The correct answer is: A. Data integration.
Data integration is the process of combining data from multiple sources into a single, unified dataset. This can be done for a variety of reasons, such as to improve the accuracy of data analysis, to make data more accessible to users, or to create a single source of truth for data.
Data integration can be a complex process, as it requires the identification of the data sources, the transformation of the data into a common format, and the merging of the data into a single dataset. However, it is a critical step in many data science projects, as it allows for the use of all available data to improve the accuracy and insights of data analysis.
The other options are incorrect because they do not accurately describe the process of aggregating data from multiple sources into a single dataset.
- Data augmentation is the process of artificially increasing the size of a dataset by creating new data points. This can be done by using techniques such as data generation, data synthesis, and data augmentation.
- Data normalization is the process of converting data into a standard format. This can be done by converting data into a common scale, such as a percentage or a ratio.
- Data imputation is the process of filling in missing data points in a dataset. This can be done by using techniques such as mean imputation, median imputation, and multiple imputation.