MITCenter

In the realm of data science and analytics, the processes of data wrangling and data cleaning are crucial steps in preparing raw data for analysis. While they are often used interchangeably, they have distinct roles and purposes. Understanding the nuances between data wrangling and data cleaning which is also searched as Data Wrangling vs Data Cleaning is vital for anyone working with data, whether in a professional or academic setting. What is Data Cleaning? Data cleaning, also known as data cleansing, is the process of identifying and correcting errors or inconsistencies in data to improve its quality. This step is crucial as dirty data can lead to incorrect analyses and insights. Data cleansing includes a variety of tasks, such as: Removing duplicates: Eliminating duplicate items that might skew the analysis. Handling missing values: Filling in or removing missing data points. Correcting errors: Fixing typographical errors or inconsistencies in data formats. Standardizing data: ...