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
The Data Cleaning Workflow Overview
Was this section helpful?
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, 2022 (Morgan Kaufmann) - A definitive textbook on data mining, offering comprehensive coverage of data preprocessing techniques including data cleaning, integration, reduction, and transformation, which are fundamental to the workflow described.
- Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - A practical guide to data manipulation and cleaning using Python's pandas library, offering hands-on methods for implementing each step of the data cleaning workflow discussed in the section.
- Fundamentals of Data Engineering, Joe Reis, Matt Housley, 2022 (O'Reilly Media) - This book provides a modern perspective on building robust data systems, emphasizing data quality and the systematic processes required for data cleaning and validation within a broader data engineering context.