Defining the Data Science Competence Framework(CF-DS)
Overview of Existing Studies and Proposed Approach

Yuri Demchenko
University of Amsterdam

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

CEN e-CF Workshop @AFNOR
9 December 2015, ParisOutline• EDISON Project approach– From Data Science Competences to Body of Knowledge and Model
Curriculum• e-CF3.0 overview and analysis• CWA ICT profiles and mapping to e-CF3.0• Data Science essential competences and skills– Demand side and job market analysis• Organisational workflow/processes and role of Data Scientist• Further steps - Survey and questionnaires

11th e-Concertation on eInfrastructure, 9 Nov 2015

EDISON & Data Science Profession

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EDISON Objectives
Objective 1: Data Science Curricula Foundation
Promote the creation of Data Scientists curricula by an increasing number of universities andprofessional training organisations.
The Data Science Competence Framework (CF-DS) including Taxonomy of competences andskills, compliant with e-CF3.0.
The Body of Knowledge (BoK) for the Data Science (DS-BoK) that will map requiredcompetencies/skills and existing academic, research and technology disciplines
A Model Curriculum for Data Science (MC-DS) as a template for building customisable Data
Science curricula based on the proposed CF-DS and DS-BoK.

Objective 2: Education and Training Environment
Provide environment for re-skilling and certifying Data Scientists expertise to graduates,
practitioners and researchers throughout their careers.
Create EDISON Education and Training Marketplace by leveraging EGI Engage Training
Marketplace.

Objective 3: Sustainability Model
Develop a sustainable business model and a roadmap for European education and training
on Data Science technologies, provide a basis for the formal recognition of the Data Scientist
as a new profession
Establish networks and community of champion universities
Create Community of practice for sustainable Data Science education and training supported by
EDISON Liaison Group(s)11th e-Concertation on eInfrastructure, 9 Nov 2015

EDISON & Data Science Profession

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Basic methodology of EDISON: Development flow,
work packages, and products

CF-DS
Tax&Inventory

11th e-Concertation on eInfrastructure, 9 Nov 2015

Roadmap &Sustainability

DS-BoK
MC-DS

ETM-DSEDISON & Data Science Profession

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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 lifecycle, organizational roles, competences, skills and knowledge– 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

EDISON ELG Meeting

DS Competence Framework

5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– Deploy– Run– Manage-> Refactor to Scientific Research (orScientific 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)EDISON ELG Meeting

DS Competence Framework

6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 is not to be confused with process or technology concepts
such as, ‘Cloud Computing’ or ‘Big Data’. These descriptions represent
evolving technologies and in the context of the e-CF, they may be
integrated as elements within knowledge and skill examples.
• 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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DS Competence Framework

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EDISON CF-DS profile(s) and e-CF3.0
Edison Profile(s)For Data Science
1. Define CF-DS profile
using input from
1. Demand/Jobs
market2. Surveys,
Interview
3. Questionnaires
4. DS programmes
2. Map required
background ICT
competences from eCF3.0 and ICT
profiles3. Identify required
extensions to e-CF3.0

EDISON ELG Meeting

e-CF 3.0

Dimension 1: Areas of
e-Competences

 A
 List e-CF ICTProfiles B

 C  D  E
1

1

n
n

Dimension 2: e-competences
A.1-A.9

B.1-B.6

E.1E.9

C.1-C.4 D.1-D12

15

Dimension 3:
Proficiencylevel

DS Competence FrameworknKnowledgenSkills8

CWA 16458 (2012): European ICT Professional
Profiles Family Tree•

European ICT Profile Family Tree – Generation 1 and 2 as a shared European reference

EDISON ELG Meeting

DS Competence Framework9Mapping between e-CF3.0 and European ICTProfiles

ICT04 Chief
Info
Officr

ICT03
Biz
Info
Mngr

ICT10

ICT16
ICT
Oper
Mngr

Business Management

ICT02

Design
Entep
Archi
tect

ICT09
ICT
Cons
ultant

ICT21
ICT08

Acco
unt
Mngr

ICT01

ICT15

Prj
Mngr

ICT07
Digit
Media
Spec

Devel
oper

Development

Syst
Archi
tect

Test
Spec

DB
Admn

MANAGE

ICT18

ICT
Train
er

Serv
Mngr

ICT05

ICT17 Serv

Desk
Mngr

Services &Operation
Tech
ICT19

Sys
admn

Netw
Spec

BUILD ICT23

PLAN

Support
•

ICT20

Syst
Analy
st

ICT11

Technical Management
ICT06

Biz
Analy
st

ICT
Sec
Mngr

QA
Mngr

Spec

ICT14

ICT22

RUN
ICT12

ICT13

ICT
Sec
Spec

ENABLE

European ICT Professional Profiles structured by six families and positioned within the ICT Business Process (eCF Dimension 1)

EDISON ELG Meeting

DS Competence Framework

10Demanded Data Science Competences andSkills: Jobs market analysis• Source– IEEE Data Science Jobs (World but majority US) (collected > 120,
selected for analysis > 30)– LinkedIn Data Science Jobs (NL) (collected > 140, selected foranalysis > 30)– Existing studies and reports• 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 without specified skills profile– Explicit Data Scientist jobs specify wide variety of expectedfunctions/responsibilities and required skills and knowledge

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Identified Data Science Competence Groups•

Traditional/known Data Science skills/knowledge
profiles include
–
–
–

•

EDISON identified 2 additional competence groups demanded by organisations––•[ref] Legacy: NIST BDWG
definition of Data Science

Inter-personal skills or team work, cooperativenessAll groups need to be represented in Data Science curriculum and training–•

Data Management, Curation, Preservation
Scientific or Research Methods and/vs Business Operations/Processes

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

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

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

EDISON ELG Meeting

DS Competence Framework

12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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DS Competence Framework

Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design

13

Data Science Competences Areas – Business eCF areas
Data Science Competence
includes 5 areas/groups

DOMAIN
EXPERTISE

•
•
•
•
•

DesignOptimisation

RESEARCH

DATA

DATA
ANALYTICS

Scientific Methods

SCIENCEANALYTIC ALGORITHMS
SYSTEMS

Monitoring

Modelling
ENGINEERINGCOMPETENCES

Business Process
Management

Data
Management
Execution

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

ScientificMethods

•
•
•
•
•
•

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

Business Process
Operations/Stages
•
•
•
•
•

DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design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.
Develop specialized
analytics to enable agile
decision making

5

6
7
EDISON ELG Meeting

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
Develop and apply
Develop and maintain computational solutions toa historical data
domain related problems
repository of analysis using wide range of data
analytics platforms

Collect and manage
different source of data

Ssearch Methods (DSRM) cientific/Re

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
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

DS Competence Framework

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
15
relations actions

Identified Data Science Skills/Experience Groups•

Skills/experience related to competences– 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– Personal, inter-personal communication, team work (also called social intelligence or soft
skills)– Application/subject domain related (research or business)– Mathematics and Statistics•

Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Big Data Analytics platforms
Math& Stats tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
No cloud related skills and knowledge mentioned explicitly

Programming and programming languages and IDE– General and specialized for data analysis and statistics•

Interpersonal skills (social intelligence)

EDISON ELG Meeting

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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

2

Machine Learning andStatistical Modelling

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

Machine learning
solutions and patternrecognition
techniques

3

4
5

6
7
8

9
10

Application/subject
domain (research or
business)

Interest in data science

Personal/Interpersonalcommunication, teamwork
Communication skills

Big Data solutions andadvanced data mining
tools

Analytical, independent,
critical, curious and focusedon results

Inter-personal intrateam and external
communication

Data Analytics forcommercial purposes

Data models anddatatypes

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

Supervised andunsupervised learning

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 (*)

Cloud based data
storage and data
management
Data management
planning

Recommender or
Ranking system

Mechanism Design
and/or Latent Dirichlet
AllocationGame Theory

Copyright and IPR

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

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

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•
B.10* Data Science infrastructure deployment
C. RUN: Operate•
C.5* User/Usage data/statistics analysis•
C.6* Service delivery/quality data monitoring

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)

EDISON ELG Meeting

DS Competence Framework

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Need for separate Data Science Competence
Framework definition?
• There is no direct mapping of required/identified DS competences andskills to e-CF areas (i.e. organizational workflow stages)– Data Scientist is involved into all stages/areas– In most cases Data Scientist competences are connected to Data Lifecycle andnot organizational workflow• Data Scientist is a cross-intra-organizational role
– Interact with different roles– Deliver information to top management• Initially assistive but may play key (leading) role in data driven
organizational processes and services– Potentially may have best organizational insight– Provide a basis for a future CEO mindset• e-CF3.0 extensions with specific Data Science competences as a firststep

EDISON ELG Meeting

DS Competence Framework

20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
EDISON ELG Meeting

DS Competence Framework

21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

EDISON ELG Meeting• Models (and data types)• Methods• Processes

DS Competence Framework

Organisational
roles

22

Possible Data Scientist profiles/roles• Data Analytics– Data Mining– Machine Learning• Data Management– Digital Librarian, Data Archivist, Data Curator• Data Science Engineering– Data Analytics applications development– Scientific programmer– Data Science/Big Data Infrastructure engineer/developer/operator• Data Science Researcher– Data Science creative– Data Science consultant/Analyst• Business Analyst• Data Scientist in subject/research domain• Research e-Infrastructure brings its own specifics to requiredcompetences and skills definition
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Mapping Data Scientist Competences to e-CF3.0• Use e-CF to identify– ICT/CS background competences/skills/knowledge– Required basic education and training• Use Data Science CF-DS to define additional
training/re-skilling– From general ICT/(Librarian) to Data Scientist• Specialised Data Science courses– From Data Scientist practitioner to certified Data Scentist• General IT and CS courses

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EXAMPLE: e-CF Dimensions for RI Technical(based on RDA IG-ETRD work)• Dimension 1: 5 e-Competence areas, derived from the ICT processes
present in RI development, management and operation:
A. PLAN and DESIGN
B. BUILD: DEVELOP and DEPLOY/IMPLEMENT
C. OPERATE (RUN)
D. USE: UTILISE (ENABLE)
E. MANAGE• Dimension 2: A set of reference competences for each area; currently
identified 35 competences that are mapped from the general eCFv3.0.
• Dimension 3: Proficiency levels of each e-Competence, currently usingeCF approach that provides European reference level specifications on
e-Competence levels e-1 to e-5, which are related to the EQF levels 3 to8.

• Dimension 4: Samples of knowledge and skills related to e-Competencesin dimension 2. They will be provided to add value and context and are
not intended to be exhaustive.
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EXAMPLE: How to use eCF for New Profile of RI
Technical
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 Sci Apps)
B.4. Solution/Apps Deployment
Additional

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

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

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

Additional

C.4. Change Support (Upgrade/Migration)

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Further Steps• Define a taxonomy and classification for DS competences and skills as abasis for more formal CF-DS definition– Closer look at skills, tools and platforms• Suggest e-CF3.0 extensions and present them at ELG meeting andworkshop in Bari
– Provide feedback and contribution to CEN workshop on e-Competence for their
meeting on 9 Dec 2015 in Paris– Talk to national e-CF bodies or adopters if available• Create a Questionnaire using CF-DS vocabulary– Run surveys for target communities• First of all, for EGI community• Create open community forum to collect contribution– Explore LinkedIn opportunities– Plan a number of key interviews, primarily experts and top executives atuniversities and companies• Provide input to DS-BoK definition following from CF-DS– Link/Map to taxonomy of academic and educational and training courses• Start related Social Network activity to promote already obtained results
related to identified new Data Scientist competences and skills
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