Introduction to Exploratory Data Analysis
Chapter 1: Foundations of Exploratory Data Analysis
What is Exploratory Data Analysis?
Goals of EDA
The EDA Workflow
Tools for EDA: Python Libraries Overview
Setting Up Your Environment
Quiz for Chapter 1
Chapter 2: Data Loading, Inspection, and Initial Cleaning
Loading Data from Various Sources (CSV, Excel, JSON)
First Look at the Data: Shape, Head, Tail
Understanding Data Types (dtypes)
Handling Missing Data: Identification
Strategies for Missing Data: Imputation vs Deletion
Detecting and Handling Duplicate Records
Hands-on Practical: Loading and Initial Cleanup
Quiz for Chapter 2
Chapter 3: Univariate Analysis: Understanding Single Variables
Analyzing Numerical Variables: Central Tendency
Analyzing Numerical Variables: Dispersion
Visualizing Numerical Variables: Histograms
Visualizing Numerical Variables: Box Plots
Analyzing Categorical Variables: Frequency Counts
Visualizing Categorical Variables: Bar Charts
Identifying Outliers using Statistical Methods
Practice: Univariate Exploration
Quiz for Chapter 3
Chapter 4: Bivariate Analysis: Exploring Relationships Between Variables
Numerical vs Numerical: Scatter Plots
Numerical vs Numerical: Correlation Analysis
Visualizing Correlation: Heatmaps
Numerical vs Categorical: Comparative Plots
Categorical vs Categorical: Cross-Tabulation
Visualizing Categorical vs Categorical: Stacked/Grouped Bar Charts
Hands-on Practical: Bivariate Exploration
Quiz for Chapter 4
Chapter 5: Advanced Visualization and Introduction to Feature Engineering
Multivariate Visualization: Pair Plots
Customizing Plots for Clarity (Titles, Labels, Legends)
Introduction to Feature Engineering Concepts
Creating New Features from Existing Ones
Basic Data Transformation: Scaling and Normalization
Handling Categorical Features: Encoding Strategies
Introduction to Dimensionality Reduction Ideas
Summarizing and Reporting EDA Findings
Hands-on Practical: Feature Creation and Summary
Quiz for Chapter 5
Handling Categorical Features: Encoding Strategies
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- Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, Alice Zheng, Amanda Casari, 2018 (O'Reilly Media) - A comprehensive guide to various feature engineering techniques, including detailed explanations of categorical encoding strategies and their practical applications.
- Preprocessing data (sklearn.preprocessing), Scikit-learn developers, 2024 - Official documentation for Scikit-learn's data preprocessing module, providing technical details and usage examples for encoders like OneHotEncoder and LabelEncoder.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media, Inc.) - A practical guide that includes essential data preprocessing steps, such as categorical feature encoding, within the context of building machine learning models.
- CatBoost: unbiased boosting with categorical features, Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin, 2018 Advances in Neural Information Processing Systems, Vol. 31 DOI: 10.5555/3295222.3295325 - An academic paper introducing CatBoost, a gradient boosting algorithm that natively handles categorical features and employs specialized encoding techniques to avoid data leakage and bias, particularly relevant for understanding target encoding limitations.