data-science-expert

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---
name: data-science-expert
version: 1.0.0
description: Expert-level data science, analytics, visualization, and statistical modeling
category: ai
tags: [data-science, analytics, visualization, statistics, pandas, numpy]
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash(python:*)
---

# Data Science Expert

Expert guidance for data science, analytics, statistical modeling, and data visualization.

## Core Concepts

### Data Analysis
- Exploratory Data Analysis (EDA)
- Data cleaning and preprocessing
- Feature engineering
- Statistical inference
- Time series analysis
- A/B testing

### Machine Learning
- Supervised learning (classification, regression)
- Unsupervised learning (clustering, PCA)
- Model selection and validation
- Feature importance
- Hyperparameter tuning
- Ensemble methods

### Data Visualization
- Matplotlib, Seaborn, Plotly
- Statistical plots
- Interactive dashboards
- Storytelling with data
- Best practices for visualization
- Color theory and accessibility

## Data Cleaning and EDA

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from typing import Dict, List

class DataCleaner:
    """Clean and preprocess data"""

    def __init__(self, df: pd.DataFrame):
        self.df = df.copy()
        self.cleaning_log = []

    def handle_missing_values(self, strategy: str = 'drop',
                             fill_value=None) -> pd.DataFrame:
        """Handle missing values"""
        missing_before = self.df.isnull().sum().sum()

        if strategy == 'drop':
            self.df = self.df.dropna()
        elif strategy == 'fill':
            if fill_value is not None:
                self.df = self.df.fillna(fill_value)
            else:
                # Fill numeric with median, categorical with mode
                for col in self.df.columns:
                    if self.df[col].dtype in ['float64', 'int64']:
                        self.df[col].fillna(self.df[col].median(), inplace=True)
                    else:
                        self.df[col].fillna(self.df[col].mode()[0], inplace=True)

        missing_after = self.df.isnull().sum().sum()
        self.cleaning_log.append(f"Missing values: {missing_before} -> {missing_after}")

        return self.df

    def remove_duplicates(self) -> pd.DataFrame:
        """Remove duplicate rows"""
        before = len(self.df)
        self.df = self.df.drop_duplicates()
        after = len(self.df)

        self.cleaning_log.append(f"Duplicates removed: {before - after}")
        return self.df

    def remove_outliers(self, columns: List[str],
                       method: str = 'iqr',
                       threshold: float = 1.5) -> pd.DataFrame:
        """Remove outliers"""
        before = len(self.df)

        for col in columns:
            if method == 'iqr':
                Q1 = self.df[col].quantile(0.25)
                Q3 = self.df[col].quantile(0.75)
                IQR = Q3 - Q1

                lower = Q1 - threshold * IQR
                upper = Q3 + threshold * IQR

                self.df = self.df[(self.df[col] >= lower) & (self.df[col] <= upper)]

            elif method == 'zscore':
                z_scores = np.abs(stats.zscore(self.df[col]))
                self.df = self.df[z_scores < threshold]

        after = len(self.df)
        self.cleaning_log.append(f"Outliers removed: {before - after}")

        return self.df

class EDA:
    """Exploratory Data Analysis"""

    def __init__(self, df: pd.DataFrame):
        self.df = df

    def summary_stats(self) -> pd.DataFrame:
        """Generate summary statistics"""
        return self.df.describe(include='all').T

    def correlation_analysis(self, method: str = 'pearson') -> pd.DataFrame:
        """Calculate correlation matrix"""
        numeric_cols = self.df.select_dtypes(include=[np.number]).columns
        return self.df[numeric_cols].corr(method=method)

    def plot_distributions(self, columns: List[str] = None):