Browse all content tagged with Data Cleaning
Data cleaning is the crucial process of detecting and fixing errors or inconsistencies in data to enhance its quality, ensuring accuracy, consistency, and reliability for analytics and decision-making. Explore key processes, challenges, tools, and the role of AI and automation in efficient data cleaning.
Exploratory Data Analysis (EDA) is a process that summarizes dataset characteristics using visual methods to uncover patterns, detect anomalies, and inform data cleaning, model selection, and analysis using tools like Python, R, and Tableau.
Fuzzy matching is a search technique used to find approximate matches to a query, allowing for variations, errors, or inconsistencies in data. Commonly applied in data cleaning, record linkage, and text retrieval, it uses algorithms like Levenshtein distance and Soundex to identify similar but not identical entries.
Garbage In, Garbage Out (GIGO) highlights how the quality of output from AI and other systems is directly dependent on input quality. Learn about its implications in AI, the importance of data quality, and strategies to mitigate GIGO for more accurate, fair, and reliable outcomes.
