What can Venn diagrams teach us about doing data science better?

Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ho, Sung Yang [verfasserIn] Tan, Sophia [verfasserIn] Sze, Chun Chau [verfasserIn] Wong, Limsoon [verfasserIn] Goh, Wilson Wen Bin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Exploratory data analysis Data science Graph literacy Visualization

Übergeordnetes Werk:	Enthalten in: International journal of data science and analytics - Cham, Switzerland : Springer International Publishing, 2016, 11(2020), 1 vom: 02. Aug., Seite 1-10
Übergeordnetes Werk:	volume:11 ; year:2020 ; number:1 ; day:02 ; month:08 ; pages:1-10

Links:	Volltext

DOI / URN:	10.1007/s41060-020-00230-4

Katalog-ID:	SPR042893798

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allfields	10.1007/s41060-020-00230-4 doi (DE-627)SPR042893798 (DE-599)SPRs41060-020-00230-4-e (SPR)s41060-020-00230-4-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Ho, Sung Yang verfasserin aut What can Venn diagrams teach us about doing data science better? 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis. Exploratory data analysis (dpeaa)DE-He213 Data science (dpeaa)DE-He213 Graph literacy (dpeaa)DE-He213 Visualization (dpeaa)DE-He213 Tan, Sophia verfasserin aut Sze, Chun Chau verfasserin aut Wong, Limsoon verfasserin aut Goh, Wilson Wen Bin verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 1 vom: 02. Aug., Seite 1-10 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:1 day:02 month:08 pages:1-10 https://dx.doi.org/10.1007/s41060-020-00230-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 1 02 08 1-10
spelling	10.1007/s41060-020-00230-4 doi (DE-627)SPR042893798 (DE-599)SPRs41060-020-00230-4-e (SPR)s41060-020-00230-4-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Ho, Sung Yang verfasserin aut What can Venn diagrams teach us about doing data science better? 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis. Exploratory data analysis (dpeaa)DE-He213 Data science (dpeaa)DE-He213 Graph literacy (dpeaa)DE-He213 Visualization (dpeaa)DE-He213 Tan, Sophia verfasserin aut Sze, Chun Chau verfasserin aut Wong, Limsoon verfasserin aut Goh, Wilson Wen Bin verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 1 vom: 02. Aug., Seite 1-10 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:1 day:02 month:08 pages:1-10 https://dx.doi.org/10.1007/s41060-020-00230-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 1 02 08 1-10
allfields_unstemmed	10.1007/s41060-020-00230-4 doi (DE-627)SPR042893798 (DE-599)SPRs41060-020-00230-4-e (SPR)s41060-020-00230-4-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Ho, Sung Yang verfasserin aut What can Venn diagrams teach us about doing data science better? 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis. Exploratory data analysis (dpeaa)DE-He213 Data science (dpeaa)DE-He213 Graph literacy (dpeaa)DE-He213 Visualization (dpeaa)DE-He213 Tan, Sophia verfasserin aut Sze, Chun Chau verfasserin aut Wong, Limsoon verfasserin aut Goh, Wilson Wen Bin verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 1 vom: 02. Aug., Seite 1-10 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:1 day:02 month:08 pages:1-10 https://dx.doi.org/10.1007/s41060-020-00230-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 1 02 08 1-10
allfieldsGer	10.1007/s41060-020-00230-4 doi (DE-627)SPR042893798 (DE-599)SPRs41060-020-00230-4-e (SPR)s41060-020-00230-4-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Ho, Sung Yang verfasserin aut What can Venn diagrams teach us about doing data science better? 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis. Exploratory data analysis (dpeaa)DE-He213 Data science (dpeaa)DE-He213 Graph literacy (dpeaa)DE-He213 Visualization (dpeaa)DE-He213 Tan, Sophia verfasserin aut Sze, Chun Chau verfasserin aut Wong, Limsoon verfasserin aut Goh, Wilson Wen Bin verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 1 vom: 02. Aug., Seite 1-10 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:1 day:02 month:08 pages:1-10 https://dx.doi.org/10.1007/s41060-020-00230-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 1 02 08 1-10
allfieldsSound	10.1007/s41060-020-00230-4 doi (DE-627)SPR042893798 (DE-599)SPRs41060-020-00230-4-e (SPR)s41060-020-00230-4-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Ho, Sung Yang verfasserin aut What can Venn diagrams teach us about doing data science better? 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis. Exploratory data analysis (dpeaa)DE-He213 Data science (dpeaa)DE-He213 Graph literacy (dpeaa)DE-He213 Visualization (dpeaa)DE-He213 Tan, Sophia verfasserin aut Sze, Chun Chau verfasserin aut Wong, Limsoon verfasserin aut Goh, Wilson Wen Bin verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 1 vom: 02. Aug., Seite 1-10 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:1 day:02 month:08 pages:1-10 https://dx.doi.org/10.1007/s41060-020-00230-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 1 02 08 1-10
language	English
source	Enthalten in International journal of data science and analytics 11(2020), 1 vom: 02. Aug., Seite 1-10 volume:11 year:2020 number:1 day:02 month:08 pages:1-10
sourceStr	Enthalten in International journal of data science and analytics 11(2020), 1 vom: 02. Aug., Seite 1-10 volume:11 year:2020 number:1 day:02 month:08 pages:1-10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Exploratory data analysis Data science Graph literacy Visualization
dewey-raw	004
isfreeaccess_bool	false
container_title	International journal of data science and analytics
authorswithroles_txt_mv	Ho, Sung Yang @@aut@@ Tan, Sophia @@aut@@ Sze, Chun Chau @@aut@@ Wong, Limsoon @@aut@@ Goh, Wilson Wen Bin @@aut@@
publishDateDaySort_date	2020-08-02T00:00:00Z
hierarchy_top_id	84425083X
dewey-sort	14
id	SPR042893798
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042893798</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220112044253.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210129s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41060-020-00230-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042893798</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs41060-020-00230-4-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41060-020-00230-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ho, Sung Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">What can Venn diagrams teach us about doing data science better?</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exploratory data analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data science</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph literacy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visualization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tan, Sophia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sze, Chun Chau</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong, Limsoon</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Goh, Wilson Wen Bin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of data science and analytics</subfield><subfield code="d">Cham, Switzerland : Springer International Publishing, 2016</subfield><subfield code="g">11(2020), 1 vom: 02. 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author	Ho, Sung Yang
spellingShingle	Ho, Sung Yang ddc 004 misc Exploratory data analysis misc Data science misc Graph literacy misc Visualization What can Venn diagrams teach us about doing data science better?
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topic_title	004 ASE What can Venn diagrams teach us about doing data science better? Exploratory data analysis (dpeaa)DE-He213 Data science (dpeaa)DE-He213 Graph literacy (dpeaa)DE-He213 Visualization (dpeaa)DE-He213
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title_sort	what can venn diagrams teach us about doing data science better?
title_auth	What can Venn diagrams teach us about doing data science better?
abstract	Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis.
abstractGer	Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis.
abstract_unstemmed	Abstract Data science is about deriving insight, learning and understanding from data. This process may be automated via the use of advanced algorithms or scaffolded cognitively via the use of graphs. While much emphasis is currently placed on machine learning, there is still much to learn about the role of the data scientist, in particular the thinking process by which he reaches conclusions. The thinking process of the data scientist needs to be scaffolded as the human brain is easily overwhelmed by many variables. Graphs are a form of data abstraction and constitute an essential part of the data scientist’s toolkit. Graphs are also a viable scaffold on which the data scientist may gain familiarity with data. But the process of extracting insight from graphs is not always a trivial or straightforward process; it requires interpretative logic as well. Generalizing from the example of a simple graph type, the Venn diagram, we discuss various logical fallacies that can be committed when interpreting a Venn diagram. Amidst various considerations that dictate how a graph should be tackled, we explain why context is most important, and should form the first guiding principle during data analysis.
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container_issue	1
title_short	What can Venn diagrams teach us about doing data science better?
url	https://dx.doi.org/10.1007/s41060-020-00230-4
remote_bool	true
author2	Tan, Sophia Sze, Chun Chau Wong, Limsoon Goh, Wilson Wen Bin
author2Str	Tan, Sophia Sze, Chun Chau Wong, Limsoon Goh, Wilson Wen Bin
ppnlink	84425083X
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s41060-020-00230-4
up_date	2024-07-03T15:26:24.109Z
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