doi: 10.3934/fods.2021031
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Evaluation of EDISON's data science competency framework through a comparative literature analysis

1. 

Trinity Christian College, 6601 West College Dr., Palos Heights, Illinois 60463, USA

2. 

Brown University, 164 Angell St., Providence, RI 02912, USA

3. 

Smith College, 44 College Lane, Northampton, MA 01063, USA

4. 

Valparaiso University, 1900 Chapel Dr., Valparaiso, IN 46383-6493, USA

5. 

Brown University, 182 George St., Box F, Providence, RI 02912, USA

* Corresponding author: Karl R. B. Schmitt

Received  July 2021 Revised  October 2021 Early access November 2021

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

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.

Citation: Karl R. B. Schmitt, Linda Clark, Katherine M. Kinnaird, Ruth E. H. Wertz, Björn Sandstede. Evaluation of EDISON's data science competency framework through a comparative literature analysis. Foundations of Data Science, doi: 10.3934/fods.2021031
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Y. Demchenko, A. Belloum, W. Los, T. Wiktorski and A. Manieri, et al., EDISON data science framework: A foundation for building data science profession for research and industry, IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Luxembourg, Luxembourg, 2016. doi: 10.1109/CloudCom.2016.0107.  Google Scholar

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Y. Demchenko, L. Comminiello and G. Reali, Designing customisable data science curriculum using ontology for data science competences and body of knowledge, Proceedings of the 2019 International Conference on Big Data and Education - ICBDE'19, ACM Press, London, United Kingdom, 2019,124–128. Google Scholar

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[24]

S. C. Hicks and R. A. Irizarry, A guide to teaching data science, Amer. Statist., 72 (2018), 382-391.  doi: 10.1080/00031305.2017.1356747.  Google Scholar

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T. K. Hira, Personal finance: Past, present and future, Networks Financial Institute Policy Brief, (2009), 23pp. doi: 10.2139/ssrn.1522299.  Google Scholar

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Joint Task Force on Computing Curricula, Association for Computing Machinery (ACM) and IEEE Computer Society, Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science, ACM, New York, NY, USA, 2013. Available from: https://www.acm.org/binaries/content/assets/education/cs2013_web_final.pdf. Google Scholar

[27]

A. Manieri, S. Brewer, R. Riestra, Y. Demchenko and M. Hemmje, et al., Data science professional uncovered: How the EDISON Project will contribute to a widely accepted profile for data scientists, IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), Vancouver, BC, Canada, 2015. doi: 10.1109/CloudCom.2015.57.  Google Scholar

[28]

P. W. G. MorrisL. CrawfordD. HodgsonM. M. Shepherd and J. Thomas, Exploring the role of formal bodies of knowledge in defining a profession - The case of project management, Internat. J. Project Management, 24 (2006), 710-721.  doi: 10.1016/j.ijproman.2006.09.012.  Google Scholar

[29]

National Academies of Sciences, Data Science for Undergraduates: Opportunities and Options, National Academies Press, 2018. Available from: https://www.nap.edu/catalog/25104/data-science-for-undergraduates-opportunities-and-options. Google Scholar

[30]

C. Pompa and T. Burke, Data science and analytics skills shortage: equipping the APEC workforce with the competencies demanded by employers, Asia-Pacific Economic Cooperation Secretariat, Singapore, 2017, https://www.apec.org/Publications/2017/11/Data-Science-and-Analytics-Skills-Shortage. Google Scholar

[31]

R. Rawlings-Goss, L. Cassel, M. Cragin, C. Cramer and A. Dingle, et al., Keeping Data Science Broad: Negotiating the Digital & Data Divide, Technical report, South Big Data Hub, 2018. Available from: https://par.nsf.gov/biblio/10075971. Google Scholar

[32] S. R. SingerN. R. Nielsen and H. A. Schweingruber, Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering, National Academies Press, 2012.  doi: 10.17226/13362.  Google Scholar
[33]

E. Van DusenA. SuenA. Liang and A. Bhatnagar, Accelerating the advancement of data science education, Proceedings of the 18th Python in Science Conference, (2019), 1-4.  doi: 10.25080/Majora-7ddc1dd1-000.  Google Scholar

[34]

M. A. Waller and S. E. Fawcett, Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management, J. Business Logistics, 34 (2013), 77-84.  doi: 10.1111/jbl.12010.  Google Scholar

[35]

J. M. Wing and D. Banks, Highlights of the inaugural data science leadership summit, Harvard Data Science Review, 1. doi: 10.1162/99608f92.e45fcb79.  Google Scholar

[36]

H. Wu, Systematic study of big data science and analytics programs, ASEE Annual Conference & Exposition Proceedings, ASEE Conferences, Columbus, Ohio, 2017. doi: 10.18260/1-2–28900.  Google Scholar

[37]

D. Yan and G. E. Davis, A first course in data science, J. Statist. Education, 27 (2019), 99-109.  doi: 10.1080/10691898.2019.1623136.  Google Scholar

[38]

P. Zorn, C. S. Schumacher and M. J. Siegel, 2015 CUPM Curriculum Guide to Majors in the Mathematical Sciences, The Mathematical Association of America, 2015. Available from: https://www.maa.org/sites/default/files/pdf/CUPM/pdf/CUPMguide_print.pdf. Google Scholar

show all references

References:
[1]

ACM Data Science Task Force. Available from: http://dstf.acm.org/. Google Scholar

[2]

AICPA, PFPBody of Knowledge. Available from: https://www.aicpa.org/interestareas/personalfinancialplanning/membership/pfsbodyofknowledge.html. Google Scholar

[3]

American Statistical Association, Curriculum guidelines for undergraduate programs in statistical science. Available from: http://www.amstat.org/education/curriculumguidelines.cfm. Google Scholar

[4]

P. Anderson, J. Bowring, R. McCauley, G. Pothering and C. Starr, An undergraduate degree in data science: Curriculum and a decade of implementation experience, Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE '14, ACM, New York, NY, USA, 2014,145–150. doi: 10.1145/2538862.2538936.  Google Scholar

[5]

P. Anderson, J. McGuffee and D. Uminsky, Data science as an undergraduate degree, Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE '14, ACM, New York, NY, USA, 2014,705–706. doi: 10.1145/2538862.2538868.  Google Scholar

[6]

J. Blitzstein, Teaching data science and storytelling, in The Data Science Handbook, Data Science Bookshelf, 2015,174–187. Google Scholar

[7]

Business-Higher Education Forum (BHEF, Webinar: Data science and analytics (dsa)-enabled graduate competency map | BHEF, 2019. Available from: https://s3.goeshow.com/dream/DataSummit/Data%20Summit%202018/BHEF_2016_DSA_competency_map_1.pdf. Google Scholar

[8]

I. Cárdenas-Navia and B. K. Fitzgerald, The broad application of data science and analytics: Essential tools for the liberal arts graduate, Change: The Magazine of Higher Learning, 47 (2015), 25-32.  doi: 10.1080/00091383.2015.1053754.  Google Scholar

[9]

B. Cassel and H. Topi, Strengthening Data Science Education Through Collaboration, Workshop on Data Science Education Workshop Report, 2015. Google Scholar

[10]

CC2020 Task Force, Computing Curricula 2020: Paradigms for Global Computing Education, ACM, New York, NY, USA, 2020. doi: 10.1145/3467967.  Google Scholar

[11]

M. Cetinkaya-Rundel, Computing infrastructure and curriculum design for introductory data science, Proceedings of the 50th ACM Technical Symposium on Computer Science Education, SIGCSE '19, ACM, New York, NY, USA, 2019, 1236–1236. doi: 10.1145/3287324.3287556.  Google Scholar

[12]

Civil Engineering Body of Knowledge 3 Task Committee, Civil Engineering Body of Knowledge: Preparing the Future Civil Engineer, 3$^rd$ edition, American Society of Civil Engineers, Reston, VA, 2019. doi: 10.1061/9780784415221.  Google Scholar

[13]

N. R. Council, Training Students to Extract Value from Big Data: Summary of a Workshop, The National Academies Press, Washington, DC, 2014. Available from: https://www.nap.edu/catalog/18981/training-students-to-extract-value-from-big-data-summary-of. Google Scholar

[14]

Data Science Association, About the Data Science Association. Available from: https://www.datascienceassn.org/. Google Scholar

[15]

R. D. De VeauxM. AgarwalM. AverettB. S. Baumer and A. Bray, Curriculum guidelines for undergraduate programs in data science, Ann. Rev. Statist. Appl., 4 (2017), 15-30.  doi: 10.1146/annurev-statistics-060116-053930.  Google Scholar

[16]

Y. Demchenko, A. Belloum, W. Los, T. Wiktorski and A. Manieri, et al., EDISON data science framework: A foundation for building data science profession for research and industry, IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Luxembourg, Luxembourg, 2016. doi: 10.1109/CloudCom.2016.0107.  Google Scholar

[17]

Y. Demchenko, L. Comminiello and G. Reali, Designing customisable data science curriculum using ontology for data science competences and body of knowledge, Proceedings of the 2019 International Conference on Big Data and Education - ICBDE'19, ACM Press, London, United Kingdom, 2019,124–128. Google Scholar

[18]

EDISON Project, Data science training and data science education - EU. Available from: http://edsa-project.eu/. Google Scholar

[19]

EDISON Project, EDISON: Building the data science profession. Available from: https://edison-project.eu/. Google Scholar

[20]

U. Fayyad and H. Hamutcu, Toward foundations for data science and analytics: A knowledge framework for professional standards, Harvard Data Science Review. Available from: https://hdsr.mitpress.mit.edu/pub/6wx0qmkl/release/2. Google Scholar

[21]

D. G. Freelon, ReCal: Intercoder reliability calculation as a Web service, Internat. J. Internet Sci., 5 (2010), 20–33. Available from: http://dfreelon.org/publications/2010_ReCal_Intercoder_reliability_calculation_as_a_web_service.pdf. Google Scholar

[22]

L. Haas, A. Hero and R. A. Lue, Highlights of the national academies report on "Undergraduate data science: Opportunities and options", Harvard Data Science Review, 1. Available from: https://hdsr.mitpress.mit.edu/pub/z4sb5j9l/release/3. Google Scholar

[23]

J. HardinR. Hoerl and N. J. Horton, Data science in statistics curricula: Preparing students to "think with data", Amer. Statist., 69 (2015), 343-353.  doi: 10.1080/00031305.2015.1077729.  Google Scholar

[24]

S. C. Hicks and R. A. Irizarry, A guide to teaching data science, Amer. Statist., 72 (2018), 382-391.  doi: 10.1080/00031305.2017.1356747.  Google Scholar

[25]

T. K. Hira, Personal finance: Past, present and future, Networks Financial Institute Policy Brief, (2009), 23pp. doi: 10.2139/ssrn.1522299.  Google Scholar

[26]

Joint Task Force on Computing Curricula, Association for Computing Machinery (ACM) and IEEE Computer Society, Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science, ACM, New York, NY, USA, 2013. Available from: https://www.acm.org/binaries/content/assets/education/cs2013_web_final.pdf. Google Scholar

[27]

A. Manieri, S. Brewer, R. Riestra, Y. Demchenko and M. Hemmje, et al., Data science professional uncovered: How the EDISON Project will contribute to a widely accepted profile for data scientists, IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), Vancouver, BC, Canada, 2015. doi: 10.1109/CloudCom.2015.57.  Google Scholar

[28]

P. W. G. MorrisL. CrawfordD. HodgsonM. M. Shepherd and J. Thomas, Exploring the role of formal bodies of knowledge in defining a profession - The case of project management, Internat. J. Project Management, 24 (2006), 710-721.  doi: 10.1016/j.ijproman.2006.09.012.  Google Scholar

[29]

National Academies of Sciences, Data Science for Undergraduates: Opportunities and Options, National Academies Press, 2018. Available from: https://www.nap.edu/catalog/25104/data-science-for-undergraduates-opportunities-and-options. Google Scholar

[30]

C. Pompa and T. Burke, Data science and analytics skills shortage: equipping the APEC workforce with the competencies demanded by employers, Asia-Pacific Economic Cooperation Secretariat, Singapore, 2017, https://www.apec.org/Publications/2017/11/Data-Science-and-Analytics-Skills-Shortage. Google Scholar

[31]

R. Rawlings-Goss, L. Cassel, M. Cragin, C. Cramer and A. Dingle, et al., Keeping Data Science Broad: Negotiating the Digital & Data Divide, Technical report, South Big Data Hub, 2018. Available from: https://par.nsf.gov/biblio/10075971. Google Scholar

[32] S. R. SingerN. R. Nielsen and H. A. Schweingruber, Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering, National Academies Press, 2012.  doi: 10.17226/13362.  Google Scholar
[33]

E. Van DusenA. SuenA. Liang and A. Bhatnagar, Accelerating the advancement of data science education, Proceedings of the 18th Python in Science Conference, (2019), 1-4.  doi: 10.25080/Majora-7ddc1dd1-000.  Google Scholar

[34]

M. A. Waller and S. E. Fawcett, Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management, J. Business Logistics, 34 (2013), 77-84.  doi: 10.1111/jbl.12010.  Google Scholar

[35]

J. M. Wing and D. Banks, Highlights of the inaugural data science leadership summit, Harvard Data Science Review, 1. doi: 10.1162/99608f92.e45fcb79.  Google Scholar

[36]

H. Wu, Systematic study of big data science and analytics programs, ASEE Annual Conference & Exposition Proceedings, ASEE Conferences, Columbus, Ohio, 2017. doi: 10.18260/1-2–28900.  Google Scholar

[37]

D. Yan and G. E. Davis, A first course in data science, J. Statist. Education, 27 (2019), 99-109.  doi: 10.1080/10691898.2019.1623136.  Google Scholar

[38]

P. Zorn, C. S. Schumacher and M. J. Siegel, 2015 CUPM Curriculum Guide to Majors in the Mathematical Sciences, The Mathematical Association of America, 2015. Available from: https://www.maa.org/sites/default/files/pdf/CUPM/pdf/CUPMguide_print.pdf. Google Scholar

19] into the List of Topics that were investigated in our combined studies">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
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
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%
Park City IADSS Wu BHEF ACM
Count of Topics 31 43 43 45 58
% Coverage of EDISON CF-DS 44% 61% 61% 63% 82%
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%
Agreement Indeterminate Total Consensus
Positive Negative
Count 34 14 23 48
Percent 48% 20% 32% 68%
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%
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%
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%
Agreement Indeterminate Total Consensus
Positive Negative
Count 15 41 15 56
Percent 21% 58% 21% 79%
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