What are the best practices in data reduction?

What are the best practices in data reduction?

Data reduction involves techniques to minimize the amount of data while preserving its integrity. Key practices include data compression, dimensionality reduction, and data sampling.

Can you explain dimensionality reduction?

Dimensionality reduction is the process of reducing the number of features in a dataset while retaining essential information. Techniques like PCA (Principal Component Analysis) are commonly used.

What is data compression and how does it work?

Data compression reduces the size of data by encoding it more efficiently. Lossless compression retains all original data, while lossy compression sacrifices some data for higher reduction rates.

Are there any tools you recommend for data reduction?

Yes, popular tools include Python libraries like Pandas for data manipulation, Scikit-learn for machine learning, and specialized software like Apache Spark for large datasets.