Python for data Science 4 handling missing values and converting data types

Python for Data Science 4: Handling Missing Values & Converting Data Types

Welcome to the fourth episode in our Python for Data Science series! In this tutorial, we dive deep into two crucial aspects of data preprocessing:

🔹 Handling Missing Values:
Learn how to identify, analyze, and effectively deal with missing data using tools like pandas. We cover techniques such as:

Detecting null values

Dropping missing data

Filling missing data with statistical methods (mean, median, mode)

Custom imputation strategies

🔹 Converting Data Types:
Data types matter! Discover how to:

Convert between object, numeric, datetime, and category types

Avoid common pitfalls during type conversion

🎯 Whether you're cleaning a messy dataset or preparing data for analysis and modeling, this lesson gives you the skills to make your data reliable and ready for insights.

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