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Terms and definitions from all courses
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səhifə | 7/28 | tarix | 30.12.2023 | ölçüsü | 148,01 Kb. | | #167905 |
| PFymNGYQQ5Cf1XbjyxwNOg fe8a91120d2244988c658b5a363087f1 Advanced-Data-Analytics-Certificate-glossary
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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”
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