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 Constitutes Duplicate Data?
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- Data Cleaning: Problems and Current Approaches, Erhard Rahm, Hong Hai Do, 2000 IEEE Bulletin of the Technical Committee on Data Engineering, Vol. 23 (IEEE Computer Society) - Defines data cleaning problems and their handling, including how to identify and resolve duplicate records, setting a conceptual framework.
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Foster Provost, Tom Fawcett, 2013 (O'Reilly Media) - Covers data preparation and understanding data quality in data analytics, essential for correctly defining and managing data duplication.
- Data Quality and Entity Resolution, Larisa Gheorghe and Erhard Rahm, 2019 Encyclopedia of Big Data Technologies (Springer, Cham) DOI: 10.1007/978-3-319-76840-5_37-1 - Provides an updated academic perspective on data quality and entity resolution, focusing on methods for detecting duplicates in large datasets.
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