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
Numerical vs Categorical: Comparative Plots
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- Fundamentals of Data Visualization, Claus O. Wilke, 2019 (O'Reilly Media) - A comprehensive guide to data visualization, covering principles, common plot types, and effective communication through graphics, directly relevant to comparative plots.
- seaborn.boxplot, Michael Waskom and the Seaborn development team, 2024 - Official documentation for Seaborn's box plot function, including parameters, examples, and detailed explanations for visualizing distributions.
- seaborn.violinplot, Michael Waskom and the Seaborn development team, 2024 - Official documentation for Seaborn's violin plot function, offering insights into its usage for displaying distribution shapes and summary statistics.
- Box Plots in Python, Plotly Technologies Inc., 2024 - Official documentation demonstrating how to create box plots using Plotly in Python, with examples for comparing numerical data across categories.
- Violin Plots: A Box Plot-Density Trace Synergism, Jerry L. Hintze and Ray D. Nelson, 1998 The American Statistician, Vol. 52 (American Statistical Association) DOI: 10.2307/2685478 - The original academic paper introducing violin plots, highlighting their ability to combine the summary of box plots with the visual detail of kernel density estimates.