EDISON Services for Core Data Expert Capacity
Building and Skills Management
EDISON Project update

Yuri Demchenko, EDISON Project
University of Amsterdam
RDA 9 IG-ETHRD meeting

5 April 2017, Barcelona
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)EDISON Services Overview: Capacity
Building and Skills Management•EDISON Data Science Framework (EDSF)– Compliant with EU standards on competences andprofessional occupations e-CFv3.0, ESCO
– Customisable courses design for targeted education andtraining•Skills development and career management for CoreData Experts and related data handling professions•Capacity building and Data Science team design•

Academic programmes and professional training
courses (self) assessment and design•

EU network of Champion universities pioneering Data
Science academic programmes•

Engagement in relevant RDA activities and groups•

Cooperation with International professional organisations
IEEE, ACM, BHEF, APEC (AP Economic Cooperation )RDA9 IG-ETHRD - 5 April
2017

EDISON Overview for IG-ETHRD

2EDISON Data Science Framework (EDSF)
Release 1 (October 2016)
DS-BoK

MC-DS

EDISON Online
Educational Environt

CF-DS
Taxonomy andVocabulary

Data ScienceFramework

Edu&Train Marketpltz
and Directory

DS Prof Profiles

Foundation & Concepts•Services

Roadmap &Sustainability• Community
Portal (CP)• Professionalcertification• Data Sciencecareer & profdevelopment

Biz Model

EDISON Framework components••••••

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSP – Data Science Professional profiles
Data Science Taxonomies and Scientific Disciplines Classification
EOEE - EDISON Online Education Environment

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EDISON Overview for IG-ETHRD3

Outcome Based Educations and Training Model

Data Science Competence
Framework (CF-DS)

DS Professional Profiles(DSP-P)

Learning Outcomes (LO)

LearningOutcomes
Outcomes (LO)
Learning(LO)(OBELearningLearningModel)
Model)(OBE(OBE Learning Model)

Competences
Competences
And
KnowledgeAnd
Units (KU)
Skills- (DS-BoK)
SkillsCompetences
Competences
And (LU)
Learning And
Units
Skills- Skills(MC-DS)

From Competences and DSP
Profilesto Learning Outcomes (LO)andto Knowledge Unites (KU) and
Learning Units (LU)• EDSF allow for customized
educational courses and training
modules design

Tracks/SpecialisationLU/KU/DS-PP
Tracks/Specialisations(basedon DSP-P)
Tracks/Specialisation
LU/KU/DS-PP(LO, LU, KU, courses/modules)

DS Academic Programmes

DS Professional Training

CoursesLO/LU/KU/DS-PP
LO/LU/KU/DS-PP
Courses
Courses LO/KU/LU/DSP-P

CoursesLO/LU/KU/DS-PP
LO/LU/KU/DS-PP
CoursesModules LO/KU/LU/DSP-P

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DSP Profiles mapping to ESCO Taxonomy
High Level Groups• DSP Profiles mapping to corresponding CF-DS Competence Groups– Relevance level from 5 – maximum to 1 – minimum
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2017

EDISON Overview for IG-ETHRD

5Individual Competences Benchmarking
M AT C H I N G – C O M P E T E N C E P R O F I L E S
DSP04 - Data Scientist
DSBPM06
DSBPM05
DSBPM04

DSDA01
1098

DSDA02
DSDA03
DSDA04

7DSBPM03

Individual Education/Training Pathbased on Competence benchmarking

Candidate - Data Scientist

DSDA05

6DSBPM02

DSDA0654

DSBPM01

DSDM0132DSRM06• Red polygon indicates the chosen
professional profile: Data Scientist(general)• Green polygon indicates the candidate orpractitioner competences/skills profile• Insufficient competences (gaps) arehighlighted in red– DSDA01 – DSDA06 Data Science Analytics– DSRM01 – DSRM05 Data Science Research
Methods

DSDM02

10
DSRM05

DSDM03

DSRM04

DSDM04

DSRM03

DSDM05

DSRM02

DSDM06

DSRM01•Can be use for team skills match marking andorganisational skills management[ref] For DSP Profiles definition and for enumerated
competences refer to EDSF documents CF-DS and DSPProfiles.

DSENG01

DSENG06
DSENG05

DSENG02
DSENG03
DSENG04RDA9 IG-ETHRD - 5 April
2017

EDISON Overview for IG-ETHRD

6Building a Data Science Team
Data Science
Applications Developer

Data Steward

Data Engineer,
Database Developer

Data Steward

Data Facilities
Operator

Data Scientist
Data Analysis

Researcher(Scientific domain)

Data
SourceDataSource
Data(Experiment,Driven(Experiment, Data
Application)
DrivenApplication)

Data Entry/
SupportData Science GroupManager,
Data Science Architect

Data Science
Applications Developer

Data Scientist

Data Steward
Data Science Researcher
Business Analyst
RDA9 IG-ETHRD - 5 April
2017

Data Analyst/
Business Analyst

Data Scientist
Data Steward
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Recent events and developments•

EDISON Champions conference 15-16 March 2017
– Data Steward role and profile definition– Growth of librarians mastering Data Science– Number of initiatives -> Madrid Communique to be published•PwC and BEHF report “Investing in America’s data science and analytics
talent: The case for action”– Published 30 March 2017
– Contributed by EDISON partners•

DARE project by APEC (Asia Pacific Economic Cooperation)– Initial: Development of the Data Science Analytics checklist– Extend to Body of Knowledge, professional profiles and skills management•IEEE/ACM and other professional communities in USA– 4 March 2017 - Meeting of Professional and Academic Societies to Explore Feasibilityof a Joint Project for Developing Curriculum Recommendation(s) for Data Science

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EDISON Overview for IG-ETHRD

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Data Scientist definition
Based on the definitions by NIST Big Data WG (NIST SP1500 - 2015)•A Data Scientist is a practitioner who has sufficientknowledge in the overlapping regimes of expertise inbusiness needs, domain knowledge, analytical skills,
and programming and systems engineering expertise tomanage the end-to-end scientific method process
through each stage in the big data lifecycle– … Till the delivery of expected scientific and business value
to science or industry•

Other definitions to admit such features as–––••[ref] Legacy: NIST BDWG
definition of Data Science

Ability to solve variety of business problems
Optimize performance and suggest new services for the organisation
Develop a special mindset and be statistically minded, understandraw data and “appreciate data as a first class product”

Data science is the empirical synthesis of actionable knowledge and technologies required tohandle data from raw data through the complete data lifecycle process.
Big Data is the technology to build system and infrastructures to process large volume ofstructurally complex data in a time effective way

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

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

Scientific Methods••••••Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Operations•••••
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2017

EDISON Overview for IG-ETHRD

Operations StrategyPlan
Design & Deploy
Monitor & Control
Improve & Re-design

11Data Science Competences Groups – Business
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 PatternsHypothesise Explanation
Test Hypothesis

Business ProcessOperations/Stages•••••
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2017

EDISON Overview for IG-ETHRD

DesignModel/PlanDeploy & Execute
Monitor & Control
Optimise & Re-design12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
Mathematics & Statistics applications & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisationCloud 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 datamanagement)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods or Business Process Management
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, professional network

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KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide forperforming data management”– 11 Knowledge Areas

Other Knowledge Areas motivated byRDA, European Open Data initiatives,
European Open Data Cloud(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

RDA9 IG-ETHRD - 5 April
2017(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data, OpenAccess, ORCID(15) Responsible data use•

Highlighted in red: Considered (Research)
Data Management literacy (minimumrequired knowledge)

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Useful links• EDISON project website http://edison-project.eu/•

EDISON Data Science Framework Release 1 (EDSF)
http://edison-project.eu/edison-data-science-framework-edsf––––•

Data Science Competence Framework
http://edison-project.eu/data-science-competence-framework-cf-ds
Data Science Body of Knowledge
http://edison-project.eu/data-science-body-knowledge-ds-bok
Data Science Model Curriculum
http://edison-project.eu/data-science-model-curriculum-mc-ds
Data Science Professional Profiles
http://edison-project.eu/data-science-professional-profiles-definition-dsp

Survey Data Science Competences: Invitation to participate
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession

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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– Present/visualise information in domain related actionable way– 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
Visualisation• 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(visualization)• Models (and data types)• Methods• Processes

Organisationalroles

Data Scientist role is to maintain the Data Value Chain (domain specific):•

Data Integration => Organisation/Process/Business Optimisation => Innovation

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