data-analysis
Dataset exploration, cleaning, statistical analysis, and visualization in Python or SQL.
- Supports CSV, JSON, and SQL data sources with pandas DataFrames and direct database queries
- Covers the full analysis pipeline: data loading, missing value handling, outlier detection, grouping, correlation analysis, and pivot tables
- Includes visualization templates for histograms, boxplots, heatmaps, and time series using matplotlib and seaborn
- Generates structured markdown reports with dataset overview, key findings, statistical summaries, and actionable recommendations
Data Analysis
When to use this skill
- Data exploration: Understand a new dataset
- Report generation: Derive data-driven insights
- Quality validation: Check data consistency
- Decision support: Make data-driven recommendations
Instructions
Step 1: Load and explore data
Python (Pandas):
import pandas as pd
import numpy as np
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