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Python For Kids · Lesson

Data Cleaning and Preprocessing

Understand how to handle missing data, remove duplicates, and preprocess raw data for analysis.

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Data Cleaning and Preprocessing

Raw data is often messy and requires cleaning and preprocessing before it can be analyzed. These steps are crucial for ensuring the quality and accuracy of your analysis.

In this lesson, you’ll learn techniques to handle missing data, remove duplicates, and preprocess data for analysis.

Data Cleaning and Preprocessing — illustration 1

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What Is Data Cleaning?

Data cleaning involves identifying and fixing errors in the dataset. Common tasks include:

  • Handling missing values.
  • Removing duplicates.
  • Standardizing formats.

All lessons in this course

  1. What is Data Science?
  2. The Role of Python in Data Science
  3. Data Structures for Data Science
  4. Data Cleaning and Preprocessing
  5. Exploratory Data Analysis (EDA)
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