What is meant by Data preparation?
The term "Data Preparation" refers to the process of cleaning, reshaping, and adjusting raw data to make it suitable for analysis or processing purposes. This typically involves three main steps:
- Filtering: Removing irrelevant or erroneous data from the dataset.
- Selecting: Choosing specific data elements that are relevant for analysis or model training.
- Transforming: Converting data into a format that is suitable for further analysis or processes.
Typical software functions in the area of "Data Preparation (Filtering, Selecting, Transforming)":
- Data Cleaning: Removing duplicates, errors, and inconsistencies from the data.
- Data Filtering: Applying conditions or rules to select relevant data and eliminate unnecessary data.
- Data Selection: Identifying and extracting specific records or features needed for analysis or modeling.
- Data Transformation: Converting data into different formats or structures, e.g., normalization, aggregation, or pivoting.
- Feature Engineering: Creating new features or attributes from existing data to improve model performance.
- Data Integration: Combining data from various sources or systems to create a comprehensive dataset.
- Data Enrichment: Adding additional information or context to existing data to enhance data quality and usefulness.