Data Science Steps to Follow - 01

1 Series Navigation

NoteData Science Steps Series

This is Part 1 of a 6-part series on data science fundamentals:

1. Part 1: EDA Fundamentals (You are here)
2. Part 2: Feature Preprocessing and Generation
3. Part 3: Handling Anonymized Data
4. Part 4: Text Feature Extraction
5. Part 5: Image Feature Extraction
6. Part 6: Advanced Techniques

2 Exploratory Data Analysis (EDA)

This guide is based on the excellent course How to Win a Data Science Competition: Learn from Top Kagglers by Yandex and MIPT. I’ve organized these notes to help fellow data scientists master the art of EDA.

2.1 What is EDA?

EDA is the critical first step in any data science project. It helps us:

• Understand the data deeply
• Build intuition about patterns and relationships
• Generate hypotheses for feature engineering
• Find insights that inform modeling decisions

2.2 The Power of Visualization

One of the most powerful EDA tools is visualization. Let’s look at a fascinating example from a Kaggle competition:

TipReal-World Example

In a promotion prediction competition, simple visualization revealed: - Two key features: promos sent and promos used - A direct relationship between their difference and the target - This insight led to 81% accuracy without complex modeling!

# Example visualization code
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

def plot_promo_relationship(df):
    plt.figure(figsize=(10, 6))
    plt.scatter('promos_sent', 'promos_used', data=df)
    plt.xlabel('Number of Promos Sent')
    plt.ylabel('Number of Promos Used')
    plt.title('Promo Usage Pattern')
    plt.show()

2.3 Building Intuition

2.3.1 1. Domain Knowledge Acquisition

Before diving into analysis, focus on:

• Understanding the business goal
• Researching similar problems
• Learning industry-specific metrics
• Reading relevant documentation

2.3.2 2. Data Validation

ImportantKey Validation Checks

• Value ranges (e.g., age between 0-120)
• Logical relationships (clicks ≤ impressions)
• Missing value patterns
• Outliers and anomalies

def validate_data(df):
    """Basic data validation checks"""
    issues = []
    
    # Age check
    if df['age'].max() > 120:
        issues.append("Found age > 120")
        
    # Click validation
    if any(df['clicks'] > df['impressions']):
        issues.append("Found clicks > impressions")
    
    return issues

2.3.3 3. Understanding Data Generation

Key considerations:

• Sampling methodology
• Train/test split rationale
• Time-based patterns
• Data collection process

NoteJeremy Howard’s Insight

“In data science, there are no outliers - only opportunities to understand your data better.”

2.4 Related Resources

• Understanding Matrix Multiplication - Essential math for data science
• Using Nougat for Research Papers - Tool for data science research

2.5 Next Steps

Continue to Part 2: Feature Preprocessing and Generation, where we’ll explore how to transform raw data into meaningful features.

2.6 References

1. Coursera: How to Win a Data Science Competition
2. Fast.ai: Practical Deep Learning
3. Python Data Science Handbook