Data Transformation Process

Working with data transformations requires several different steps:

1. You can start the process by collecting data from diverse sources such as APIs, flat files, or databases, depending on the needs of the project.
2. Once collected, profiling of the data needs to happen to evaluate the structure, completeness, consistency, and accuracy of the dataset. This is important because the type of actions that you can take next on this data, will depend on its profile.
3. Then comes the data cleaning phase, where missing values are addressed, duplicate records are removed, and formats are standardized to ensure uniformity across variables.
4. Finally, wrangling techniques are used to reshape, merge, or transform the cleaned data into formats that align with the requirements of downstream models, dashboards, or machine learning pipelines.