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
Strategies for Missing Data: Imputation vs Deletion
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- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, and Jian Pei, 2011 (Elsevier) - Comprehensive textbook detailing data preprocessing methods, including strategies for handling missing data, their benefits, and drawbacks.
- Missing Data Analysis: A Practical Guide to Multiple Imputation, John W. Graham, 2012 (Springer) DOI: 10.1007/978-1-4614-4018-5 - Discusses missing data analysis, presenting the theoretical background of missing data mechanisms and the constraints of simple deletion and imputation methods.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - Addresses data preprocessing steps, including managing missing data, offering strategies and considerations within a machine learning project.
- Missing Data, Paul D. Allison, 2001 Quantitative Applications in the Social Sciences, Vol. 136 (SAGE Publications, Inc.) DOI: 10.4135/9781412984183 - A classic guide providing an understanding of missing data issues and basic handling approaches.