data-science-analytics

name	data-science-analytics
description	Master data analysis, visualization, and insights extraction.

Data Science & Analytics

Quick Start

Pandas Data Analysis

import pandas as pd

# Load data
df = pd.read_csv('data.csv')

# Explore
df.head()
df.info()
df.describe()

# Filter
df[df['age'] > 30]

# Group
df.groupby('category')['value'].sum()

# Aggregate
df.agg({'value': 'sum', 'count': 'mean'})

Data Visualization

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.scatterplot(data=df, x='age', y='income')
plt.title('Age vs Income')
plt.show()

Analytics Process

1. Question Definition: What to analyze?
2. Data Collection: Gather relevant data
3. Data Cleaning: Remove outliers, handle missing
4. Exploration: Understand patterns
5. Analysis: Statistical tests
6. Visualization: Create insights
7. Communication: Present findings

Statistical Analysis

• Descriptive Stats: Mean, median, std dev
• Hypothesis Testing: T-tests, chi-square
• Correlation: Pearson, Spearman
• Regression Analysis: Linear, logistic

Tools

• Pandas: Data manipulation
• NumPy: Numerical computing
• Matplotlib: Visualization
• Seaborn: Statistical visualization
• Plotly: Interactive dashboards

Best Practices

✅ Ask clear questions ✅ Validate assumptions ✅ Use appropriate visualizations ✅ Test hypotheses ✅ Document methodology ✅ Avoid misleading charts ✅ Cite data sources