Introduction to Data Cleaning and Preprocessing
Chapter 1: The Importance of Clean Data
What is Data Cleaning?
What is Data Preprocessing?
Common Sources of Dirty Data
Impact of Poor Data Quality
The Data Cleaning Workflow Overview
Quiz for Chapter 1
Chapter 2: Identifying and Handling Missing Data
What Are Missing Values?
Methods for Detecting Missing Data
Visualizing Missing Data Patterns
Strategy 1: Deleting Rows (Listwise Deletion)
Strategy 2: Deleting Columns
Strategy 3: Basic Imputation (Mean/Median/Mode)
Considerations for Choosing a Strategy
Handling Missing Data: Hands-on Practical
Quiz for Chapter 2
Chapter 3: Dealing with Duplicate Data
What Constitutes Duplicate Data?
Why Remove Duplicates?
Identifying Complete Duplicate Rows
Identifying Duplicates Based on Specific Columns
Removing Duplicate Rows
Handling Duplicates: Practice
Quiz for Chapter 3
Chapter 4: Correcting Data Types
Common Data Types in Datasets
Why Correct Data Types Matter
Identifying Incorrect Data Types
Converting to Numeric Types (Integer, Float)
Handling Errors During Numeric Conversion
Converting to Datetime Types
Converting to Categorical or String Types
Data Type Correction: Hands-on Practical
Quiz for Chapter 4
Chapter 5: Basic Data Formatting and Standardization
Importance of Consistent Formatting
Standardizing Text Case (Upper/Lower)
Removing Leading/Trailing Whitespace
Simple String Replacements
Basic Unit Conversion Example
Formatting Practice
Quiz for Chapter 5
What is Data Cleaning?
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- Data Cleaning: A Practical Guide, Ihab F. Schopf, Christian Müller, and Hermann J. P. W. Schöberl, 2021 (Springer) DOI: 10.1007/978-3-030-80249-1 - This book provides a current and comprehensive overview of data cleaning techniques, addressing common data quality issues and practical strategies for resolution.
- Data Quality: The Accuracy Dimension, Jack E. Olson, 2003 (Morgan Kaufmann) - A foundational book on data quality, offering detailed insights into defining, measuring, and improving data accuracy, which is a core objective of data cleaning.
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, 2011 (Morgan Kaufmann) - A widely-used textbook with a dedicated chapter on data preprocessing, thoroughly covering data cleaning techniques, common issues, and methods for handling various data problems.