What is Data Cleaning?

Data cleaning, also referred to as data scrubbing or data cleansing, is the process of preparing data for analysis by identifying and correcting errors, inconsistencies, and inaccuracies. It’s essentially like cleaning up a messy room before you can use it effectively.

Raw data, which is data in its unprocessed form, is often riddled with issues that can negatively impact the results of analysis. These issues can include:

• Missing values: When data points are absent from a dataset.
• Inconsistent formatting: Inconsistency in how data is presented, like dates written in different formats (e.g., MM/DD/YYYY, YYYY-MM-DD).
• Duplicates: When the same data point appears multiple times in a dataset.
• Errors: This can include typos, spelling mistakes, or even data entry errors.

Data cleaning helps ensure that the data you’re analyzing is accurate and reliable, which is crucial for getting meaningful insights from your data.

Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets to improve their quality, accuracy, and reliability for analysis or other applications. It involves several steps aimed at detecting and rectifying various types of issues present in the data.