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
Customizing Plots for Clarity (Titles, Labels, Legends)
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- Matplotlib Legend Guide, Matplotlib Developers, 2024 (Matplotlib Developers) - A tutorial explaining how to create, customize, and place legends effectively in Matplotlib plots, which is crucial for distinguishing data series.
- Matplotlib Text in Matplotlib, Matplotlib Developers, 2024 - Covers adding and customizing various text elements, including plot titles, axis labels, and annotations, to enhance the readability of visualizations.
- Customizing plots (Seaborn documentation), Michael Waskom, 2024 (Michael Waskom) - Demonstrates how to adjust various visual properties of Seaborn plots, including how Matplotlib functions are used for setting titles, labels, and legends.
- Python Data Science Handbook: Essential Tools for Working with Data, Jake VanderPlas, 2016 (O'Reilly Media) - A comprehensive guide to data science in Python, with dedicated sections on Matplotlib and Seaborn for data visualization, covering customization options in detail.
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