EDISON
Data Science Competence Framework(CF-DS)

Yuri Demchenko, EDISON
University of Amsterdam
CORBEL Workshop: Addressing the skills gaps intechnical operation of biomedical research infrastructuresEDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

17-18 February 2016, EMBL, Hinxton

Outline• EDISON Project approach– From Data Science Competences to Body of Knowledge and ModelCurriculum• Background: Existing frameworks and standards– e-CF3.0 overview and analysis– CWA ICT profiles and mapping to e-CF3.0• Data Science essential competences and skills– Used approach and data/information selection• Organisational processes and role of Data Scientist• Further steps - Survey and questionnaires

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Data Science Competences & Skills

Slide_2

EDISON methodology: Development flow, work
packages, and products

CF-DS
Tax&Inventory

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Roadmap &Sustainability
DS-BoK
MC-DS

EOEE & ETMp

Data Science Competences & Skills

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Background: EU Competence Frameworks and Profiles• e-CFv3.0 - European e-Competence framework for IT• CWA 16458 (2012): European ICT Professional Profiles
Family Tree• ESCO (European Skills, Competences, Qualifications andOccupations) framework

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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 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 ICTorganisations• Provides basis for individual career
path, competence assessment, trainingand certification• EDISON CF-DS will be used for
defining DS-BoK and MC-DS, linking
organizational functions and requiredknowledge• Provide basis for individual (self)
training and certification

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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– Run– Enable– Manage-> Refactor to Scientific Research
cycle/workflow (and linked to Scientific DataLifecycle)– See example of RI manager at IG-ETRD wikiand 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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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 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 theperson’s experience.

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7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 BusinessProcess Management)

Scientific Methods••••••

Design Experiment
Collect DataAnalyse 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

DataManagement
Execution

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

ScientificMethods
Data Science Competences & Skills

••••
••

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

Business ProcessOperations/Stages
•
••
•
•

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

12

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

6
7Collect and manage
different source of data

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 toInfluences the development ofVisualise complex and analyse multiple source of organizational objectives
variable data.
data
Prototype new data analytics
applications

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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

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
analytics platforms

5

Ssearch Methods (DSRM) cientific/Re

Data Science Competences & Skills

Participate strategically andtactically in financial decisions
that impact management andorganizations

Recommends business
related strategic objectives
and alternatives andimplements themProvides scientific, technical,
and analytic support
services to otherorganisational roles
Analyse multiple data sources
for marketing purposes

Analyse customer data toidentify/optimise customer
12relations actions

Identified Data Science Skills/Experience Groups•

Skills/experience related to competences–––––––•Big Data (Data Science) tools and platforms––––––•

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

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

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Identified Data Science Skill Groups
Data Analytics andMachine Learning

Data Management/Curation

Data Science
Engineering (hardwareand software)

Scientific/ ResearchMethods

1

Artificial intelligence,machine learning

Design efficientalgorithms for accessing
and analyzing large
amounts of data

Interest in data science

2Machine Learning andStatistical ModellingManipulating 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

34
5

6
7
8

9
10

Personal/Interpersonalcommunication, teamwork

Application/subject
domain (research orbusiness)

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/distributedsoftware, preferably in a
Linux environment

Confident with large datasets 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 VectorMachines
Predictive analysis
and statistics(including Kaggleplatform)(non)relational and (un)structured data

Cloud powered
applications design(Artificial) NeuralNetworks

Metadata annotationand management

Statistics

Data citation, metadata,
PID (*)

Mechanism Design
and/or Latent Dirichlet
AllocationGame Theory

Copyright and IPR

Cloud based data
storage and data
management
Data management
planning

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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 andPDW, 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)

156

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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

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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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16Possible Data Scientist profiles/roles(as extension to CWA16458 (2012) or ESCO)• Data Analytics– Data Mining– Machine Learning• Data Management– Digital Librarian, Data Archivist, Data Curator, Data Steward• 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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17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 datadriven 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

CORBEL Workshop, Hinxton• Models (and data types)• Methods• Processes

Data Science Competences & Skills

Organisationalroles

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EXAMPLE: Use of e-CF3.0 for Defining Profile ofRI 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 lifecyclemanagement, 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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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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21Survey•Survey link https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession

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