Difference between revisions of "Data Science"

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=An outline of Data Science=
=An outline of Data Science=
There are a few critical areas<ref>{{:Video/What are the 11 key areas of Data Management and specific data roles?}}</ref> of Data Science that must be explicitly mentioned here:
{{:Explaining the 11 key areas of Data Management}}
# [[Data Governance]]
# [[Data Architecture]]
# [[Data Modeling and Design]]
# [[Data Storage and Operations]]
# [[Data Security]]
# [[Data Integration]]
# [[Documentation and Content]]
# [[Master Data Management]]
# [[Data Warehouse and Business Intelligence]]
# [[Meta Data Management]]
# [[Data Quality]]


=Data Science and Quantum Computing=
=Data Science and Quantum Computing=

Revision as of 19:01, 20 February 2022

Data science(Q2374463) is a term popularized with the development of artificial intelligence as a branch of computing science or computer science. The idea of dataism[1], also propelled the notion of claiming the direct study of data as a branch of science by itself. The emergence of Data Science has to do with Moore's Law, since it is one of the first well-known document that relates data with physical sizes and social and economical implications.

An outline of Data Science

Anshul Tiwari, a data engineer stated that there are at least 11 areas of data management activities[2]:

  1. Data Governance
  2. Data Architecture
  3. Data Modeling and Design
  4. Data Storage and Operations
  5. Data Security
  6. Data Integration
  7. Documentation and Content
  8. Master Data Management
  9. Data Warehouse and Business Intelligence
  10. Meta Data Management
  11. Data Quality

References

  1. ↑ Harari, Yuval (Apr 19, 2013). Dr. Yuval Noah Harari - Data Processing - Part 1. local page: Yuval Noah Harari. 
  2. ↑ Tiwari, Anshul (Feb 16, 2022). What are the 11 key areas of Data Management and specific data roles?. local page: IT k Funde. 

Data Science and Quantum Computing

Data science as a scientific field can be related to the proposition that quantum computing can be treated as a scientific field. Quantum, being a unit of information, can be used to encode or represent data using varying physical phenomenon. The ability to utilize either the actual data content, or the algorithmic/physical implementations of algorithmic processing of data, must be bounded by the scientists' ability to merge or push the boundaries of either data science or quantum computing. One can think of data science as a branch of science that treats data as a noun-oriented abstraction, and quantum computing thinks of realizing computing activities using quantum physical processes being a verb-oriented abstraction.

For a more technical treatment of this subject, please see the following video[1]:

Introduction to Data Science: An overview by Steve Brunton

{{#ev:youtube |pzo13OPXZS4 }}


References

  1. ↑ Brunton, Steve (Jun 6, 2019). Intro to Data Science: Overview. local page. 

Related Pages

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B cont.
B cont.

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