Difference between revisions of "Data Science"
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The field is rather loosely defined as it applies to several different domains with a varying degree on reliance on: | The field is rather loosely defined as it applies to several different domains with a varying degree on reliance on: | ||
| − | * [[wikipedia:Computer Science | Computer Science]] and [[Data Engineering]] | + | * [[wikipedia:Computer Science | Computer Science]] and [[Data Engineering]] as the fundamental enabler |
| − | * [[wikipedia:Mathematics | Mathematics]] and more specifically [[wikipedia:Statistics | Statistics]] | + | * [[wikipedia:Mathematics | Mathematics]] and more specifically [[wikipedia:Statistics | Statistics]] as the conceptual framework |
| − | * Information [[Visualization]] | + | * Information [[Visualization]] and [[wikipedia:Graphic Design | Graphic Design]] as additional user oriented outputs |
| − | + | * the analysis and integration with [[:Category:Business Process | Business Processes]] and objectives | |
| − | * [[:Category:Business Process | Business Processes]] | ||
== Context == | == Context == | ||
Revision as of 18:27, 5 December 2023
Contents
Definition
Data Science is an emerging (as of 2020) interdisciplinary field that aims to extract and utilize knowledge from diverse data sets, which are typically large (see Big Data).
Components
The field is rather loosely defined as it applies to several different domains with a varying degree on reliance on:
- Computer Science and Data Engineering as the fundamental enabler
- Mathematics and more specifically Statistics as the conceptual framework
- Information Visualization and Graphic Design as additional user oriented outputs
- the analysis and integration with Business Processes and objectives
Context
In the context of Risk Management the concepts, objectives and practices of data science overlap strongly with those of Quantitative Risk Management.
