Clean Your Data
In this course, you’ll explore three exploratory data analysis (EDA) practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data.
By the end of this course, you will be able to:
• Apply input validation skills to a dataset with Python
• Explain the importance of input validation
• Demonstrate how to transform categorical data into numerical data with Python
• Explain the importance of categorical versus numerical data in a dataset
• Explain the importance of recognizing outliers in a dataset
• Demonstrate how to identify outliers in a dataset with Python
• Understand when to contact stakeholders or engineers regarding missing values
• Explain the importance of ethically considering missing values
• Demonstrate how to identify missing data with Python
Watch on Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Related AI Lessons
⚡
⚡
⚡
⚡
How to Write Better AI Image Prompts for Midjourney (With Examples That Actually Work)
Medium · ChatGPT
Image to Video AI: The Complete Workflow Playbook That Actually Produces Results
Medium · AI
Image Harvest v1.0.2: Internationalization, Free Pro Trial & Quality-of-Life Improvements
Dev.to · kyriewen
Pix2Pix: Image-to-Image Translation using Conditional GANs
Medium · Deep Learning
🎓
Tutor Explanation
DeepCamp AI