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Evaluation of EDISON's data science competency framework through a comparative literature analysis

  • * Corresponding author: Karl R. B. Schmitt

    * Corresponding author: Karl R. B. Schmitt 

The authors were supported by NSF grants # 1839257, # 1839259, # 1839270, and by the Luce Foundation under the Clare Boothe Luce Program

Abstract Full Text(HTML) Figure(1) / Table(5) Related Papers Cited by
  • During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or course materials. We identify areas of strong agreement and disagreement with the framework. Results from the literature analysis provide strong insights into what topics the broader community see as belonging in (or not in) Data Science, both at curricular and course levels. This analysis can provide important guidance for groups working to formalize the discipline and any college or university looking to build their own undergraduate Data Science degree or programs.

    Mathematics Subject Classification: Primary: 97K99, 97B10; Secondary: 97B70, 68T09.

    Citation:

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  • Figure 1.  Examples of the reduction process and resulting item counts for merging items from EDISON Core Data Science Skills Table & EDISON Knowledge Table from [19] into the List of Topics that were investigated in our combined studies

    Table 1.  Unique Item Counts by Knowledge/Skill in each competency category. The merged row indicates how often a Knowledge and Skill in that competency are merged. There are five acronyms in this table: DSDA stands for Data Science Analytics, DSENG stands for Data Science Engineering, DSDM stands for Data Management, DSRM stands for Research Methods and Project Management, and finally DSBA stands for Domain Related Competencies and Business Analytics Competencies

    DSDA DSENG DSDM DSRM DSBA Total
    Skill 6 4 6 5 2 23
    Knowledge 7 2 6 5 1 21
    Merged 8 8 3 1 7 27
    Total 21 14 15 11 10 71
     | Show Table
    DownLoad: CSV

    Table 2.  Counts & Coverage Percentage of Topics from EDISON DSF for each Curricular-Level data source

    Park City IADSS Wu BHEF ACM
    Count of Topics 31 43 43 45 58
    % Coverage of EDISON CF-DS 44% 61% 61% 63% 82%
     | Show Table
    DownLoad: CSV

    Table 3.  Summary of topic agreement from literature analysis of EDISON CF-DS items that should persist (or not) in Data Science

    Agreement Indeterminate Total Consensus
    Positive Negative
    Count 34 14 23 48
    Percent 48% 20% 32% 68%
     | Show Table
    DownLoad: CSV

    Table 4.  Counts & Coverage Percentage of Topics from EDISON DSF for each Course-level data source

    Source Hardin et al. Dusen et al. Data-8
    Smith Auckland UC B/D St. Olaf Purdue (various schools)
    Count of Topics 22 7 25 20 24 23
    % Coverage 31% 10% 35% 28% 34% 32%
    Source Cetinkaya-Rundel Data-Science-Box (various schools) Yan and Davis U.Massachusetts Dartmouth European DSA Foundations of Data Science Big Data (2 courses)
    Count of Topics 24 23 28
    % Coverage 34% 32% 39%
     | Show Table
    DownLoad: CSV

    Table 5.  Summary of topic agreement from literature analysis of EDISON CF-DS items that should be included (or not) in an introductory in Data Science course

    Agreement Indeterminate Total Consensus
    Positive Negative
    Count 15 41 15 56
    Percent 21% 58% 21% 79%
     | Show Table
    DownLoad: CSV
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