A Manifesto for Applied Data Science - Reasoning from a Business Perspective

Bahne Christiansen; Uwe Neuhaus; Michael Schulz; Adrian Hargreaves; Antinisca Di Marco; Guido Proietti; Fabrizio Rossi; Giovanni Stilo; Mark H. Haney; Andrew Duncan; Daniele Tessera; Tilman Todt; Andreas Brandenberg; Patricia Feubli

A Manifesto for Applied Data Science - Reasoning from a Business Perspective

Bahne Christiansen, Uwe Neuhaus, Michael Schulz, Adrian Hargreaves, Antinisca Di Marco, Guido Proietti, Fabrizio Rossi, Giovanni Stilo, Mark H. Haney, Andrew Duncan, Daniele Tessera, Tilman Todt, Andreas Brandenberg, Patricia Feubli

UHI Inverness

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Due to the increasing growth of the discipline Data Science, a partition into sub-disciplines
seems appropriate. Therefore, we propose the division of Data Science in Pure Data Science,
in which methods and tools are developed, and Applied Data Science, in which these methods
and tools are adapted and applied to practical problems of a specific domain. This article
focuses on Applied Data Science and how it should be positioned in relation to its adjoining
disciplines. We also introduce the term Business Data Science as a specific form of Applied
Data Science in the business domain and describe its relationship to existing terms like
Business Analytics and Business Intelligence.

Original language	English
Title of host publication	CEUR Workshop Proceedings
Volume	3340
Publication status	Published - Sept 2022

Keywords

Data Science
Applied Data Science
Pure Data Science
Business Data Science

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https://ceur-ws.org/Vol-3340/Licence: CC BY

Cite this

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title = "A Manifesto for Applied Data Science - Reasoning from a Business Perspective",

abstract = "Due to the increasing growth of the discipline Data Science, a partition into sub-disciplines seems appropriate. Therefore, we propose the division of Data Science in Pure Data Science, in which methods and tools are developed, and Applied Data Science, in which these methods and tools are adapted and applied to practical problems of a specific domain. This article focuses on Applied Data Science and how it should be positioned in relation to its adjoining disciplines. We also introduce the term Business Data Science as a specific form of Applied Data Science in the business domain and describe its relationship to existing terms like Business Analytics and Business Intelligence.",

keywords = "Data Science, Applied Data Science, Pure Data Science, Business Data Science",

author = "Bahne Christiansen and Uwe Neuhaus and Michael Schulz and Adrian Hargreaves and {Di Marco}, Antinisca and Guido Proietti and Fabrizio Rossi and Giovanni Stilo and Haney, {Mark H.} and Andrew Duncan and Daniele Tessera and Tilman Todt and Andreas Brandenberg and Patricia Feubli",

year = "2022",

month = sep,

language = "English",

volume = "3340",

booktitle = "CEUR Workshop Proceedings",

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TY - GEN

T1 - A Manifesto for Applied Data Science - Reasoning from a Business Perspective

AU - Christiansen, Bahne

AU - Neuhaus, Uwe

AU - Schulz, Michael

AU - Hargreaves, Adrian

AU - Di Marco, Antinisca

AU - Proietti, Guido

AU - Rossi, Fabrizio

AU - Stilo, Giovanni

AU - Haney, Mark H.

AU - Duncan, Andrew

AU - Tessera, Daniele

AU - Todt, Tilman

AU - Brandenberg, Andreas

AU - Feubli, Patricia

PY - 2022/9

Y1 - 2022/9

N2 - Due to the increasing growth of the discipline Data Science, a partition into sub-disciplines seems appropriate. Therefore, we propose the division of Data Science in Pure Data Science, in which methods and tools are developed, and Applied Data Science, in which these methods and tools are adapted and applied to practical problems of a specific domain. This article focuses on Applied Data Science and how it should be positioned in relation to its adjoining disciplines. We also introduce the term Business Data Science as a specific form of Applied Data Science in the business domain and describe its relationship to existing terms like Business Analytics and Business Intelligence.

AB - Due to the increasing growth of the discipline Data Science, a partition into sub-disciplines seems appropriate. Therefore, we propose the division of Data Science in Pure Data Science, in which methods and tools are developed, and Applied Data Science, in which these methods and tools are adapted and applied to practical problems of a specific domain. This article focuses on Applied Data Science and how it should be positioned in relation to its adjoining disciplines. We also introduce the term Business Data Science as a specific form of Applied Data Science in the business domain and describe its relationship to existing terms like Business Analytics and Business Intelligence.

KW - Data Science

KW - Applied Data Science

KW - Pure Data Science

KW - Business Data Science

UR - https://ceur-ws.org/Vol-3340/paper31.pdf

M3 - Conference contribution

VL - 3340

BT - CEUR Workshop Proceedings

ER -

A Manifesto for Applied Data Science - Reasoning from a Business Perspective

Abstract

Keywords

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