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
Visualizing Missing Data Patterns
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- Flexible Imputation of Missing Data, Second Edition, Stef van Buuren, 2018 (Chapman and Hall/CRC) DOI: 10.1201/9780429492202 - A standard text covering the theory and practical application of handling missing data, including the importance of pattern visualization.
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber and Jian Pei, 2011 (Morgan Kaufmann) - A widely recognized textbook with a section dedicated to data preprocessing techniques, including methods for addressing and visualizing missing values.
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