Data Cleaning
Data cleaning is the process of detecting and correcting (or removing) corrupt, inaccurate, incomplete, or irrelevant records from a dataset. It involves tasks like handling missing values, removing duplicates, correcting inconsistencies, and standardizing formats to ensure data quality and reliability for analysis or machine learning. This process is a critical step in data preprocessing and is essential for producing accurate insights and models.
Developers should learn data cleaning because it is foundational for any data-driven project, including data analysis, machine learning, and business intelligence, where poor data quality can lead to misleading results. It is used in scenarios like preparing datasets for training machine learning models, ensuring data integrity in databases, and cleaning user-generated data from web applications or surveys. Mastering data cleaning improves the robustness and accuracy of downstream applications.