How to Handle Missing Data in Data Science | Python Pandas & Scikit-Learn Tutorial (2025 )

Published: 01 March 2025
on channel: Dr. Uohna June Thiessen

Missing data is one of the most common — and most damaging — problems in data science. In this tutorial, Dr. Uohna Thiessen shows you exactly how to detect, understand, and handle missing values using Python's Pandas and Scikit-learn libraries.

Whether you're building a machine learning model or cleaning a dataset for analysis, this video gives you the practical tools to handle missing data like a professional.

🕐 TIMESTAMPS
00:00 — Why Missing Data Matters
00:45 — Types of Missing Data: MCAR, MAR, MNAR
01:45 — Detecting Missing Values with Pandas
02:30 — Deletion Methods: When to Drop Rows/Columns
03:15 — Imputation: Mean, Median, Mode
04:15 — Advanced Imputation with Scikit-learn
05:30 — Best Practices and Common Mistakes
06:15 — Summary

📌 WHAT YOU'LL LEARN
The three types of missing data and why the distinction matters
How to detect and visualize missing values in Pandas
When to delete vs. impute missing data
How to use SimpleImputer in Scikit-learn
Best practices for missing data in machine learning pipelines

🔗 RESOURCES & NEXT STEPS
📬 Weekly Python and data science tutorials: [NEWSLETTER_LINK]
▶️ Watch next: Python Data Types for Beginners
▶️ Watch: AI Bias in Machine Learning — What You Need to Know

👩‍🏫 ABOUT DR. UOHNA THIESSEN
Data scientist and educator. Practical Python and ML tutorials for working professionals and job seekers.

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