Explanation: In the field of data science, the process of handling data is systematic and involves several steps. The first step after acquiring the data is data cleansing, which is crucial for ensuring the quality and reliability of the data. Data cleansing involves identifying and correcting or removing errors, inconsistencies, and irrelevant information from the dataset. This step is essential because the quality of the data directly impacts the accuracy and reliability of the analysis and insights derived from it.
Data cleansing can include various activities such as removing duplicate records, correcting data entry errors, handling missing values, and standardizing data formats. By performing data cleansing first, data scientists ensure that the data is clean and consistent, which is a prerequisite for effective data integration and analysis.
Data integration, on the other hand, is the process of combining data from different sources into a single, unified view. This step is typically performed after data cleansing to ensure that the integrated data is clean and consistent. Integrating data before cleansing it can lead to the propagation of errors and inconsistencies, which can negatively impact the analysis.
Data replication involves creating copies of data to ensure availability and redundancy. This step is not typically performed immediately after acquiring the data. Instead, it is often a part of data management and backup strategies. Replicating data before cleansing it can result in the duplication of errors and inconsistencies, which can complicate the data management process.
In summary, the correct order of steps in the data science workflow is to first cleanse the data to ensure its quality and reliability, followed by data integration and data replication at appropriate stages in the process. This systematic approach ensures that the data is clean, consistent, and ready for analysis, leading to more accurate and reliable insights.