Datajoukko

A datajoukko, in data science and statistics, is a collection of data records that is intended for analysis, modeling, or reporting. It is typically organized so that each row represents an observation and each column represents a feature or attribute. Datajoukko size can range from a few dozen observations to billions of rows, and sources may include experiments, surveys, logs, or external databases.

A datajoukko usually has a schema that defines the columns, their data types, and any constraints. Common formats and storage options include CSV files, JSON, Parquet, SQL database tables, or data stored in data warehouses or data lakes. Metadata accompanies a datajoukko to describe provenance, collection methods, quality, and lineage.

Datajoukko can be categorized by structure and labeling. Structured data is organized into rows and columns, while semi-structured or unstructured data may require additional processing. Datasets can be labeled (for supervised learning) or unlabeled, and may be time series or cross-sectional. Missing values, outliers, and inconsistencies are common data quality issues that require preprocessing.

Typical workflows for datajoukko include cleaning, deduplication, normalization or standardization, encoding of categorical variables, and feature engineering. Data is often split into training, validation, and test sets to support model development and evaluation. Reproducibility is enhanced through documentation of sources, preprocessing steps, and versioning.

Applications of datajoukko span statistical analysis, hypothesis testing, machine learning, data mining, and performance benchmarking. Ethical and legal considerations, such as privacy, consent, and licensing, are important in managing and sharing datajoukko.