Terms and definitions from all courses

D

Data anonymization: The process of protecting people's private or sensitive data by eliminating PII


Data cleaning: The process of formatting data and removing unwanted material
Data engineer: A data professional who makes data accessible, ensures data ecosystems offer reliable results, and manages infrastructure for data across enterprises


Data ethics: Well-founded standards of right and wrong that dictate how data is collected, shared, and used


Data governance: A process for ensuring the formal management of a company’s data assets


Data professional: Any individual who works with data and/or has data skills


Data science: The discipline of making data useful


Data scientist: A data professional who works closely with analytics to provide meaningful insights that help improve current business operations


Data source: The location where data originates


Data stewardship: The practices of an organization that ensures that data is accessible, usable, and safe
Data structure: A collection of data values or objects that contain different data types


Data type: An attribute that describes a piece of data based on its values, its programming language, or the operations it can perform
DataFrame: A two-dimensional, labeled data structure with rows and columns
Data visualization: A graph, chart, diagram, or dashboard that is created as a representation of information


Database (DB) file: A file type used to store data, often in tables, indexes, or fields Dataframe: A two-dimensional data-structure organized into rows and columns DBSCAN: A clustering methodology that searches data space for continuous regions of high density; stands for “density-based spatial clustering of applications with noise”