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
Why Remove Duplicates?
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- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, 2011 (Elsevier) - This book is a widely recognized resource for data mining, with a dedicated chapter on data preprocessing that covers data cleaning, including strategies and the rationale for addressing inconsistencies and duplicates to ensure data quality for analysis.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - This practical guide to machine learning includes extensive coverage of data preparation techniques, emphasizing the importance of clean data, including duplicate removal, for building robust and accurate machine learning models.
- Data Quality: The Ten Dimensions That Every Data Professional Needs to Know, James D. Price, 2017 (Technics Publications) - This book provides a practical framework for understanding and managing data quality, detailing how issues like duplicate records undermine data integrity and affect business operations and analytical outcomes across multiple dimensions.