Difference between revisions of "Data Science versus Statistics"
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== Data Science versus Statistics == | == Data Science versus Statistics == | ||
− | '''Statistics''' is an academic field (branch of Mathematics) and a professional (practiced in various domains) | + | '''Statistics''' is an academic field (branch of Mathematics) and a professional discipline (practiced formally in various domains) that concerns the systematic collection, organization, analysis, interpretation, and presentation of data. There is an implied emphasis in rigor and the use of mathematically well established methodologies. |
− | '''Data Science''' is a broader, interdisciplinary activity, that may | + | '''Data Science''' is a broader, interdisciplinary and more applied activity, that may use Statistics but also other computational tools, processes, algorithms and systems to extract or extrapolate knowledge and insights from diverse data. There is an implied emphasis on general applicability and relevance in an evolving data landscape and the generation of actionable insights rather than academic rigor. |
+ | |||
+ | == Issues and Challenges == | ||
+ | * Despite the use of the word "Science", Data Science is not more ''scientific'' than Statistics, actually less so. Statistics is a term that has been used in academy context for a much longer period and it is more sharply and rigorously defined as sub-field of mathematics. | ||
== See Also == | == See Also == | ||
− | |||
* [[Data Science]] | * [[Data Science]] | ||
+ | * [[Data Analysis]] | ||
* [[:Category:Statistics | Statistics]] | * [[:Category:Statistics | Statistics]] | ||
Latest revision as of 17:27, 5 December 2023
Data Science versus Statistics
Statistics is an academic field (branch of Mathematics) and a professional discipline (practiced formally in various domains) that concerns the systematic collection, organization, analysis, interpretation, and presentation of data. There is an implied emphasis in rigor and the use of mathematically well established methodologies.
Data Science is a broader, interdisciplinary and more applied activity, that may use Statistics but also other computational tools, processes, algorithms and systems to extract or extrapolate knowledge and insights from diverse data. There is an implied emphasis on general applicability and relevance in an evolving data landscape and the generation of actionable insights rather than academic rigor.
Issues and Challenges
- Despite the use of the word "Science", Data Science is not more scientific than Statistics, actually less so. Statistics is a term that has been used in academy context for a much longer period and it is more sharply and rigorously defined as sub-field of mathematics.