{'fraction_non_alphanumeric': 0.028643492882613864, 'fraction_numerical': 0.00881338242541965, 'mean_word_length': 5.083597883597884, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 2, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
EVALUATION OF EDISON’S DATA SCIENCE COMPETENCY FRAMEWORK THROUGH A COMPARATIVE LITERATURE ANALYSIS

. During the emergence of Data Science as a distinct discipline, dis-cussions of what exactly constitutes Data Science have been a source of con- tention, 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.

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.
1. Introduction. The rapid emergence and growth of Data Science 1 as its own discipline has been directly fueled by increased societal demands for combinations of advanced quantitative skills across many domains [8,30,34]. Given the interdisciplinarity of Data Science, and its co-emergence from many established disciplines, the absence of a single authoritative voice has presented challenges to defining core concepts and skills that are essential to professional competence and success. What is more, the scope of what is included in Data Science industry applications is evolving at a pace that is difficult for the academy and its institutions to match. The result is a new discipline that many are eager to claim and/or join, but that lacks a clear identity to serve as the central basis of educational curricula, research, and development. Identifying the core concepts of a well-defined Data Science discipline is further complicated by the highly varied pathways by which current industry leaders have acquired their skill sets and/or job titles. The practical skills that are now being included in Data Science by industry have long been essential in a diverse range of professions. Current "data scientist" job postings call for a mix of skills that have often been taught for decades, though spread out across several domains. What may be striking to realize is that few of the current mid-to-senior level "Data Scientist" ever received formal training in a full range of Data Science skills under an established Data Science degree or program, given that these degrees did not formally exist before 2012 [4,5]. Rather, they took on the nomenclature of "data scientist" as a new title for the mix of skills they had acquired through their work and professional development, while their original degrees were from parental disciplines like computer science, electrical engineering, mathematics, physics, or statistics.
While rapid growth has presented challenges, broad expansion of Data Science job descriptions, degree programs, and published curricula also present an opportunity. It is possible to assess emergent patterns of what is considered to be central to the discipline from multiple stakeholders and perspectives [9,15,29,31]. These patterns can provide both a framework and a common lexicon that guide the prioritization and development of educational curricula and discipline-based education research within Data Science. To better understand the scope and importance of this problem, we look to three primary levers of influence in the formation and identity of disciplines. These levers include professional societies, institutions of higher education, and national/international organizations that operate outside of the boundaries of any single institution.
1.1. Professional societies. Most academic disciplines include mechanisms by which a standardized core of knowledge has been established. For some, there are professional societies that produce curriculum guidelines for degree programs such as [3] for statistics and [38] for mathematics, while other disciplines have defined formal bodies of knowledge (BoKs), for example Project Management [28], Civil Engineering [12], and Finance/Accounting [2,25]. Some guidelines and BoKs, such as the ACM/IEEE Computing Curricula 2020 report [10], are even collaboratively developed between multiple professional societies of closely related fields that have broad areas of cross-disciplinary overlap.
Curricular products generated by professional societies provide an important scaffolding for educational priorities that are grounded in professional practice. While BoKs fall into this category, it is worth noting that a formal BoK is distinct from curricular recommendations. A BoK can be thought of as a map that defines the breadth and depth of knowledge, skills, and attitudes recognized as belonging in the discipline. Curricular recommendations can then specify from the discipline's BoK how much, and in what combination, topics should be included for a given course, degree track, or program. For example, in the ACM/IEEE curricular recommendations for Computer Science (CS-2013), the guidelines specify the number of instruction hours student should receive in/on various knowledge areas [26].
In the case of Data Science, most of the current curricular guidance for Data Science has come from existing societies that have disciplinary overlap with Data Science, mainly those in Business, Computer Science, and Statistics. While there are a few professional societies that focus solely on the discipline, e.g. the Data Science Association [14], they have minimal membership overlap with academia. The professional societies who have contributed to curricular guidance often approach Data Science from the unique, and sometimes narrow, perspectives and identities of their respective disciplines. This has created a quagmire of curricular recommendations, with no obvious authoritative voice to help design a path forward. The discipline's disparate societal voices are contrasted by the several smaller, more complete efforts at defining full degree curricula. Examples of these more constrained effort are the Workshop on Data Science Education Report [9], the South Big Data Hub's Keeping Data Science Broad series [31] and the Park City Report [15].
1.2. Institutions of higher education. Institutions of higher education have been racing to meet the growing demand for data professionals. Hence the focus has been on repackaging existing courses and developing new courses that equip students with the necessary Data Science skills. Combined, these repackaged and new courses have been grouped into a sharply increasing set of new Data Science programs at the undergraduate and graduate levels.
Collectively, these degree programs and courses have produced a source of peer data that can be examined to identify variations in scope, and areas of consensus.
Two early examples of this approach are Wu [36] and Hardin et al. [23], who identified program-level competencies and compared early introductory Data Science courses, respectively. To illustrate the growth in this area, we note that the 2017 Wu paper states the existence of approximately 100 programs, while the list of Data Science Colleges and Universities contains over 600 programs as of January 1st, 2021. The latter list provides an excellent source for accessing peer data and performing fine-grained comparisons. Yet that information may be incomplete due to self-selection bias, difficult to organize and mine due to varied formats, or have search capabilities that do not customize well to specific needs. We are aware of at least two projects currently in development that use natural language processing and text mining to examine these sources, but neither project has been completed.

National and international organizations.
Other bodies with significant influence in defining the identity of Data Science include committees, task forces, and funding agencies that operate on broad national and international levels. Project reports and agendas at this scale tend to have lasting impact by driving research agendas and disciplinary knowledge development. For example, the EU has sponsored two such projects, the EDISON Project (www.edison-project.eu) [19] and the European Data Science Academy (EDSA, www.edsa-project.eu) [18], that significantly contributed to defining the field [16,17,27]. We can see evidence of their influence in the citations of the ACM Curricular guide to the EDISON report and the subsequent data programs that have been created throughout Europe. Similarly, in the US, the National Science Foundation has had multiple calls for Data Science Educational development (e.g. NSF 18-542 and NSF 19-518/21-523).
Three other recent and current efforts are particularly noteworthy. First, in 2018 the U.S. National Academy of Sciences, Engineering, and Medicine [22] released broad curriculum recommendations and maintains an ongoing working group exploring the topic. Second, during the spring of 2021 the Association of Computing Machinery (ACM) Education Board approved the final report from their Data Science Task Force [1]. Third, in the summer and fall of 2021 two Data Science programs will undergo a preliminary accreditation process through ABET via the Computing Accreditation Commission (CAC). Many of these groups are working on producing recommendations and requirements. Others are simply looking for guidance on implementing their own programs or supporting the broad development of the academic ecosystem for teaching Data Science. These organizations include the Academic Data Science Alliance [35] and individual colleges and universities.
1.4. Problem statement. Partly despite, and partly because of the rapid and distributed expansion of Data Science, the discipline still lacks cohesion. This lack encompasses everything from defining the basic scope of the field to detailed lists of foundational skills and knowledge that Data Science practitioners should have. Such cohesion is important to effectively transition discipline-based educational research in Data Science from experience reports and case-studies to formalized observational or experimental educational research. The value of such a transition is evident in the many other disciplines that made this shift in the past [32]. A fully 'comprehensive' research design to develop such a cohesive framework in a timely manner would be near impossible given the rate of change in practice here. Instead, we evaluated the most comprehensive body of work available at the time of our research, namely the EDISON Data Science Framework (EDSF) 2 , as a potential foundation for educational research in Data Science within the broader international community.
Our premise is that, without a clear authoritative guide, community consensus among multiple stakeholders can provide this evaluation. To evaluate the EDSF as a foundation for educational curricula and research we investigated patterns 2 Throughout this paper we reference three distinct products from the EDISON Project, their 'Data Science Framework' or EDSF, their 'Competency Framework for Data Science' or CF-DS, and their 'Data Science Body of Knowledge' or DS-BoK. The most recent documentation for these products can be found at https://github.com/EDISONcommunity/EDSF, while a more publicfriendly version can be found at https://edison-project.eu/. Because the sub-documents are difficult to reference as they do not have associated meta-data, the formal citations used throughout the paper are simply to the project homepage. A journal article overview from 2016 is also available, see [16]. of consensus among key sources of disciplinary influence through a review of the literature. Guiding our investigations are two questions: 1. Which of the topics identified in the EDISON project have persisted since its publication? 2. Which of the topics identified in the EDISON project are included in a first Data Science course? We have chosen to include the second research question for two reasons. First, within discipline-based educational research the 'first' course in a discipline is studied far more often than advanced topics, as it generally enrolls the most students and often serves as non-majors' first or only engagement with the field. Second, a first course in a discipline often focuses on skill sets that define the identity of the discipline.
To answer our study questions, we perform a comparison of relevant public documents, including formal reports, peer-reviewed publications, and openly disseminated course materials.
2. Motivation and background. As part of an EU-funded effort to accelerate the creation of the Data Science profession, the EDSF project produced a comprehensive competency framework. 3 The framework included both a defined body of knowledge (DS-BoK) as well as a Competency Framework for Data Science (CF-DS). In generating this framework, the EDISON project team went through a lengthy data collection process, multiple refinements, and developed a complete model curriculum for a degree [19]. The CF-DS was developed around the following five major knowledge area groups: • Data Analytics (DSDA) These knowledge areas are defined by the DS-BoK as the core areas required for developing Data Science curricula, which in turn corresponds to the CF-DS which defines the explicit skills and knowledge that exemplify competence in these areas. While a BoK is ideal for directing nuanced educational research, most published literature outside of educational research (such as curricular guides, case study reports, etc) usually refers to student learning outcomes or competencies rather than knowledge [17]. Within the CF-DS the five major competency groups were further expanded, based on a job-market analysis, to include specific skills and knowledge topics required to support the competencies. We use the term EDISON Data Science Framework (EDSF) when referring to the overall EDISON project, which includes the both the CF-DS and DS-BoK among other components that extend beyond identification of core competencies. For the purpose of our analysis, we pulled competency items from the CF-DS. We refer to Tables 4.2 and 4.4 of their Competency Framework documentation [19] for the list of skills as "the EDISON Core Data Science Skills Table" and the knowledge units as "the EDISON Knowledge Table." The EDISON project [19] was selected as the basis of our analysis. It was chosen because, to our knowledge, it represents the first published set of materials which attempt to fully articulate what knowledge and skills belong in the field of Data Science. We considered other frameworks, but found these to be less comprehensive in the case of ACM [1], 4 while in the case of the Initiative for Analytics and Data Science (IADSS) the body of knowledge was still a work-in-progress [20].
2.1. Preprocessing EDISON Data Science Framework. Rather than using the full Data Science Body of Knowledge (DS-BoK), our study focuses a modified version of EDISON's Competency Framework for Data Science (CF-DS). This decision was motivated by: 1. Concerns that practitioners or specialized instructors would be unfamiliar with, or use alternative terms for, many of the more fine-grained technical BoK topics. 2. Recognition that, with the exception of the ACM report, the literature sources present knowledge topics at a significantly more abstract level than the detailed level of a BoK. 3. A desire to collect information on first-course topics while recognizing that many technical BoK topics would never occur in a first course. The EDISON CF-DS distinguishes between Core Skills (in the EDISON Core Data Science Skills Table) and Knowledge (in the EDISON Knowledge Table) [19]. Using the complete list of both skills and knowledge would have introduced a significant amount of duplication, as the originals were not intended to be directly combined. On the other hand, many elements were unique to one of the two tables. We therefore generated a list of unique knowledge and skills from the EDISON CF-DS relying on our disciplinary knowledge. We examined both tables for any knowledge areas or skills that appeared as near-duplicates, which were then were combined into one item to create our final list of competencies. An example is shown in Figure 1. A breakdown of the number of topics and duplicates in these two tables and the count of unique topics in our final list are shown in Table 1. The resulting list of competencies is the content we wish to explore and compare against existing literature. The full list of competencies used is provided in the Appendix A.
3. Study methodology and analysis. We conduct a comparison of the EDISON CF-DS to other bodies of curricular literature. In this section, we interpret our overarching research questions from Section 1.4 as: To what extent do scholars and researchers perceive, as encoded in published, peer-reviewed documents, the skills and knowledge identified by the EDISON Project as...
• Central to Data Science?
• Appropriate for a first course in Data Science?
We consider two distinct corpora: (1) curricular level competencies, and (2) introductory course topics. Both corpora encode the opinions of instructors who are shaping Data Science as an academic discipline. We view these corpora as proxies for the collective opinions of departments and programs. We note the limitation that these sources are limited to those developed largely in European and English  speaking countries. There may be inherent biases in these literature sources that a multilingual and broader cultural inclusion criteria may illuminate.

Curricular level competencies.
To identify potential data sources for this comparison, we sought documents that provided a high-level list of competencies that undergraduate Data Science students should learn. Our first two sources were the incomplete BoK's from ACM [1] and IDASS [20]. In both cases competencies directly comparable to EDISON's CF-DS are explicitly listed. In addition to these documents, the Business Higher Education Forum (BHEF) has published a Data Science and Analytics Competency Map [7]. This map is a list of Data Science concepts and principles tiered into when and where these concepts are learned, such as in college or in a work experience. A much less structured source of competencies are the curricular contents of the many Data Science programs and majors that have been formed [13]. Typically these have followed curricular recommendations like those from De Veaux et al. [15], the National Research Council [13], Anderson et al. [4,5], Blitzstein [6], and Hicks and Irizarry [24]. These recommendations typically provide broad statements of the content or courses that should be included while usually glossing over the details such as specific sub-classes of algorithms. For our analysis we will use the most commonly referenced recommendations known as "the Park City Report" [15].
The existence of a large number of successful programs also allows us to adopt a reverse approach. Rather than stating what should be in a curriculum, the existing programs can be analyzed to identify what is common across them. Wu [36] conducted such a systematic study of existing Data Science and Analytics programs, where he reported out a list of competencies that all graduates of those degrees are expected to have. For our purposes, these results will be treated as a set of curricular level 'recommendations' for what should be included in a program.
3.2. Introductory course topics. For this comparison, we considered a variety of published materials about first courses in Data Science. Rather than examining individual syllabi, we identify five well-cited papers whose models have been regularly adopted at other institutions. Our curriculum comparisons against the EDISON CF-DS included the courses in Hardin et al. [23] (except for the 36-week 'Data Science Specialization' program) and four more recent courses: • Data-Science-in-a-Box by Dr. Cetinkaya-Rundel [11] • Foundations in Data Science (Data 8), originating at U.C. Berkeley [33] • DSC101 at Univ. Massachusetts, Dartmouth [37] • Foundations of Data Science & Foundations of Big Data by the European Data Science Academy (EDSA) [18] 3.3. Corpora analysis methods. We treated the topics listed in the EDISON CF-DS as 'ground truth.' For each topic in this competency framework, we cross checked which of the documents in Section 3.1 and courses from Section 3.2 included, or not included, it. To avoid undercounting, we erred in favor of inclusion; that is, if it could be assumed that the document examined included at least partial coverage of the EDISON topic, it was marked as included. We then quantified the level of agreement in the resulting tables as follows. To assess overall averaged agreement across all courses, we compute a Krippendorff-Alpha (k α ) score using the online RelCal site [21]. Typically, k α is used to measure the agreement between multiple 'coders' or qualitative ratings of items. In this work, the 'coders' are the source documents that indicate whether a topic in the CF-DS belongs in Data Science or not, according to their perspective (as stated in the source document). The k α is a similarity measure. A k α = 0 indicates systematic disagreement, while a k α = 1 indicates perfect agreement of either inclusion or exclusion. While the k α score takes into account any amount of partial positive or negative agreement, it does not provide more insight into which items caused a decrease in the score. To assist our identification of these points of disagreement we generated two derivative encodings, defining two types of 'agreement.' For topics included in at least four of the five curricular sources (noted in Section 3.1), we say that it has 'positive agreement' that the topic should persist (research question 1) or be included (research question 2) within a Data Science curriculum or course, respectively. Similarly any topic excluded in at least four of the curricular sources is considered to have 'negative agreement', meaning that a topic should neither persist nor be included within a Data Science curriculum or course, respectively. Topics that have neither kind of agreements are labeled as indeterminate. This derivative encoding provides more insight into specific k α scores.

Corpora analysis results.
In the results of our corpora analysis, we provide the number of unique topics from the EDISON CF-DS that are covered by each document. We investigate the amount of inter-document agreement within each level. What is interesting (though not surprising) is that there is less consensus around what should be included in a Data Science curriculum as a whole (Section 4.1) than what should not be included in a first course about Data Science (Section 4.2).

4.1.
Comparison of curricular-level competencies to the CF-DS. Overall, in Table 2 we can see that the coverage by the current ACM guidelines is best aligned with the EDISON competencies. This was to be expected, as they referenced the EDISON framework and the other documents as background work [7,15,19]. Furthermore, we can immediately see that current curriculum implementations (from Wu [36]) cover more topics (61% vs. 44%) than originally specified in the Park City Report [15] but less than the potential specifications coming from ACM (61% vs. 82%). When considering all five curricular sources, we find k α = 0.288 indicating a relatively low level of agreement among the sources. Since the ACM curriculum had a dramatically higher coverage of EDISON, we were curious if the other four documents were in more agreement with each other. Therefore we also computed k α excluding the ACM mapping. Here we find a slightly higher agreement of k α = 0.316. However, this value still indicates a high level of disagreement on what should or should not be covered in the curriculum. To break this down further, we provide in Table 3 a count of positive or negative agreement, and indeterminate topics based on our definitions in the methods section. We also consolidate positive and negative agreement to indicate overall consensus in the community. Thus based on the literature analyzed here, we observe that the community has reached consensus on 48 curricular topics, including the persistence of 33 topics from the EDISON CF-DS and the removal of 14 topics. The curricular literature did not reach a consensus on the remaining 23 topics, where the dissenting sources seem to mostly follow disciplinary lines. There are several examples of singular dissenting opinions. For instance, the Park City report and the IADSS framework had one topic which they each explicitly included that the other sources did not. Park City included competencies on 'simulation' and the IADSS included 'operations research.' The Park City report also included a significant description of various mathematical topics. However, the EDISON CF-DS makes a distinction between optimization, which the Park City report explicitly mentions, and operations research, which it did not explicitly mention. Both the ACM and BHEF frameworks had multiple items that were uniquely included, largely reflecting their disciplinary origins in Computing and Business respectively. Not surprisingly, Wu [36] based on realized programs had no unique inclusions.

4.2.
Comparison of introductory course literature. Conducting our analysis for agreement on the first-course corpus produced the results found in Appendix C and summarized in Table 4. Examining the overall amount of coverage in each course produces a fairly consistent value of slightly over 30%. The exception to this level of coverage is the course from the University of Auckland. However, Hardin et al. [23] explicitly call out that course as being late in a student's experience, rather than a 'first course' like the others presented here. Additionally, even though the EDSA's foundational courses together achieve a slightly higher coverage, that occurs largely because we have chosen to include two courses rather than one. Since the EDSA curriculum was designed from the ground up to map to the EDISON CF-DS, they are able to more explicitly differentiate within their courses when specific topics were covered. This led each individual course to have a far lower coverage (not shown).
Overall, we found that there was fairly high agreement on what should be covered in a first course, with Krippendorff's Alpha-Reliability score being k α = 0.57. Excluding the Auckland course noticeably raises the agreement with a k α = 0.68. Some disagreement is to be expected since courses may adopt different approaches. For example, the course may be project driven, might be heavily statistical, or be heavily programming oriented. However, it seems that, regardless of the approach, many of the same fundamental topics do get covered (or not covered). To illustrate this in more detail, in Table 5, we observe a count of positive or negative agreement, and indeterminate topics based on our definitions in the methods section. We also use positive or negative agreement to indicate consensus from the community.  It is clear that there is a central set of topics that are being taught in introductory Data Science courses. We find 15 topics (21%) regularly included and 41 topics (58%) regularly excluded. In total, that gives 56 topics (79%) which the community has strong agreement on and only 15 topics (21%) on which there is not a strong consensus. The details of the excluded list is provided in Appendix C, with the included topics listed below.
• Machine Learning (supervised) • Machine Learning (unsupervised) • Qualitative analytics • Data preparation and pre-processing • Performance and accuracy metrics • Modeling and simulation, theory and systems • Data Architecture, data types and formats, data modeling and design, including related technologies • Data lifecycle and organizational workflow, data provenance and linked data • Research methods, research cycle, hypothesis definition and testing • Data lifecycle and data collection, data quality evaluation • Use Machine Learning technology, algorithms, tools • Use Data Mining techniques • Apply analytics and statistics methods for data preparation and pre-processing • Be able to use performance and accuracy metrics for data analytics assessment and validation • Use effective visualization and storytelling methods to create dashboards and data analytics reports.
Within the 15 topics without a strong consensus, several stand out as instructional choices, including text data mining, cloud or big data systems, conducting a full-fledged experiment process (design, collect data, and analyze), and database technologies. Given these results, it would be interesting to survey Data Science instructors and industry practitioners to see which of these topics students should be exposed to in their first, and often only Data Science course. We have one such survey in early stages.
5. Discussion and future work. This work provides a preliminary evaluation of the Data Science Framework produced by the EDISON project [19]. The comparison with various curricular guidelines at both the discipline level and that of a first course leads us to conclude that the EDSF largely capture the current scope of the discipline of Data Science.
The most significant discrepancies are in the domain areas, specifically business applications. Originally Data Science, especially as a label, grew out of business analytics' need for a particular combination of skills. Now though, the world is seeing the value of those skills in far more than just business. Over the last few years, there has been explosive growth of Data Science into technical domains and other application areas beyond business. Therefore, it seems logical that a revision of what could be domain knowledge would be required.
In addition to the application of Data Science, these findings also impact curriculum development and teaching. For faculty who wish to 'teach' data science, these results point to the kinds of skills and knowledge that can be identified as Data Science, reducing the confusion as the definition of Data Science continues to evolve. For academic programs developing programmatic Data Science endeavors (at the undergraduate or graduate level), the identification of these skills, particularly those identified in the first course, allow faculty to selectively integrate common building blocks of data science into lower level introductory courses, building a planned deliberate pattern of courses into a mature curriculum. Finally, as higher education continues to emphasize assessment and accreditation, coming to a consensus on what a Data Science curriculum should consist of will facilitate the ability to evaluate programs.
Furthermore, from the corpora analysis review we identified a 'core' of topics that are typically included in an introductory Data Science course. As an example, an introductory course should include not only technical knowledge, such as machine learning, data types, and data mining among others, but also critical thought processes related to Data Science such as the data science workflow, research methods, and developing hypotheses. There remains significant variability in other topics though, notably topics such as 'Natural Language Processing', and 'Big Data.' One delimitation of this study was that our scope focused on comparisons among published literature that was generated through or by professional societies, scholars within higher education, and national/international organizations and consortia. What is not well represented within these groups, however, is the influence of industry corporations and not-for-profit learning platforms that also have significant influence on the development and identity of Data Science as a discipline. Future work that is currently in progress will address this gap with a more direct validation initiative by surveying both industry practitioners and academics involved in working as or teaching Data Science. Specifically, we present participants with a random subset of 10 of the CF-DS topics analyzed in the current study. Analogous to the corpora analysis, participants are asked to respond 'Agree' or 'Disagree' to each presented topic as belonging 'within Data Science' and as 'appropriate for coverage during first course in Data Science.' Our preliminary results suggest a high correspondence with the corpora analysis results.
Combined, these efforts toward comparative evaluation of the EDSF will support important next steps in the development of Data Science as a discipline. Having a common consensus of the skills and knowledge in Data Science allows the intentional design and execution of educational research to assess how well courses are teaching Data Science topics. It allows the easier identification of courses being taught outside the umbrella of Data Science that provide broader coverage of necessary knowledge. Similarly, this commonality allows programs to articulate learning outcomes in a consistent language making it simpler for those hiring data scientists to understand their capabilities. For the areas in which there is disagreement, the community can focus on understanding for whom, or why, those topics might be important. Should they be specializations available within some degrees, should they be moved to advanced courses? Do these variations help to distinguish among important sub-disciplinary domains?
While it is exciting to see some consensus forming in the community, it is also clear that a significant amount of work is still needed to help Data Science coalesce into a cohesive and comprehensible discipline. x x 13% x 13% KDSDM02 x 13% KDSDM03 0% KDSDM04 x