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
Summarizing and Reporting EDA Findings
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- Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - A comprehensive guide to data manipulation and analysis with Python and Pandas, including extensive coverage of exploratory data analysis techniques and data preparation, which are crucial for structuring EDA findings.
- Storytelling with Data: A Data Visualization Guide for Business Professionals, Cole Nussbaumer Knaflic, 2015 (John Wiley & Sons) - Provides practical guidance on how to effectively communicate insights from data through clear visualizations and narrative, essential for reporting EDA findings.
- Computational notebooks as a means for reproducible data science, Christian A. C. Böttcher and R. B. A. M. H. Farenhorst, 2019 Big Data and Cognitive Computing, Vol. 3 (MDPI) DOI: 10.3390/bdcc3030038 - Discusses how computational notebooks like Jupyter facilitate reproducible data analysis by integrating code, results, and narrative, aligning with the section's emphasis on documentation and knowledge sharing.
- Feature Engineering for Machine Learning: Principles and Techniques for Building Predictive Models, Alice Zheng, Amanda Casari, 2018 (O'Reilly Media) - This book provides practical guidance on creating and selecting features, with a strong emphasis on how insights from exploratory data analysis inform these steps for machine learning.