Overview
Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
It's estimated that data scientists spend up to 80% of their time cleaning and preparing data, making it a crucial skill.
Key Concepts
- Handling missing values
- Dealing with outliers
- Fixing data types
- Removing duplicates
- Feature scaling and normalization
Practice Exercise
Exercise: Clean a Messy Dataset
Using the 'Dirty Data' dataset provided:
- Identify and handle missing values appropriately
- Convert data types to their proper format
- Detect and handle outliers
- Remove duplicate entries
- Create a clean version of the dataset ready for analysis
Resources
Kaggle Data Cleaning
Main resource for today
Data Cleaning with Python
Real Python tutorial
Handling Missing Data
Towards Data Science article
Outlier Detection
Methods for detecting and handling outliers
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