What does the term 'data wrangling' typically refer to in data science?

The term 'data wrangling' refers specifically to the process of cleaning and transforming raw data into a usable format for analysis. This involves preparing the data by correcting inaccuracies, converting data types, handling missing values, and structuring the data in a way that facilitates easy access and processing. In data science, this is a crucial step because high-quality, clean data is essential for obtaining reliable insights and making informed decisions.

To successfully conduct analyses, data scientists often work with diverse datasets that may come from various sources and formats. Data wrangling provides the necessary tools to standardize these datasets, allowing for seamless integration and further analysis. As a result, the focus of data wrangling on preparing data for analytical tasks underscores its importance in the overall data science workflow.