Towards a Standardized Data Science Competence Framework: A

Literature Review Approach

Maria Potanin, Maike Holtkemper, Tobias Golz and Christian Beecks

Department of Data Science, University of Hagen, Universit

atsstrasse 11, 58097 Hagen, Germany

Keywords:

Data Science, Competence, Framework, Curricula.

Abstract:

Described as the ”sexiest job of the 21st century”, the data scientist profession has attracted a lot of attention

and demand over the past decade. The rapid growth of this profession, coupled with high barriers to entry and a

lack of standardization, has led to challenges in deﬁning required competencies. This study examines various

frameworks and curricula that aim to teach essential data science competencies, with a focus on the needs

of the industry. A systematic literature review resulted in 32 relevant articles of which 12 documents were

analysed using a qualitative content analysis to synthesise the existing knowledge and integrate it into a uniﬁed

competence framework based on the EDISON Data Science Framework. The results provide a comprehensive

overview of the current relevant literature and propose a grouping of competencies, their knowledge and

skills based on current research ﬁndings. These ﬁndings will be presented transparently to different users

from teaching, training and resource planning practice through the visualisation of different levels in a web

application. This work serves as a foundation for future research efforts aimed at improving the effectiveness

and relevance of data science curricula and frameworks.

1 INTRODUCTION

According to an article in the Harvard Business Re-

view, the profession of data scientist received great

attention when it was named the ”Sexiest Job of the

21st Century” (Davenport and Patril, 2012, p.1). At

this point, the role of a data scientist was still quite

new and in high demand from both technology com-

panies and start-ups (Davenport and Patril, 2022). Ten

years later, the follow-up article ”Is data scientist Still

the Sexiest Job of the 21st Century?” stated that the

demand for qualiﬁed workers in this ﬁeld was higher

than ever (Davenport and Patril, 2022, p.1). The

skills required to use the latest digital technologies to

extract valuable knowledge from heterogeneous data

sources are a major part of the fascination of this pro-

fession (Vaast and Pinsonneault, 2021). In particu-

lar, the development of technology, combined with

the expansion of the ﬁeld over the past decade, has

led to a proliferation of data scientists who specialise

in speciﬁc areas of expertise, rather than working as

generalists (Davenport and Patril, 2022).

Alongside the growing demand for new technolo-

gies, the shortage of data-oriented professionals re-

lated to Data Science is also continuing to grow. In

addition to demographic change (Gren

ıkov

a et al.,

2022), high barriers to entry and a lack of standard-

ization in the ﬁeld (Fayyad and Hamutcu, 2020) are

possible causes. Deﬁnitions, required skills and dif-

ferentiation from other related professions are not

clear (National Academies of Sciences, Engineer-

ing, and Medicine, 2018; Pompa and Burke, 2017).

Companies or other institutions must carefully ex-

amine which roles with the appropriate knowledge

and skill proﬁles are required for their large data sci-

ence projects and how the Data Scientist’s qualiﬁca-

tions ﬁt into one of these roles (Davenport and Pa-

tril, 2022; Holtkemper et al., 2024). In addition,

intelligent solutions to support or even completely

fulﬁll data science tasks have been developed (Nec-

ula, 2023). This raises the question of whether tech-

nologies such as automation frameworks (Auth et al.,

2019; Potanin et al., 2023; Potanin et al., 2024)

will persist or could potentially make the data sci-

entist profession obsolete (Vaast and Pinsonneault,

2021). According to Manyika et al. (Manyika et al.,

2017), time-consuming tasks such as data processing

or data collecting, have the greatest potential for au-

tomation. Therefore, Potanin et al. (Potanin et al.,

2023) analyzed the impact of automation frameworks

on today’s data science competencies by investigating

whether modern automation frameworks have data

science competencies in their design.

Educational institutions are facing signiﬁcant

challenges in keeping up with the rapid changes and

the complex demands of modern work environments,

Potanin, M., Holtkemper, M., Golz, T. and Beecks, C.

Towards a Standardized Data Science Competence Framework: A Literature Review Approach.

DOI: 10.5220/0013364200003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 2, pages 569-581

ISBN: 978-989-758-746-7; ISSN: 2184-5026

569

as evidenced by the ﬁndings of Pompa (Pompa and

Burke, 2017). A study by Wu (Wu, 2017) revealed

considerable variation in job titles and required quali-

ﬁcations for data scientists across different employ-

ers. The interdisciplinary nature of data science,

which combines elements of computer science, statis-

tics, mathematics, and domain-speciﬁc knowledge,

has resulted in different ﬁelds developing their own

deﬁnitions of data science (National Academies of

Sciences, Engineering, and Medicine, 2018). The

lack of standardization in both education and prac-

tice has resulted in various frameworks and curricula,

some of which differ signiﬁcantly in content (Schmitt

et al., 2023). The lack of clarity on this matter makes

it difﬁcult to devise a curriculum that is ﬁt for purpose

(Schmitt et al., 2023).

The aim of this study is to investigate different

frameworks or curricula that focus on essential com-

petencies in data science and to integrate them into a

uniﬁed competence framework. Establishing a con-

sensus on the essential skills and knowledge enables

intentional design of educational research in data sci-

ence. This clarity will beneﬁt the industry by provid-

ing a clearer understanding of the competence proﬁle

required for a data scientist and other roles in a data

science project. We therefore pose the following re-

search question: How can competencies from existing

curricula and frameworks in data science be merged

into an integrated competence framework?

Using Webster and Watson’s systematic litera-

ture review (Webster and Watson, 2002), 32 relevant

documents out of 209 were identiﬁed and evaluated

for their suitability for this study based on various

quality characteristics. Twelve selected frameworks

were analysed using qualitative content analysis ac-

cording to Kuckartz (Kuckartz and R

adiker, 2022).

As a result of the literature synthesis, we summa-

rized the collected ﬁndings in an integrated compe-

tence framework based on the EDISON Data Science

Competence Framework (CF-DS) (Demchenko and

Jos

e, 2021). In contrast to a previous study (Schmitt

et al., 2023), which identiﬁed similarities and incon-

sistencies of the EDISON CF-DS compared to ﬁve

other frameworks or curricula, we focus on adding the

inconsistencies and unnamed elements (knowledge

and skills) to get a comprehensive view of possible

facets of the domain. We extend the EDISON frame-

work with new competence groups, map the ﬁndings

from the EDISON CF-DS to the new structure and

add competencies, knowledge, and skills from other

literature-based data sources.

This article is organized as follows. Section 2 pro-

vides the theoretical background. Section 3 presents

an overview of the research methods used to answer

the research questions and the literature review pro-

cess. Section 4 presents the results of the literature

review and synthesis, followed by a discussion in Sec-

tion 5. The paper ends with the conclusion and future

work in Section 6.

2 BACKGROUND

2.1 Competence Framework

Terminology

Competence frameworks have become increasingly

common over the last few decades and a grown num-

ber of domains use them (Mills et al., 2020). They

can be understood as frames with an organized collec-

tion of related competency statements, for which the

literature of different domains seems to agree (Mills

et al., 2020). However, it quickly becomes apparent

that published competence frameworks differ in their

use of the term ”competency” or ”competence”. Mills

et al. (Mills et al., 2020, p.4) attribute this to two dom-

inant conceptualisations: the educational emergence

of ”behavioural competency frameworks” and, about

a decade later, the employer-driven ”functional com-

petence frameworks”. In the behavioural approach,

”competency” is deﬁned by the concept of cumulative

attributes and refers to performance in general and

not to a speciﬁc profession or task (Chen and Chang,

2010). In contrast, functional competence frame-

works have been developed to describe performance

expectations for speciﬁc professions and deﬁne stan-

dards, whereby a standard was either achieved or

not (Boritz and C., 2003; Bruno et al., 2010; Rowe,

1995). Over time, various frameworks have been

published that merge behavioral and functional ap-

proaches, leading to coherence in the concepts and

motivation of the framework (Boritz and C., 2003;

Mills et al., 2020; Shippmann et al., 2000; Lambert

et al., 2014; Russ-Eft, 1995). ”Competency” is often

used to refer to how performance occurs (behavioural

approach), and ”competence” to refer to how perfor-

mance occurs (functional approach), but this has not

been consistent (Thistlethwaite et al., 2014; Bruno

et al., 2010; Davis et al., 2008; McLagan, 1997; Stu-

art et al., 1995). Mills et al. (Mills et al., 2020)

proposes a glossary for competency frameworks in

health, which currently does not exist for business-

related frameworks. It can be argued that data science

projects require the current state of competencies to

conduct risk assessments, which Mills et al. (Mills

et al., 2020) argues is equivalent to the functional ap-

proach. However, project or HR managers, and other

stakeholders in the ﬁeld are also interested in the long-

CSEDU 2025 - 17th International Conference on Computer Supported Education

570

term development of skills. For this reason, the ter-

minology ”competence” (in the plural form ”compe-

tencies”) of the EDISON Competence Framework is

used in this paper, as it represents the basis for modi-

ﬁcation.

2.2 EDISON Data Science Framework

The EDISON Data Science Framework (EDSF) en-

compasses all the outcomes of the EDISON project,

which ran from 2015 to 2017 (EDISON Initiative,

2023). It was dedicated to advancing the establish-

ment of the novel profession of ’data scientist’ and

aimed to deﬁne the required competencies, formu-

late a framework/proﬁle of skills, develop a body

of knowledge and design a model curriculum (Eu-

ropean Commission, 2017). Within this framework,

the CF-DS identiﬁes the relevant competencies of the

data science domain and links them to knowledge and

skills. In addition, EDISON aimed to create a sustain-

able business model to ensure the continuous growth

of data scientists coming from universities and trained

by various professional education and training insti-

tutions across Europe (European Commission, 2017).

The deﬁnition of competence is outlined in the Euro-

pean e-Competence Framework (European Commit-

tee for Standardization, 2014), which is also included

in the CF-DS (Demchenko and Jos

e, 2021). A com-

petence ”is a demonstrated ability to apply knowl-

edge, skills and attitudes for achieving observable

results” (European Committee for Standardization,

2014, p.5). Within the CF-DS, this concept is oper-

ationalized by associating several types of knowledge

and skills with a given competence, which is used as

a broad term (Demchenko et al., 2022b). Knowledge

is ”information often acquired through formal educa-

tion, books, or other media” (Fayyad and Hamutcu,

2020, p.10). In short, knowledge is the theoretical

component required for the exercise of competence.

Skills can be described as ”the ability to apply this

knowledge, often gained through practice.” (Fayyad

and Hamutcu, 2020, p.10). The EDISON concept

consists of four dimensions:

• Competence Areas: This dimension organizes

competencies into ﬁve groups: Data Analyt-

ics (DSDA), Data Engineering (DSENG), Data

Management and Governance (DSDM), Research

Methods and Project Management (DSRMP), and

Domain-speciﬁc Knowledge (DSDK), applied to

Business Analytics.

• Generic Description: This dimension further de-

velops the competence groups based on the spe-

ciﬁc domain where data science is applied, high-

lighting industry-speciﬁc requirements and chal-

lenges.

• Proﬁciency Level: This focuses on the roles and

responsibilities within data science teams, deﬁn-

ing tasks and skills required for each role.

• Knowledge and Skills: Competencies are di-

vided into methodological, technological, and soft

skills, each supporting different aspects of data

science work.

2.3 Related Work

In recent years, data science has witnessed the devel-

opment of numerous competence frameworks aimed

at guiding the design of educational programs (Bile

Hassan et al., 2021; Danyluk and Leidig, 2021;

Pompa and Burke, 2017; Ramamurthy, 2016; Rosen-

thal and Chung, 2020; Salloum et al., 2021; Veaux

et al., 2017). The research landscape within the

ﬁeld is constantly evolving. Since the introduc-

tion of the EDSF as a central publication of the

European Union, numerous additional frameworks

have emerged, such as publications by the National

Academies of Sciences, Engineering, and Medicine

(National Academies of Sciences, Engineering, and

Medicine, 2018), the Harvard Data Science Review

(Fayyad and Hamutcu, 2020), and the United Nations

(UN Global Working Group, ). Of particular impor-

tance is the Association for Computing Machinery

(ACM) Data Science Curriculum (Danyluk and Lei-

dig, 2021), which delves deeply into the ﬁeld of data

science.

In their research paper ”Evaluation of EDISON’s

data science competence framework through a com-

parative literature analysis” Schmitt et al. (Schmitt

et al., 2023) compared the CF-DS with ﬁve other

competence framework approaches. The study car-

ries out an exemplary comparison at the curriculum

level and also evaluates several introductory data sci-

ence courses. The results of this study show the dif-

ferences between the existing curricula and the dif-

ferent priorities in the education of data scientists.

While the authors provide valuable insights into the

differences in content between the various compe-

tence frameworks and teaching programs, they do not

present a modiﬁed framework. Urs and Minhaj (Urs

and Minhaj, 2023) carried out an analysis of the vari-

ous data science programs offered by educational in-

stitutions, in which they catalogued the topics that

were covered. They were categorized into clusters

based on their titles, and their frequency across in-

dividual courses was assessed. The results indicated

that certain disciplines, notably machine learning and

databases, were included in almost all instructional

Towards a Standardized Data Science Competence Framework: A Literature Review Approach

571

programs, while others, such as software development

or domain-speciﬁc skills, were taught comparatively

infrequently. The recorded course topics were then

mapped to the knowledge areas outlined in the ACM

Computer Science Curricula of 2013 (The Joint Task

Force on Computing Curricula, 2013). This analy-

sis revealed that certain knowledge areas were per-

vasive across most teaching programs, while others

were completely absent. As the studies above show,

institutions vary greatly in how they design data sci-

ence programs. While they are working to deﬁne

the competencies students should develop, their ap-

proaches and priorities differ, resulting in a lack of a

uniﬁed competence framework for the industry, such

as for job advertisements (Suryan and Gupta, 2021).

3 METHODOLOGY

3.1 Literature Review

In accordance with the recommendations set by Web-

ster and Watson (Webster and Watson, 2002), a com-

prehensive literature review is conducted, which in-

volves keyword and backward searches. The method-

ology outlined by vom Brocke et al. (vom Brocke

et al., 2009) was employed to document the process

in a systematic manner. An overview of the litera-

ture review process is presented in Figure 1. The in-

vestigation is centred on the various frameworks and

curricula that are currently in use within the ﬁeld of

data science. A comprehensive search was initially

conducted across a range of scientiﬁc databases, in-

cluding the ACM Digital Library, IEEE Xplore, and

Science Direct. Predeﬁned search queries were used,

including ”Data Science Curricula”, ”Data Science

Competency” and ”Data Science Framework” (see

Table 1).

Figure 1: Literature Review Process.

During the literature review process, peripheral

topics such as statistics were not investigated further,

given that the major focus of the study is on the ﬁeld

of data science. Initially, a total of 209 documents

were identiﬁed, of which 160 were classiﬁed as re-

search articles. Following the removal of duplicate

entries, 71 unique documents were identiﬁed as being

potentially relevant for subsequent analysis. The eval-

Table 1: Findings of Keyword Terms.

Data-

base

Keyword Total

Re-

sults

Artic-

les

Rele-

vant

ACM

Data Science

Curriculum

61 38 20

Data Science

Framework

15 15 13

Data Science

Competency

2 2 2

Total ACM 78 55 35

IEEE

Xplore

Data Science

Curriculum

10 9 6

Data Science

Framework

25 25 16

Data Science

Competency

3 3 2

Total IEEE Xplore 38 37 24

Science

Direct

Data Science

Curriculum

13 10 5

Data Science

Framework

69 48 4

Data Science

Competency

11 10 3

Total Science Direct 93 68 12

Total 209 160 71

uation of abstracts led to the selection of 62 papers

for comprehensive examination. Following an eval-

uation of the text, the ﬁnal subset of 26 articles was

reviewed, augmented by results obtained from a back-

ward search. This resulted in a ﬁnal pool of 32 arti-

cles. Of these, 10 publications related to the EDISON

(E) project, 4 presented industry-relevant (I) compe-

tencies, 14 focused on university programs (U), and 4

fell into other (O) categorizations (see Figure 1; Table

2).

3.2 Quality Appraisal

Following the literature review process, a literature

analysis was conducted. As part of this process, the

results from the literature review were analyzed in

more detail. To ensure the integrity of the selected

literature, a comprehensive critical appraisal method-

ology was applied, following the framework outlined

by Kitchenham (Kitchenham, 2004). Each article was

subjected to a rigorous quality assessment utilizing

predeﬁned checklists, in order to facilitate its cate-

gorization into one of three tiers of relevance: low,

CSEDU 2025 - 17th International Conference on Computer Supported Education

572

Table 2: Findings from Keyword (K) and Backward (B) Search.

Title Year Cat. Type

Model Curricula for Data Science EDISON Data Science (Wiktorski et al., 2017) 2017 E K

Customisable Data Science Educational Environment: From competencies 2017 E K

Management and Curriculum Design to Virtual Labs On-Demand (Demchenko et al., 2017)

Data Science Model Curriculum Implementation for Various Types of 2019 E K

Big Data Infrastructure Courses (Wiktorski et al., 2019)

EDISON Data Science Framework (EDSF) Extension to Address Transversal 2019 E K

Skills Required by Emerging Industry 4.0 Transformation (Demchenko et al., 2019b)

Designing Customisable Data Science Curriculum Using Ontology for 2019 E K

Data Science competencies and Body of Knowledge (Demchenko et al., 2019a)

Big Data Platforms and Tools for Data Analytics in the Data Science 2019 E K

Engineering Curriculum (Demchenko, 2019)

EDISON Data Science Competence Framework (CF-DS, Release 4) (Demchenko et al., 2022b) 2022 E K

EDISON Data Science Framework (EDSF): Addressing Demand for Data 2021 E K

Science and Analytics competencies for the Data Driven Digital Economy (Demchenko and Jos

e, 2021)

Data Scientist Professional Revisited: competencies Deﬁnition and 2021 E K

Assessment, Curriculum and Education Path Design (Demchenko et al., 2021)

Data science in the business environment: Insight management for an 2022 E K

Executive MBA (Lu, 2022)

A Practical and Sustainable Model for Learning and Teaching Data Science (Ramamurthy, 2016) 2016 U K

Systematic Study of Big Data Science and Analytics Programs (Wu, 2017) 2017 U B

Curriculum Guidelines for Undergraduate Programs in Data Science (Veaux et al., 2017) 2017 U B

Data Science for Undergraduates Opportunities and Options (National Academies of Sciences, Engineering, and Medicine,

2018)

2018 U B

A Functional Approach to Data Science in CS1 (Dahlby Albright et al., 2018) 2018 U K

A Data Science Major: Building Skills and Conﬁdence (Rosenthal and Chung, 2020) 2020 U K

Creating a Balanced Data Science Program (Adams, 2020) 2020 U K

A CDIO Oriented Curriculum for Division of Data Science and Big Data 2020 U K

Technologies: The Content, Process of Derivation, and Levels of Proﬁciency (Zhou et al., 2020)

Data Science Curriculum Design: A Case Study (Bile Hassan et al., 2021) 2021 U K

Developing an Interdisciplinary Data Science Program (Salloum et al., 2021) 2021 U K

A Data-centric Computing Curriculum for a Data Science Major (Fekete et al., 2021) 2021 U K

Exploring potential roles of academic libraries in undergraduate data science 2021 U K

education curriculum development (Shao et al., 2021)

Computing Competencies for Undergraduate Data Science Curricula (Force, 2021) 2021 U B

Rankings vs Realities Exploring Competency Differences in Graduate 2023 U K

Data Science Programs (Li et al., 2023)

Data Science and Analytics Skills Shortage: Equipping the APEC Workforce 2017 I B

with the Competencies Demanded by Employers (Pompa and Burke, 2017)

Data Science Competency in Organisations: A Systematic Review 2019 I K

and Uniﬁed Model (Hattingh et al., 2019)

Toward Foundations for Data Science and Analytics: A Knowledge Framework 2020 I B

for Professional Standards (Fayyad and Hamutcu, 2020)

Investigating Academia-Industry Gap for Data Science Jobs and Curriculum (Suryan and Gupta, 2021) 2021 I K

Teaching Computational Modeling in the Data Science Era (Giabbanelli and Mago, 2016) 2016 O K

Cloud Computing Curriculum: Developing Exemplar Modules for General 2020 O K

Course Inclusion (Adams, 2020)

Establishing ABET Accreditation Criteria for Data Science (Blair et al., 2021) 2021 O K

Integrated Data Science for Secondary Schools: Design and Assessment 2022 O K

Course Inclusion (Schanzer et al., 2022)

Total:

Towards a Standardized Data Science Competence Framework: A Literature Review Approach

573

medium, or high (Nidhra et al., 2013):

1 Does the topical domain of the research paper

align with the present research objectives?

2 Has a complete framework or curriculum been de-

veloped?

3 Does it constitute a study or further education pro-

gram for data scientists?

4 Are the results relevant for the present study?

Prior to analysis, responses were categorized ac-

cording to their alignment with the established qual-

ity criteria. Responses that met the quality criterion

were assigned a weight of 1, those that partially met

the quality criterion were assigned a weight of 0.5,

and those that failed to meet the quality criterion were

assigned a weight of 0. Studies were then classi-

ﬁed as high-quality if their overall score exceeded

4, low-quality if their overall score fell below 1, and

medium-quality if their overall score ranged between

1 and 3. In accordance with the established quality

criteria for this study, 12 research paper were classi-

ﬁed as high-quality studies and 21 as medium-quality.

3.3 Qualitative Content Analysis

The 12 research papers (see Table 4) were system-

atically analyzed using a qualitative and structured

content analysis according to Kuckartz (Kuckartz and

adiker, 2022). The objective of the analysis was to

identify similarities and differences between the vari-

ous literature sources. As the information was named

and presented differently in the publications (e.g. ta-

bles or text), the ﬁrst deductive coding process was

undertaken to divide relevant text passages into com-

petence groups (if available), competencies and de-

scriptions based on the EDISON CF-DS scheme. De-

scriptions include statements on particular skills or

knowledge as well as additional information on the

respective competencies. The EDISON CF-DS was

selected as the foundation for subsequent mapping of

results within a modiﬁed framework. It is the most

frequently cited framework in our literature search,

and further developments have been released contin-

uously. The table structure provides an overview of

competence groups and competencies, and thus cate-

gories and subcategories were partly adopted in a de-

ductive manner for the second coding process. The

categories of application development, business an-

alytics, theoretical foundations and soft skills were

added inductively, with further sub-categories and

sub-sub-categories. In the ﬁnal stage of the process,

all codes were checked according to the ﬁnal coding

scheme and reassigned if necessary. Table 3 illus-

trates an example of the DSAD - Application Develop-

ment category from the coding scheme. In accordance

with the recommendations of Kuckartz (Kuckartz and

adiker, 2022), all documents were coded by two of

the authors to enhance the rigor of the results. The

overall agreement between the coded segments was

high, although not identical. The elements that had

been categorised differently were discussed with the

addition of the third author and assigned to the cate-

gory in which two of the three authors were in agree-

ment.

Table 3: Final coding scheme for category “Application De-

velopment”.

Competence

Group (Cat-

egory)

Competence

(Sub-category)

Description (Sub-

sub-category)

DSAD -

Application

Development

DSAD01 - Pro-

gramming

Programming

skills, Develop-

ment issues

DSAD02 - Li-

braries and tools

Programming

libraries & tools,

Development

environment

DSAD03 - Soft-

ware Engineer-

ing

Software Engi-

neering principles

DSAD04 -

Development

pipelines

Version control,

Development

pipelines (automa-

tion)

4 FINDINGS

4.1 Descriptive Results

As a ﬁrst result, Table 4 represents the identiﬁed com-

petence frameworks derived from the literature review

and analysis. The selected documents were published

between 2017 and 2022, with at least one framework

(or release) being published each year. This high-

lights the necessity for a framework that is both cur-

rent and applicable across a range of contexts. The

research work has a strong academic focus (66%),

even though some of the frameworks include prac-

tical domains. The EDISON CF-DS, for example,

was evaluated by expert groups comprising represen-

tatives from academic and industrial sectors. Other

publications (44%) such as Hattingh et al. (Hattingh

et al., 2019) are clearly focused on practical appli-

cations in order to develop an industry-speciﬁc skill

set. The comparison of data sources is therefore in-

clusive of both academic and practice-oriented doc-

uments. With the exception of one research paper

CSEDU 2025 - 17th International Conference on Computer Supported Education

574

Table 4: Identiﬁed Frameworks for Qualitative Content Analysis.

ID Title Research

Method

Data

1 EDISON Data Science Competence

Framework (CF-DS) (Demchenko

et al., 2022a)

Content analysis Six existing frameworks;evaluated by

expert groups (academia & industry)

2 Curriculum Guidelines for Undergrad-

uate Programs in data science (Veaux

et al., 2017)

Literature analy-

sis

NSF Workshop on Data Science Ed-

ucation, guidelines for undergraduate

majors in Mathematics, Statistics and

Computer Science

3 Data Science and Analytics Skills

Shortage (Pompa and Burke, 2017)

Literature re-

view

Academic literature, research and

technical papers, government reports,

working papers, industry publications

and surveys

4 Systematic Study of Big Data Science

and Analytics Programs (Wu, 2017)

Literature re-

view

DSA programs in the U.S.

5 National Academies of Sciences, En-

gineering, and Medicine (National

Academies of Sciences, Engineering,

and Medicine, 2018)

Content analysis Information-gathering activities and

community conversations

6 Data Science Competency in Organisa-

tions: A Systematic Review and Uni-

ﬁed Model (Hattingh et al., 2019)

literature review Literature on essential data science

workforce competencies

7 Toward Foundations for Data Sci-

ence and Analytics: A Knowledge

Framework for Professional Standards

(Fayyad and Hamutcu, 2020)

Literature re-

view

Literature on analytics and data science

skills

8 Computing Competencies for Un-

dergraduate Data Science Curricula

(ACM) (Danyluk and Leidig, 2021)

2 Surveys for

Academia and

Industry

Results of the surveys

9 Investigating Academia-Industry Gap

for Data Science Jobs and Curriculum

(Suryan and Gupta, 2021)

Literature re-

view

28 curricula from Indian and Internat.

institutions

10 A Data Science Major: Building Skills

and Conﬁdence (Rosenthal and Chung,

2020)

Following Jolly

et al.’s trilogy

Based on data science guidelines such

as EDISON, ACM, BHEF, NIST

11 Data Science Curriculum Design: A

Case Study Bile (Bile Hassan et al.,

2021)

Literature re-

view

122 Data Science degrees in the U.S.

12 A Data-centric Computing Curriculum

for a Data Science Major (Fekete et al.,

2021)

Standardized

structure of ma-

jors (University

of Sydney)

ACM guideline

Towards a Standardized Data Science Competence Framework: A Literature Review Approach

575

(Force, 2021), the authors primarily base their ﬁnd-

ings on qualitative data and use research methods

such as literature reviews.

4.2 Framework Analysis and

Modiﬁcation

These 12 documents were coded as described in the

methodology section. As a result, a total of 8 compe-

tence groups were identiﬁed, comprising 46 distinct

competencies. These encompass 269 elements of

knowledge, methodological and technological skills,

and soft skills. Four of the ﬁve EDISON groups were

kept, whereby three originally came from the NIST

(NIST Big Data Public Working Group, Deﬁnitions

and Taxonomies Subgroup, 2019) deﬁnition of a data

scientist:

• Data Analytics (DSDA). Using appropriate ana-

lytical methods and statistical techniques on avail-

able data to discover correlations and support

decision-making.

• Data Engineering (DSENG). Applying engi-

neering principles and modern computing tech-

nologies to design and implement new data an-

alytics applications, and developing tools, sys-

tems, and infrastructure to support data process-

ing throughout the data life cycle.

• Data Management and Governance (DSDM).

Development and implementation of a data man-

agement strategy for data collection, storage,

preservation, and availability for further process-

ing steps.

• Research Methods and Project Management

(DSRMP). Creating new knowledge by applying

scientiﬁc or similar engineering methods to gen-

erate knowledge and achieve research or organi-

zational goals.

The ﬁfth group of the EDISON CF-DS, Domain-

speciﬁc knowledge and expertise was modiﬁed, as the

addition of industry-related articles was designed to

facilitate a heightened emphasis on practice. As a re-

sult, it was renamed Business Analytics (DSBA).

During the comparison, we determined that more

groups should be added to categorize all identiﬁed

competencies in a logical manner. For example,

the EDISON CF-DS excludes personal competencies

from its overview, in constrast to other documents

(Bile Hassan et al., 2021; Fekete et al., 2021; Hattingh

et al., 2019; Rosenthal and Chung, 2020). For com-

petencies related to software and application develop-

ment (Hattingh et al., 2019), theoretical fundamentals

such as algorithms and data structures, mathematics,

computational fundamentals and complexity theory

(Bile Hassan et al., 2021; Danyluk and Leidig, 2021;

Fekete et al., 2021; Hattingh et al., 2019; Rosenthal

and Chung, 2020), as well as personal competencies

like problem solving, communication, and leadership

(Bile Hassan et al., 2021; Danyluk and Leidig, 2021;

Hattingh et al., 2019; Pompa and Burke, 2017; Veaux

et al., 2017), new competence groups are added. They

are deﬁned as follows:

• Application Development (DSAD). Planning

and development of software products using suit-

able programming languages and technologies, as

well as implementation of development pipelines.

• Theoretical Fundamentals (DSTF). Proﬁciency

in the relevant theoretical foundations for the ap-

plication of data science principles.

• Personal competencies. Development of per-

sonal and social skills and attributes that are cru-

cial for success in today’s job market and espe-

cially as a data scientist.

In conclusion, the ﬁnal categories of competence

groups are as follows: Data Analytics (DSDA), Data

Engineering (DSENG), Data Management and Gov-

ernance (DSDM), Research Methods and Project

Management (DSRMP), Business Analytics (DSBA),

Application Development (DSAD), Theoretical Fun-

damentals (DSTF), and Personal competencies.

In the subsequent phase, the EDISON frame-

work was modiﬁed in accordance with the aforemen-

tioned eight groups. For this purpose, new compe-

tencies were created (e.g. Business and Organiza-

tion or Information Needs for the Business Analyt-

ics group) or existing ones were reassigned. For en-

hanced presentation and comprehension, the compe-

tencies were further subdivided when necessary. The

entire framework with the literature comparison is ac-

cessible here. The structure of the modiﬁed frame-

work is explained using the DSAD competence group

sample in Figure 2.

The Application Development competence group

is comprised of the following competencies: Pro-

gramming, Libraries and Tools, Software Engineer-

ing and Development pipelines. In line with the rec-

ommendations of the e-Competence Framework (Eu-

ropean Committee for Standardization, 2014), all ele-

ments are assigned an ID, in this case acronyms. To il-

lustrate, the competence DSAD01 - Programming has

been divided into Programming and Development is-

sues (Table 4). The individual elements of the litera-

ture synthesis correspond to the deﬁnition of a com-

petence, which includes knowledge and skills. In the

case of skills, a distinction is made between method-

ological, technological and personal skills. Acronyms

CSEDU 2025 - 17th International Conference on Computer Supported Education

576

Figure 2: Framework modiﬁcation and comparison for DSAD - Application Development.

are also assigned to these elements (excluding per-

sonal skills). The complete element catalogs for the

acronyms can be accessed here.

The competence group DSAD with its four as-

signed competencies shows that the competence with

the subgroup Programming in particular was named

with a 100% agreement in every framework examined

and thus contains a generally relevant competence

for science and practice. The Development pipelines

are less well represented. Data Science Analytics

(DSDA) with ﬁve competencies and very detailed sub-

groups also shows four core aspects that were found

in all documents: Machine Learning Fundamentals,

Visualize Results, Basic Data Analytics and Statis-

tics. More speciﬁc subgroups such as Outlier Iden-

tiﬁcation (Danyluk and Leidig, 2021) or Descriptive

Data Analysis (Demchenko et al., 2022b) were only

mentioned once. Seven competencies were identiﬁed

from the Data Science Engineering category, which

can also be described in detail with up to six sub-

groups. High overlap in the literature can be found in

the areas of Data Collection (75%) and Data Prepa-

ration (92%) of the DSENG02 competence. Rela-

tional and Non-relational Databases (75%) can also

be found in most sources. In general, the named com-

petence groups were very well covered in the litera-

ture with overlaps of up to 100%.

The competence groups Data Management, Re-

search Methods, Project Management and Business

Analytics were less frequently mentioned. Data Man-

agement comprises six competencies with up to six

subgroups. Data Management Policy was mentioned

in nine documents, resulting in an overlap of 75%.

The remaining subgroups range from 8% to 67%. Par-

ticularly detailed areas such as Identify Data Sources

(Danyluk and Leidig, 2021) or Data Interoperability

(Demchenko et al., 2022b) are mentioned only once.

Data Quality is addressed in 7 documents (58%). The

category Research Methods and Project Management

also achieves the highest level of agreement in the

use of Research Methods (58%), the Data Life Cy-

cle (58%) and Project Management (67%). The mod-

iﬁed Business Analytics competence group contains

the most competencies. A total of 8 were identiﬁed,

with overlap ranging from 8% to 42% within the liter-

ature. There is little overlapping in the competencies

DSBA02 (Fuzzy Concepts) and DSBA04 (Process Op-

timization), which, in addition to Demchenko et al.

(Demchenko et al., 2022b), are only covered in the

form of process optimization in Hassan et al. (Bile

Hassan et al., 2021). Business Analytics and Business

Intelligence, Domain Knowledge and (agile) Decision

Making are addressed more frequently (42%).

Theoretical Fundamentals (DSTF) consist largely

of knowledge items and focus on four compe-

tencies: Algorithms & Data structures (DSTF01),

Mathematics (DSTF02), Computational Fundamen-

tals (DSTF03) and Complexity Theory (DSTF04). In

this case, there are overlaps especially in DSTF01

(92%) and DSTF02 (83%). The competence group

Personal competencies was not subdivided according

to the scheme in Table 3. The competencies Personal

Improvement, Communication, Collaboration, Lead-

ership, Problem Solving, and Ethical Thinking were

included with personal skills. Most of the competen-

cies were identiﬁed in at least 8 documents.

4.3 Web Application

The modiﬁed competence framework is designed to

be made accessible to different target groups to be

used widely. In the ﬁrst step, the framework is pub-

lished via a web application for evaluation purposes

and can be accessed via the link. The choice of an

easily accessible, easy-to-understand and yet detailed

form of presentation makes it possible to address dif-

ferent stakeholders from education and industry. It is

possible to switch between three different views.

1. Competence List: Presents the competencies in

tabular form, providing a quick overview. In each

table, a competence group is presented, which in-

cludes all associated competencies. A ﬁlter allows

the selection of all or speciﬁc competence groups.

2. Competence Grid: Competence groups are dis-

played colored tiles. By selecting a competence

Towards a Standardized Data Science Competence Framework: A Literature Review Approach

577

group, the tiles of the next level (competencies)

are displayed. On the next layer, the details of the

selected competence are presented, which include

information, assigned knowledge and skills.

5 DISCUSSION AND

LIMITATIONS

Comparing different competence frameworks reveals

some limitations that arise from several aspects. On

the one hand, the frameworks have different objec-

tives, such as Suryan’s focus on the skills of a data

scientist in industry (Suryan and Gupta, 2021) ver-

sus Rosenthal’s focus on the university education of

a data scientist (Rosenthal and Chung, 2020). In

addition, the length of the individual research ar-

ticles varies considerably, leading to differences in

the scope and depth of detail of the competence de-

scriptions, as in the case of EDISON CF-DS (176

pages) compared to Rosenthal’s Data Science Major

(6 pages). Table 5 shows the comparison of the dif-

ferent data sources.

Table 5: Document comparison on ﬁnding frequencies.

EDSF

Park City

IADSS

ACM

APEC

National A.

Suryan

Hassan

Fekete

Rosenthal

Hattingh

Total 118 48 40 56 87 31 72 32 38 46 40 38

in % 87 36 30 41 64 23 53 24 28 34 30 28

A total of 135 possible competence subgroups

were recorded. The EDISON CF-DS (87%) and the

ACM (64%) achieved the most ﬁndings. The fact that

EDISON was used as the basis for coding the docu-

ments may also explain the high degree of overlap.

Concise frameworks typically address broad topics

like statistics, while expansive frameworks delve into

speciﬁc topics with greater detail, such as decision

trees. In addition, many frameworks use literature

analyses or qualitative content analyses as research

methods. There is a lack of comprehensive practical

research, such as case studies or quantitative surveys,

which examine core elements of practice.

The discourse can be particularly focused on in

the context of new technologies and the ﬂexibility

of skills frameworks. The literature examined does

not show any explicit use of certain key technologies

such as generative AI (GenAI) or large language mod-

els. In various areas with an industry focus, such as

business process management, GenAI can be used to

support automated routine tasks or the discovery of

process innovations (Beverungen et al., 2021). The

question therefore also arises as to how this technol-

ogy can be used in data science projects (Feuerriegel

et al., 2023; Zschech et al., 2020) and, associated with

this, which competencies are required for the various

roles in such a project. For example, the gap between

modeling experts and domain users could be closed

(Zschech et al., 2020). However, this leads us to the

limitation that current frameworks are not adaptable

enough and cannot react quickly to changes and new

ﬁndings. Therefore, more and more new frameworks

are being developed instead of updating old frame-

works. The lack of standardization on a current and

adapted framework makes it difﬁcult for practition-

ers and educators to keep up with the rapid develop-

ment of new technologies. The framework expanded

here serves to present the status quo from the liter-

ature, which is intended to include both education

and practice. It shows similarities and differences

between individual frameworks and which competen-

cies are hardly represented. For example, data qual-

ity plays an important role in the development of ma-

chine learning models, but is hardly considered in the

various data science programs. In order to establish it

as a useful tool and to make it more adaptable to tech-

nological developments and to the needs of teaching

and practice, a discourse between the different target

groups (teachers, students, practitioners) is necessary.

As an initial evaluation approach, two expert inter-

views were conducted with data scientists in practice

and a group discussion was held with three univer-

sity lecturers. In summary, both the expert interviews

and the group discussion highlighted the relevance of

a standardized data science competence framework.

There was general agreement on the content of the

framework and no major gaps in the listed compe-

tencies were identiﬁed. Respondents from both sides

gave positive feedback on the overlap with current ed-

ucational programs and the daily work of a data sci-

entist.

6 CONCLUSION

Described a decade ago as the most attractive pro-

fession of our time, the data science profession has

grown due to increasing demand and technological

advances (Davenport and Patril, 2012). However, this

growth has also brought challenges, particularly in

deﬁning the necessary skills. While the development

of various frameworks and curricula demonstrates ef-

forts to address these issues, it has resulted in a lack

of standardization and clarity.

CSEDU 2025 - 17th International Conference on Computer Supported Education

578

Our study addresses this complexity and aims to

integrate different frameworks and curricula into a

uniﬁed competence framework. Through a systematic

literature review and qualitative content analysis, we

synthesized ﬁndings from different sources and built

on the EDISON CF-DS. In contrast to previous stud-

ies, we not only identiﬁed similarities and inconsis-

tencies, but also introduced new groups of competen-

cies to enrich the understanding of the ﬁeld of data

science. Our integrated competence framework pro-

vides a comprehensive view of the essential compe-

tencies, knowledge and skills required for data scien-

tists. Initial evaluations with experts and trainers have

further validated and reﬁned our ﬁndings. Our frame-

work highlights the importance of standardization in

deﬁning data science competencies, which beneﬁts

both academia and industry. By providing a clearer

understanding of the skill proﬁle required for data

scientists, our framework facilitates the targeting of

educational programs and helps organizations iden-

tify and develop talent. However, further research is

needed to reﬁne and validate our framework as the

ﬁeld of data science continues to evolve rapidly. In

summary, our study contributes to bridging the gap

between theory and practice in data science education

and serves as a foundation for future research efforts

aimed at improving the effectiveness and relevance

of data science curricula and frameworks. It also

aims to highlight automation opportunities to address

skills shortages, education and industry. Based on the

framework presented, future research will investigate

how automation frameworks can help automate spe-

ciﬁc data science tasks. Future research should inves-

tigate which competencies employees, especially do-

main experts, need to develop in order to operate these

automation frameworks. Based on this approach, do-

main experts can be speciﬁcally trained without hav-

ing all the data science knowledge.

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