Which step in the Data Science process involves assessing the quality of collected data?

The correct answer is C. Data Validation.

Data validation is the process of ensuring that data is accurate, complete, and consistent. It is a critical step in the data science process, as it ensures that the data that is used for analysis is of high quality.

Data validation can be done manually or automatically. Manual data validation involves checking the data for errors by hand. This can be a time-consuming process, but it is often necessary for ensuring the accuracy of data. Automatic data validation involves using software to check the data for errors. This can be a more efficient process, but it is important to ensure that the software is accurate and up-to-date.

Data validation is an important step in the data science process. It ensures that the data that is used for analysis is of high quality. This can lead to more accurate and reliable results.

Here is a brief explanation of each option:

A. Data Collection: Data collection is the process of gathering data. This can be done through a variety of methods, such as surveys, interviews, and observation.
B. Data Cleaning: Data cleaning is the process of removing errors and inconsistencies from data. This can be a time-consuming process, but it is important to ensure the accuracy of data.
C. Data Validation: Data validation is the process of ensuring that data is accurate, complete, and consistent. It is a critical step in the data science process, as it ensures that the data that is used for analysis is of high quality.
D. Data Visualization: Data visualization is the process of representing data in a visual way. This can be done through a variety of methods, such as charts, graphs, and maps.