Static Datasets

Static datasets are collections of data that do not change over time or during the execution of a program, typically stored in files or databases in a fixed format. They are used for analysis, testing, training machine learning models, or as reference data in applications. Examples include CSV files, JSON datasets, SQL dumps, or pre-compiled data packages.

Also known as: Fixed datasets, Immutable data, Pre-loaded data, Static data, SDS

Why learn Static Datasets?

Developers should use static datasets when they need consistent, reproducible data for tasks like unit testing, data analysis, or model training, as they ensure results are not affected by real-time changes. They are essential in scenarios requiring data versioning, offline processing, or when working with historical data that must remain unchanged for accuracy.

Learning Resources

• docs: Data Version Control (DVC) Documentation
• tutorial: Kaggle Datasets Tutorial
• course: Coursera: Data Management for Data Science
• video: YouTube: Working with Static Data in Python
• book: Book: 'Data Science from Scratch' by Joel Grus