EDISON Framework:
Data Science Competence Framework (CF-DS)
andData Science Body of Knowledge (DS-BoK)

Yuri Demchenko, EDISON
University of Amsterdam

IG-ETRD Meeting

EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

Date: 1 March 2016
RDA7, Tokyo, Japan

Outline•

EDISON Project approach– From Data Science Competences to Body of Knowledge and Model Curriculum•

Background: Existing frameworks and standards– e-CF3.0, CWA ICT profiles, ESCO•

Data Science Competence Framework: Essential competences and skills– Domain related competences and skills••

Taxonomy: Data Science occupations Family (proposed ESCO extension)
Data Science Body of Knowledge (DS-BoK)••

Taxonomy: Knowledge area, academic disciplines

Further steps - Survey and questionnaires

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EDISON Framework: Building the Data Science
Profession
CF-DS

•

DS-BoK

MC-DS

EOEE & ETMplz

Taxonomies & Vocabulary

Edu&Train Inventory

Foundation & Concepts

Services

Sustainability:• Roadmap• Community
Portal• Certification• Professional
community/
association

Biz Model

EDISON Framework components••••

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
Data Science Taxonomies and Scientific Disciplines Classification–•

Linked to e-CFv3.0, ACM CCS (2012) and ESCOEOEE - EDISON Online Education Environment

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Background Frameworks and Standards• e-CFv3.0 - European e-Competence Framework for IT– Structured by 4 Dimensions and organizational processes• Competence Areas: Plan – Build – Run – Enable - Manage• Competences: total defined 40 competences• Proficiency levels: identified 5 levels linked to professional education levels• Skills and Knowledge• CWA 16458 (2012): European ICT Professional Profiles Family Tree
– Defines 23 ICT profiles for common ICT jobs

• ESCO (European Skills, Competences, Qualifications and Occupations)framework– Standard for European job market since 2016
– Intended inclusion of the Data Science occupations family – end of 2016

• ACM Classification of Computer Science – CCS (2012)– ACM Computer Science Body of Knowledge (CS-BoK) and ACM and IEEE Computer
Science Curricula 2013 (CS2013)
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4EDISON Approach: e-CFv3.0 and CF-DS•

Competence Framework for Data Science (CF-DS) definition will be built based on
European e-Competence framework for IT (e-CFv3.0)– Linking scientific research cycle/flow, organizational roles, competences, skills andknowledge– Defining Data Science Body of Knowledge (DS-BoK)– Mapping CF-DS and DS-BoK to academic disciplines in a DS Model Curriculum (MC-DS)• Multiple use of e-CFv3.0 within ICT
organisations• Provides basis for individual career
path, competence assessment, training
and certification• EDISON CF-DS will be used for
defining DS-BoK and MC-DS, linking
organizational functions and required
knowledge• Provide basis for individual (self)
training and certification

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e-CFv3.0 Internal Structure: Refactoring for CF-DS• s• 4 Dimensions– Competence Areas– Competences– Proficiency levels– Skills and Knowledge• 5 Competence Area defined by ICT
Business Process stages– Plan– Build– Run– Enable– Manage-> Refactor to Scientific Research
cycle/workflow (and linked to Scientific Data
Lifecycle)– See example of RI manager at IG-ETRD wiki
and meeting• Each competence has 5 proficiency level– Ranging from technical to engineering tomanagement to strategist/expert level• Knowledge and skills property are definedfor/by each competence and proficiency
level (not unique)
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Definitions (according to e-CFv3.0)• Competence is a demonstrated ability to apply knowledge, skills andattitudes for achieving observable results.
– Competence vs Competency (e-CF vs ACM)
• Competence is ability acquired by training or education (linked to learning outcome)• Competency is similar to skills or experience (acquired feature of a person)– Competence can be treated as outcome of learning or training• Knowledge in the context of competence definition is treated as
something to know, to be aware of, familiar with, and obtained as a partof education.
• Skills is treated as provable ability to do something and relies on the
person’s experience.

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Demanded Data Science Competences andSkills: Jobs market analysis•

Sources (period Aug – Sept 2015)– IEEE Data Science Jobs (World but majority US) (collected > 120, selected for analysis> 30)– LinkedIn Data Science Jobs (NL) (collected > 140, selected for analysis > 30)– Existing studies and reports + numerous blogs•

Analysis methods– Using manually data analytics methods: classification, clustering, expert evaluation– Research methods: Data collection - Hypothesis – Artefact - Evaluation•

Observations– Many job ads don’t use Data Scientist as a definite profession• Data Science competences/skills are specified as part of traditional ICT professions/positions– Many academic openings are without specified skills profile– Explicit Data Scientist jobs specify wide variety of expected functions/responsibilities
and required skills and knowledge

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8Identified Data Science Competence Groups•

Traditional/known Data Science competences/skills groups include
–
–
–

•

Data Analytics or Business Analytics or Machine Learning
Engineering or Programming
Subject/Scientific Domain Knowledge

EDISON identified 2 additional competence groups demandedby organisations[ref] Legacy: NIST BDWG
definition of Data Science– Data Management, Curation, Preservation– Scientific or Research Methods and/vs Business Processes/Operations•

Other skills commonly recognized aka “soft skills” or “social intelligence”–•

All groups need to be represented in Data Science curriculum and training–•

Inter-personal skills or team work, cooperativeness

Challenging task for Data Science education and training

Another aspect of integrating Data Scientist into organisation structure–––

General Data Science (or Big Data) literacy for all involved roles and management
Common agreed way of communication and information/data presentationRole of Data Scientist: Provide such literacy advice and guiding to organisation

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9Data Science Competence Groups - Research
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Operations
•
•
•
•
•

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Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design10Data Science Competences Groups – Business
Data Science Competence
includes 5 areas/groups

DOMAIN
EXPERTISE

•
•
•
•
•

DesignOptimisation

RESEARCHDATAANALYTICS

DATA

Scientific Methods

SCIENCEANALYTIC ALGORITHMS
SYSTEMS

Monitoring

Modelling
ENGINEERINGCOMPETENCES

Business Process
Management

Data
Management
Execution

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Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

ScientificMethods
Data Science Competences and BoK

•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Process
Operations/Stages
•
•
•
•
•

DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design11Identified Data Science Competence Groups
Data Analytics (DA)

1

2

3

4

Data Management/
Curation (DM)
Use appropriate
Develop andstatistical techniques on implement data
available data to deliver strategy
insights

Use predictive analytics
to analyse big data anddiscover new relations

Research and analyze
complex data sets,
combine different
sources and types ofdata to improve analysis.

DS Engineering (DSE)
Use engineering principles toresearch, design, or develop
structures, instruments,
machines, experiments,
processes, systems, theories,
or technologies

Develops specialized data

Develop data models analysis tools to support
including metadata
executive decision making
Integrate different data
source and provide forfurther analysis
Design, build, operate
relational non-relational
databases

analytics platforms

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Collect and manage
different source of data

DS Domain Knowledge(including Business Apps)

Create new understandings andcapabilities by using the scientific
Understand business andmethod's hypothesis, test, and evaluation provide insight, translatetechniques; critical review; or similar
unstructured businessengineering research and development
problems into an abstract
methods
mathematical framework
Use data to improve existing
Direct systematic study toward a fuller
services or develop new
knowledge or understanding of thefundamental aspects of phenomena and servicesof observable facts, and discovers new
approaches to achieve goals
Undertakes creative work, making
systematic use of investigation or
experimentation, to discover or revise
knowledge of reality, and uses thisknowledge to devise new applications

Develop and apply

Develop specialized Develop and maintain computational solutions toa historical data
domain related problems
analytics to enableagile decision making repository of analysis using wide range of data

5

Ssearch Methods (DSRM) cientific/Re

Apply ingenuity to complex problems,develop innovative ideas
Ability to translate strategies into actionplans and follow through to completion.

Develop solutions for secureand reliable data access
Develop algorithms to
Influences the development ofVisualise complex and analyse multiple source of organizational objectives
variable data.
data
Prototype new data analytics
applications

Data Science Competences and BoK

Participate strategically andtactically in financial decisions
that impact management andorganizations

Recommends business
related strategic objectives
and alternatives andimplements them

Provides scientific, technical,
and analytic support
services to otherorganisational roles
Analyse multiple data sources
for marketing purposes

Analyse customer data toidentify/optimise customer
12
relations actions

Identified Data Science Skills/Experience Groups•

Group 1: Skills/experience related to competences
–
–
–
–
–
–

•

Group 2: Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Big Data Analytics platforms
Math & Stats apps & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
Cloud based platforms and tools

Group 3: Programming and programming languages and IDE–•

Data Analytics and Machine Learning
Data Management/Curation (including both general data management and scientific data
management)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods
Application/subject domain related (research or business)
Mathematics and Statistics

General and specialized development platforms for data analysis and statistics

Group 4: Soft skills or Social Intelligence–

Personal, inter-personal communication, team work (also called social intelligence or soft skills)

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Identified Data Science Skill Groups
Data Analytics andMachine Learning

Data Management/
Curation

Data Science
Engineering (hardware
and software)

Scientific/ Research
Methods

1

Artificial intelligence,machine learning

Design efficientalgorithms for accessing
and analyzing large
amounts of data

Interest in data science2

Machine Learning andStatistical Modelling

Manipulating andanalyzing complex, highvolume, highdimensionality data fromvarying sources
for data improvement

Big Data solutions andadvanced data mining
tools

Machine learning
solutions and patternrecognition
techniques

Data models anddatatypes

Supervised andunsupervised learning

3

4
5

6
7
8

9
10

Personal/Interpersonalcommunication, teamwork

Application/subject
domain (research or
business)

Communication skills

Recommender or
Ranking system

Analytical, independent,
critical, curious and focusedon results

Inter-personal intrateam and external
communication

Data Analytics forcommercial purposes

Multi-core/distributed
software, preferably in a
Linux environment

Confident with large data
sets and ability to identify
appropriate tools andalgorithms

Network of contacts in
Big Data community

Data sources andtechniques for businessinsight and customer
focus

Handling vast amountsof data

Databases, databasesystems, SQL and NoSQL

Data mining

Experience of working
with large data sets

Statistical analysis
languages and tooling

Flexible analytic approach toachieve results at varying
levels of precision
Exceptional analytical skills

Markov Models,Conditional Random
Fields
Logistic Regression,
Support Vector
Machines
Predictive analysis
and statistics(including Kaggle
platform)(non)relational and (un)structured data

Cloud powered
applications design(Artificial) Neural
Networks

Metadata annotationand management

Statistics

Data citation, metadata,
PID (*)

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Mechanism Design
and/or Latent Dirichlet
AllocationGame Theory

Copyright and IPR

Cloud based data
storage and data
management
Data management
planning

Data Science Competences and BoK

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Identified Big Data Tools and Programming
Languages
Big Data Analytics
platforms

Math& Stats tools

Databases

Data/ applications
visualization

Data Management andCuration platform

Big Data Analytics platforms

Advanced analytics tools(R, SPSS, Matlab, etc)

SQL and relational databases

Data visualization Libraries (D3.js,
FusionCharts, Chart.js, other)

Data modelling and related
technologies (ETL, OLAP, OLTP,
etc)

2

Big Data tools (Hadoop,
Spark, etc)

Data Mining tools:
RapidMiner, others

NoSQL Databases

Visualisation software (D3,Processing, Tableau, Gephi, etc)

Data warehouses platform andrelated tools

3

Distributed computing tools aplus (Spark, MapReduce,
Hadoop, Hive, etc.)

MathlabNoSQL, Mongo, Redis

Online visualization tools(Datawrapper, Google Charts,
Flare, etc)

Data curation platform,
metadata management (ETL,
Curator's Workbench, DataUp,
MIXED, etc)

4Real time and streaming
analytics systems (like Flume,
Kafka, Storm)

PythonNoSQL, Teradata

Hadoop Ecosystem/platform

R, Tableau R

Excel

SpotfireSAS

7

Azure Data Analytics
platforms (HDInsight, APS and
PDW, etc)

Scripting language, e.g.
Octave8

Amazon Data Analytics
platform (Kinesis, EMR, etc)

Statistical tools and data
mining techniques

Other cloud based Data
Analytics platforms(HortonWorks, Vertica
LexisNexis HPCC System, etc)

Other Statisticalcomputing and languages(WEKA, KNIME, IBM SPSS,
etc)

1

5
6

9

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Backup and storage
management (iRODS, XArch,
Nesstar, others

•
•
•
•
•

Big Data Analytics platforms
Math& Stats tools
Databases
Data/applications visualization
Data Management and Curation
platform

Data Science Competences and BoK

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Suggested e-CF extensions for DS
A. PLAN and Design•
A.10* Organisational workflow/processes model definition/formalisation•
A.11* Data models and data structures
B. BUILD: Develop and Deploy/Implement•
B.7* Apply data analytics methods (to organizational processes/data)•
B.8* Data analytics application development•
B.9* Data management applications and tools
15 Data Science Competences proposed•
B.10* Data Science infrastructure deployment
C. RUN: Operate•
C.5* User/Usage data/statistics analysis•
C.6* Service delivery/quality data monitoringcovering different organizational roles andworkflow stages• Data Scientist roles are crossing multiple
org roles and workflow stages

D. ENABLE: Use/Utilise•
D10. Information and Knowledge Management (powered by DS)•
D.13* Data presentation/visualisation, actionable data extraction•
D.14* Support business processes/roles with data and insight (support to D.5, D.6, D.7, D.12)•
D.15* Data management/preservation/curation with data and insight
E. MANAGE•
E.10* Support Management and Business Improvement with data and insight (support to E.5, E.6)•
E.11* Data analytics for (business) Risk Analysis/Management (support to E.3)•
E.12* ICT and Information security monitoring and analysis (support to E.8)

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Data Scientist and Subject Domain Specialist• Subject domain components– Model (and data types)– Methods– Processes– Domain specific data and presentation/visualization methods– Organisational roles and relations• Data Scientist is an assistant to Subject Domain Specialists– Translate subject domain Model, Methods, Processes into abstract data
driven form
– Implement computational models in software, build required
infrastructure and tools– Do (computational) analytic work and present it in a form understandable
to subject domain– Discover new relations originated from data analysis and advice subject
domain specialist– Interact and cooperate with different organizational roles to obtain data
and deliver results and/or actionable data
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Data Science and Subject Domains
Data Science domain components
Data structures &databases/storage• Abstract data driven
math&compute models• Data Analytics
methods• Data and Applications
Lifecycle Management

Crossorganisational
assistive role

Data Scientist functions is to translate between two domains

Domain specific components
Domain specificdata & presentation

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• Models (and data types)• Methods• Processes

Data Science Competences and BoK

Organisational
roles

18

Possible Data Scientist profiles/roles
as extension to CWA16458 (2012)• Data Analyst, Business Analyst– Data Mining– Machine Learning• Digital Librarian, Data Archivist, Data Curator, Data Steward– Data Management related competences• Data Science Engineer/Administrator/Programmer– Data analytics applications development– Scientific programming– Data Science/Big Data Infrastructure development/operation• Data Science Researcher– Data Science research methods– Data models and structures• Data Scientist in subject/research domain• Research e-Infrastructure brings its own specifics to requiredcompetences and skills definition
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Proposed Placing of Data Science Occupations
Family in ESCO taxonomy (1)Professionals
Science andengineering
professionals

Data Science
Professionals

Data Science
professionals notelsewhere classified

Data Scientist

Data Science Researcher(Big) Data Analyst
Data Science (Application)
Programmer
Business Analyst
Database and network
professionals

Information andcommunications
technology
professionals

Data Science
technology
professionals

Large scale (cloud) data
storage designers andadministrators

Large scale (cloud) database
designer*)

Database designers andadministrators

Large scale (cloud) databaseadministrator*)

Database and network
professionals notelsewhere classified

Scientific databaseadministrator*)

Data handlingprofessionals notelsewhere classified

Digital Librarian

Data Archivist
Data Steward
Data curator
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Proposed Placing of Data Science Occupations
Family in ESCO taxonomy (2)Technicians and associate professionals
Science andengineering associate
professionals

Data Science
Technology
Professionals

Data Infrastructureengineers andtechnicians

Big Data facilities Operators

Large scale (cloud) data
storage operators
Database and network
professionals notelsewhere classified

Scientific database operator*)

Managers
Production andspecialised services
managers

Data Science/Big Data
Infrastructure Managers

Data Science/Big Data
Infrastructure Manager
Research InfrastructureManagers

RI Manager
RI Data storage facilities
manager

Clerical support workers
General andkeyboard clerks
Data handling
support workers(alternative)

Data and information
entry and access

Digital Archivists andLibrarians

Digital Librarian

Data Archivist
Data Steward
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Data curator

21

EXAMPLE: Use of e-CF3.0 for Defining Profile of
RI Technical (part of RDA IG-ETRD work)
A. PLAN and DESIGN

D. USE: UTILISE (ENABLE)

A.2. Service Level Management
A.3. Product / Service Planning
A.5. Application Design
A.4. Architecture Design

D.1. Scientific Applications Integration (on running RI)
D.5. Data collection and preservation
D.4. New requirements and change Identification
D.6. Education and Training Provision

Additional

A.6. Sustainable Development
A.7. Innovating and Technology Trend Monitoring
A.8. Business/Research Plan Development and Grant
application
A.1. RI and Research Strategy Alignment
B. BUILD: DEVELOP and DEPLOY/IMPLEMENT

B.1. Application Development (Reqs Engineering,
Function Specs, API, HCI)
B.2. Component Integration
B.3. Testing (RI services and Scientific Apps)
B.4. Solution/Apps Deployment

Additional

D.2. Information Security Strategy Development
D.3. RI/ICT Quality Strategy Development
D.7. Purchasing/Procurement
D.8. Contract Management
D.9. Personnel Development
D.10. Dissemination and outreach
E. MANAGE

E.1. Overall RI management (by systems andcomponents)
E.5. Information/Data Security Management
Additional

Additional

E.6. Data Management (including planning and lifecycle
management, curation)
E.4. RI Security and Risk/Dependability Management
E.2. Project and Portfolio Management
E.3. ICT Quality Management and ComplianceE.7. RI/IS Governance

B.5. Documentation Production
B.6. Systems Engineering (DevOps)
C. OPERATE (RUN)

C.1. User Support
C.2. Service Delivery
C.3. Problem Management
Additional

C.4. Change Support (Upgrade/Migration)

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Education and Training• Foundation and methodological base
– Data Science Body of Knowledge (DS-BoK)• Taxonomy and classification of Data Science related scientific subjects– Instructional methodologies and teaching models• Platforms and environment– Virtual labs, datasets, developments platforms– Access control and accounts/identity management– Online education environment and courses management• Services– Individual benchmarking and profiling tools (competence assessment)– Knowledge evaluation tools– Certifications and training– Education and training marketplace: Courses catalog and repository
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Data Science Body of Knowledge (DS-BoK)
DS-BoK Knowledge Area Groups (KAG)• KAG1-DSA: Data Analytics group including
Machine Learning, statistical methods,
and Business Analytics• KAG2-DSE: Data Science Engineering group
including Software and infrastructure engineering• KAG3-DSDM: Data Management group including data curation,
preservation and data infrastructure• KAG4-DSRM: Scientific/Research Methods group• KAG5-DSBP: Business process management group• Data Science domain knowledge to be defined by related expert groups

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KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide for
performing data management”– 11 Knowledge Areas(1) Data Governance,(2) Data Architecture,(3) Data Modelling and Design,(4) Data Storage and Operations,(5) Data Security,(6) Data Integration andInteroperability,(7) Documents and Content,(8) Reference and Master Data,(9) Data Warehousing and BusinessIntelligence,(10) Metadata,(11) Data Quality

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Other Knowledge Areas motivatedby European Open Data
initiatives, European Open Data
Cloud, and RDA (Research Data
Alliance)(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data,
Open Access, ORCID(15) Responsible data use

Data Science Competences and BoK

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Topics considered for the Data Management(Literacy) Training – Working draft
A. Use cases for data management and stewardship•
Preserving the Scientific Record
B. Data Management elements (organisational and individual)•
Goals and motivation for managing your data•
Data formats•
Creating documentation and metadata, metadata for discovery•
Using data portals and metadata registries•
Tracking Data Usage•
Handling sensitive data•
Backing up your data•
Data Management Plan (DMP) - to be a part of hands on session
C. Responsible Data Use Section (Citation, Copyright, Data Restrictions)
D. Open Science and Open Data (Definition, Standards, Open Data use and reuse, open government data)•
Research data and open access•
Repository and self- archiving services•
ORCID identifier for data•
Stakeholders and roles: engineer, librarian, researcher•
Open Data services: ORCID.org, Altmetric Doughnut, Zenodo
E. Hands on:a) Data Management Plan designb) Metadata and toolsc) Selection of licenses for open data and contents (e.g. Creative Common and Open Database)
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European consultation call – Deadline 15 May 2016
https://ec.europa.eu/futurium/en/content/consultation-european-e-infrastructure•

What are the main challenges for the realisation of an integrated European e- infrastructurefrom the perspective of scientific data-related needs (from data access to sharing, analytics,
re-use, preservation, standards, interoperability, value chain and other issues)?

•

What are the challenges for reinforcing the cooperation between European e-infrastructureservice providers and their scientific users, including thematic research infrastructures, toaccelerate user's adoption of e-infrastructure services - such as identity management
innovation - and foster innovation in e-infrastructures?

•

What are the challenges faced by industrial actors preventing them to fully benefit from theservices provided by European e-infrastructures and to contribute to the innovation of theexisting e-infrastructures?

•

What are the main challenges Europe is facing regarding skills and competences
required for effective data driven science, and management of research einfrastructures?

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Further Steps•

Define a taxonomy and classification for DS competences and skills as a basis for moreformal CF-DS definition–•

Closer look at skills, tools and platforms

Create a Questionnaire and run Survey using CF-DS vocabulary–

Run surveys for target communities
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession–•

Proceed with suggested e-CF3.0 extensions and participate in the next e-CF meetings
–

•
•
•

Talk to national e-CF bodies or adopters if available

Provide feedback and contribution to ESCO
Suggest ACM2012 Classification extensions and contact ACM people
Provide input to DS-BoK definition following from CF-DS–•

Plan a number of key interviews, primarily experts and top executives at universities and companies

Link/Map to taxonomy of academic and educational and training courses

Create open community forum to collect contribution––

CF-DS document is on public comments available from EDISON website
http://www.edison-project.eu/data-science-competence-framework-cf-ds
Start related Social Network groups to promote already obtained results and obtain feedback andcommunity contribution

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Survey
Survey link https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession

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