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CodeSignal

Foundations of Feature Engineering

via CodeSignal

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Overview

Master the basics of feature engineering by learning to clean, handle missing data, scale, and normalize datasets. Prepare raw data for transformation and analysis, setting a solid foundation for advanced data engineering tasks.

Syllabus

  • Unit 1: Introduction to Feature Engineering
    • Loading The Titanic Dataset
    • Exploring Dataset Structure
    • Peek at Your Data Preview
    • Customize Data Preview Settings
    • Understanding Numbers Through Statistics
  • Unit 2: Identifying and Handling Missing Data
    • Detecting Missing Data Like a Pro
    • Missing Ages Need Fixing
    • Switching to Mean Imputation for Missing Ages
    • Mode Imputation for Missing Ports
    • Missing Data Handling for Passenger Decks
  • Unit 3: Detecting and Addressing Outliers
    • Calculating Quartiles for Outlier Detection
    • Adjusting Outlier Detection Sensitivity
    • Outliers in Need of Detection
    • Capping Outliers Effectively
  • Unit 4: Exploring Data Scaling Techniques
    • Scale Your First Dataset
    • Moving to Standard Scaling
    • Data Scaling Gone Wrong
    • Reverting Scaled Data in Practice

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