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

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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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Abbreviations

DS:

Data science

AI:

Artificial intelligence

ML:

Machine learning

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Funding

WWBG and CCS gratefully acknowledge support from the Accelerating Creativity and Excellence (ACE) and EdeX grants from Nanyang Technological University, Singapore. WWBG also acknowledges an NRF-NSFC (Grant No. NRF2018NRF-NSFC003SB-006). LW acknowledges support from a Kwan Im Thong Hood Cho Temple Chair Professorship and the National Research Foundation Singapore under its AI Singapore Programme (Grant No. AISG-100E−2019-027 and AISG-100E−2019-028).

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Authors and Affiliations

  1. School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore

    Sung Yang Ho, Chun Chau Sze & Wilson Wen Bin Goh

  2. Teaching Learning and Pedagogy Division, Nanyang Technological University, Singapore, 637551, Singapore

    Sophia Tan

  3. Department of Computer Science, National University of Singapore, Singapore, 117417, Singapore

    Limsoon Wong

Authors
  1. Sung Yang Ho
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  2. Sophia Tan
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  3. Chun Chau Sze
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  4. Limsoon Wong
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  5. Wilson Wen Bin Goh
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Contributions

HSY contributed to the initial drafting of the manuscript and the development of the figures. ST and CCS provided insight on the education and scientific logic implications. LW and WWBG initiated the project and supervised. All authors contributed towards writing.

Corresponding authors

Correspondence to Limsoon Wong or Wilson Wen Bin Goh.

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The authors have declared no conflicts of interest.

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Ho, S.Y., Tan, S., Sze, C.C. et al. What can Venn diagrams teach us about doing data science better?. Int J Data Sci Anal 11, 1–10 (2021). https://doi.org/10.1007/s41060-020-00230-4

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  • DOI: https://doi.org/10.1007/s41060-020-00230-4

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