin runner script.
5. Save clean data, figures, and summary metrics under `artifacts/`.
6. Validate with shape checks, range checks, and at least one numerical assertion.

## API Preferences

- Tables and timetables for labeled data.
- `groupsummary`, `rowfun`, `varfun`, `synchronize`, and `retime` for structured transformations.
- `fitlm`, `fitnlm`, `fitrgp`, `fitcsvm`, or `fitcensemble` only after checking toolbox availability.
- `optimproblem` or solver APIs for constrained fitting when simple regression is not enough.
- Export figures with explicit size, resolution, and format.

## Reproducible Figures

Every reproduced paper figure should include:

- Source data provenance.
- Script name and command used.
- Random seed if applicable.
- Axis labels, units, legend, and saved image path.
- A simple metric comparing reproduced result with reference when possible.

## Validation

Use checks such as:

```matlab
assert(height(T) > 0)
assert(all(isfinite(metrics.rmse)))
assert(isfile(fullfile(outDir, "figure_1.png")))
```

Do not accept a plot as "done" unless the underlying numerical summary also looks sane.