A data analyst uses data selection and data preprocessing techniques to decide which data is relevant to their analysis and which data types and variables are appropriate.
Data selection involves identifying and extracting relevant data from a larger dataset or multiple datasets. This may involve filtering out irrelevant data, combining data from different sources, or sampling data to reduce its size while maintaining its representativeness.
Data preprocessing involves transforming the selected data into a format that is suitable for analysis. This may involve cleaning the data to remove errors or inconsistencies, transforming the data to normalize its distribution, or reducing the dimensionality of the data to focus on the most important variables.
In addition to data selection and preprocessing, data analysts may also use statistical and machine learning techniques to analyze the data and extract insights. These techniques may involve modeling the relationships between variables, identifying patterns or trends in the data, or making predictions or recommendations based on the data. However, selecting and preprocessing the data is a crucial first step in the data analysis process, as it helps ensure that the analysis is based on relevant and appropriate data.