data-analysis

# Data Analysis This skill enables an AI agent to perform rigorous statistical analysis on structured datasets. The agent loads data, computes descriptive and inferential statistics, identifies trends and correlations, tests hypotheses, and produces actionable insights. It supports CSV, Excel, Parquet, and JSON inputs and leverages pandas, scipy, and statsmodels for analysis. ## Workflow 1. **Load and profile the data.** Read the dataset into a pandas DataFrame and inspect its shape, column types, and memory usage. Display the first and last rows to confirm the data loaded correctly. Check for obvious structural issues such as shifted columns or encoding problems. 2. **Compute descriptive statistics.** Generate summary statistics for all numeric columns including mean, median, standard deviation, skewness, and kurtosis. For categorical columns, compute value counts and mode. This step establishes a baseline understanding of each variable's distribution. 3. **Identify trends and patterns.** Apply rolling averages, percentage changes, and seasonal decomposition to time-indexed data. For non-temporal data, use group-by aggregations and pivot tables to surface patterns across categories. Flag any monotonic trends or cyclical behavior. 4. **Perform correlation and hypothesis testing.** Calculate Pearson and Spearman correlation matrices to quantify relationships between variables. Conduct hypothesis tests (t-tests, chi-square, ANOVA) where appropriate to determine statistica...