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
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
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
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
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
The Importance of Clean Data
Data is the starting point for any analysis or machine learning project, but raw data often comes with problems. Errors, missing values, and inconsistencies can significantly skew results and undermine the reliability of any insights derived.
This chapter introduces the concepts of data cleaning and preprocessing. You will learn:
- What data cleaning and preprocessing entail.
- Common ways data becomes inaccurate or "dirty".
- The consequences of using low-quality data for analysis or modeling.
- A high-level overview of the typical steps involved in the cleaning process.
Understanding these fundamentals is the first step towards preparing reliable data for your projects.
Sections
1.1 What is Data Cleaning?
1.2 What is Data Preprocessing?
1.3 Common Sources of Dirty Data
1.4 Impact of Poor Data Quality
1.5 The Data Cleaning Workflow Overview
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