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CodeSignal

Introduction to Data Cleaning with Python

via CodeSignal

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Overview

This course introduces fundamental concepts of data cleaning using Python, covering essential libraries, handling missing values, detecting and removing duplicates, dealing with outliers, and normalizing data for analysis.

Syllabus

  • Unit 1: Data Handling and File Operations in Python
    • Modify Entries in a DataFrame
    • Managing Missing Data with Pandas
    • Debug the Agenda Logic
    • Mastering DataFrame Inspection
  • Unit 2: Handling Missing Data with Pandas
    • Adapting Missing Data Strategies
    • Fix Bugs in Handling Missing Data
    • Clean and Fill Your DataFrame
    • Handling Missing Values Like a Pro
  • Unit 3: Handling Duplicates in Data Using Pandas
    • Debug Duplicate Detection
    • Mastering DataFrame Duplicate Handling
    • Clearing Up Duplicate Data
  • Unit 4: Detecting Outliers in Data Using Python
    • Calculate Z-scores for Outlier Detection
    • Fix the Outlier Detection Code
    • Handle Outliers with Z-score
    • Identify and Remove Outliers Easily
  • Unit 5: Standardizing and Normalizing Data in Python
    • Changing the Scale of Data
    • Fix the Data Preprocessing Errors
    • Handling Missing Data Efficiently
    • Transform Your Data Efficiently

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