Data Analysis¶

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Practical, reproducible workflows for data analysis from start to finish.

Data analysis done right combines solid methodology with practical tools. This section provides battle-tested approaches that help you extract meaningful insights efficiently and communicate them effectively.

🎯 Goals¶

• Reproducible workflows that work consistently across projects
• Practical methodologies for exploratory data analysis (EDA)
• Tool mastery for pandas, visualization, and statistical analysis
• Clear communication of findings and recommendations

🔍 Quick Verification¶

Test your data analysis environment:

# Check Python and key libraries
python -c "import pandas as pd; import matplotlib.pyplot as plt; import seaborn as sns; print('✅ Data analysis tools ready')"

# Check Jupyter setup
jupyter --version

# Verify sample dataset access
python -c "import pandas as pd; df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv'); print(f'✅ Sample data loaded: {df.shape}')"

📚 Essential Guides¶

Methodology & Process¶

• Methodology - EDA fundamentals and systematic approach to data exploration
• Data Cleaning - Handle missing data, outliers, and quality issues
• Feature Engineering - Create meaningful variables for analysis

Tools & Techniques¶

• Pandas Quick Cheats - Fast reference for common data manipulation tasks
• Visualization Principles - Create clear, impactful charts and graphs
• Communication of Insights - Present findings that drive decisions

Advanced (Optional)¶

• Metrics & Experiments - A/B testing and statistical significance (coming soon)

🚀 Common Workflows¶

New Dataset Exploration¶

1. Load & inspect → Pandas Quick Cheats
2. Clean & validate → Data Cleaning
3. Explore patterns → Methodology
4. Visualize findings → Visualization Principles
5. Communicate results → Communication of Insights

Typical Analysis Pipeline¶

# 1. Load and inspect
df = pd.read_csv('data.csv')
df.info()
df.describe()

# 2. Clean and prepare
df_clean = df.dropna().copy()
# ... cleaning steps

# 3. Explore and analyze
# ... EDA and statistical analysis

# 4. Visualize key findings
# ... create charts

# 5. Document insights
# ... write summary

🔗 Related Sections¶

• Python - Environment setup and notebook workflows
• Databases - SQL queries and data extraction
• Toolkit - Jupyter, pandas, and visualization tools

⚡ Quick References¶

Pandas Essentials:

df.head()                    # First few rows
df.info()                    # Data types and memory
df.describe()                # Summary statistics
df.isnull().sum()           # Missing values count
df.value_counts()           # Frequency counts

Visualization Starter:

import matplotlib.pyplot as plt
import seaborn as sns

# Quick distribution plot
sns.histplot(data=df, x='column_name')
plt.show()

# Correlation heatmap
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.show()

Start here: If you're new to data analysis, begin with Methodology to understand the systematic approach, then move to Pandas Quick Cheats for practical tools.