Final answer:
The two main ways to ensure data cleanliness are Imputation, which involves replacing missing or invalid data, and Validation, which checks for data accuracy and consistency. Other methods contribute to preprocessing but are not the primary ones for data cleanliness.
Step-by-step explanation:
The two main ways to ensure that data is clean are Imputation and Validation. Imputation is a process where missing or invalid data is replaced or estimated based on various methods. For example, if a dataset of temperatures has a few missing values, imputation may fill in these gaps with the mean or median temperature from the rest of the dataset. Machine learning algorithms and statistical methodologies often guide these estimations.
Validation, on the other hand, involves checking the data for accuracy and consistency. This might include verifying that entries fall within a certain range, ensuring that they are the correct datatype, or checking that the data adheres to a defined schema or pattern. Regular expressions, constraints, and other rule-based checks are common validation techniques.
Other methods like Sorting and Filtering, Encoding and Normalization, and Cleaning and Scaling do play a role in data preprocessing, but they are not the primary methods for ensuring data cleanliness.