MATLAB Data Analysis

Generate idiomatic MATLAB code for tabular data analysis tasks using tables and timetables.

When to Use

• Any task involving tabular data: exploring, cleaning, transforming, or aggregating tables
• Time-series analysis: resampling, synchronizing, trend detection, smoothing
• Answering questions about data in tables (top-N, filtering, group comparisons)
• Data cleaning: missing values, outliers, type conversion, normalization

When NOT to Use

• The task has no tabular data context (no tables, timetables, or structured datasets)
• The primary goal is visualization or plotting, not data analysis
• The task is purely symbolic math, simulation, or app building

This skill covers core MATLAB functions for tabular and time-series workflows. These functions work natively with table and timetable, handle missing data correctly, and are performance-optimized. Prefer the modern functions recommended here (e.g., groupsummary, datetime, fillmissing) over legacy alternatives (e.g., accumarray, nanmean, datenum). Override only if the user explicitly requests otherwise.