How I Tackle Data Cleaning as a Data Practitioner

1. 1Explore raw data to identify inconsistencies and missing values
2. 2Apply data normalization techniques to standardize data formats
3. 3Handle missing data using imputation or interpolation methods
4. 4Remove duplicates and outliers to improve data quality
5. 5Validate data against business rules and domain expertise

Data scientists and analysts can benefit from this article to improve their data cleaning skills and collaborate with data engineers to ensure high-quality data

💡 Data cleaning is a crucial step in the data science workflow that requires careful attention to detail and a systematic approach

Clean data is key to insights! Learn how to tackle data cleaning like a pro