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
Strategy 2: Deleting Columns
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- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter, Wes McKinney, 2022 (O'Reilly Media) - A foundational resource for data manipulation in Python, including practical methods for handling missing data and restructuring datasets with pandas.
- Flexible Imputation of Missing Data, Second Edition, Stef van Buuren, 2018 (CRC Press) DOI: 10.1201/9780429492259 - Provides a detailed statistical and methodological perspective on missing data, discussing the assumptions and implications of various handling strategies, including deletion.
- pandas.DataFrame.drop, pandas development team, 2024 - Official documentation for the pandas function used to remove specified rows or columns from a DataFrame.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - Discusses data preprocessing techniques, including the handling of missing values, as a fundamental step in preparing data for machine learning models.
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