What is the primary purpose of data cleaning in data science?

The primary purpose of data cleaning in data science is to improve data quality by fixing errors and removing duplicates. High-quality data is essential for accurate analysis, modeling, and ultimately making informed decisions. Data cleaning involves identifying inconsistencies, inaccuracies, or incomplete data entries and taking corrective actions, such as correcting misspellings, standardizing formats, or eliminating duplicate records. This process ensures that the datasets used for analysis are reliable and valid, which significantly contributes to the effectiveness of any analytical results derived from it.

While the other options touch on relevant aspects of data handling, they do not capture the core objective of data cleaning. Increasing the amount of data or enhancing processing speed may be beneficial in certain contexts, but they are not direct goals of the data cleaning process. Similarly, simplifying data collection is important in its own right but does not address the critical need for ensuring the quality and integrity of the data that has already been gathered.