Data Wrangling
Data wrangling, also known as data munging, is the process of cleaning, transforming, and structuring raw data into a usable format for analysis or modeling. It involves tasks like handling missing values, correcting inconsistencies, converting data types, and merging datasets. This foundational step is crucial in data science and analytics pipelines to ensure data quality and reliability.
Developers should learn data wrangling when working with real-world datasets, which are often messy and unstructured, such as in data science, machine learning, or business intelligence projects. It's essential for preparing data for analysis, visualization, or model training, improving accuracy and efficiency in downstream tasks. Use cases include cleaning customer data for insights, preprocessing sensor data for IoT applications, or aggregating logs for monitoring systems.