EDISON Data Science Framework (EDFS):
Education and training for Data Science and data
related skills

Yuri Demchenko, EDISON Project
University of Amsterdam

EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)EDISON workshop: From Infrastructure to
skills for insight and value
Amsterdam, 29 August 2017

Outline• Background: Data driven research and demand for new skills– Recent reports, studies and facts• EDISON Data Science Framework (EDSF)– Data Science competences and skills– Essential Data Scientist professional skills: Thinking and doing like Data Scientist• Data Science Professional Profiles• Data Science Body of Knowledge and Model Curriculum• Use of EDSF and Example curricula– Competences assessment– Building Data Science team

• Roadmap recommendations• Discussion and Questions

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Visionaries and Drivers:
Seminal works, High level reports, Activities
The Fourth Paradigm: Data-Intensive Scientific Discovery.
By Jim Gray, Microsoft, 2009. Edited by Tony Hey, Kristin Tolle, et al.
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
Riding the wave: How Europe can gain fromthe rising tide of scientific data.
Final report of the High Level Expert Group on
Scientific Data. October 2010.
http://cordis.europa.eu/fp7/ict/einfrastructure/docs/hlg-sdi-report.pdf

The Data Harvest: Howsharing research data
can yield knowledge,
jobs and growth.
An RDA Europe Report.
December 2014

https://www.rd-alliance.org/
HLEG report on European
Open Science Cloud(October 2016)

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https://rd-alliance.org/dataharvest-report-sharing-dataknowledge-jobs-and-growth.html

Emergence of Cognitive Technologies(IBM Watson, Cortana and others)
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Riding the wave (2010): How Europe can gainfrom the rising tide of scientific data.
•
•

•

•

“Unlocking the full value of scientific data”- Neelie Kroes, Vice-President of the European Commission, responsible forthe Digital Agenda
Just how students will be trained in the future, or how the profession of“data scientist” will be developed, are among the questions the resolutionof which is still evolving and will present intellectual challenges for bothprivately and publicly supported research.
- John Wood, HLEG Chair
Vision 2030: “Our vision is a scientific e-Infrastructure that supports seamless
access, use, re-use and trust of data. In a sense, the physical and technicalinfrastructure becomes invisible and the data themselves become theinfrastructure.”
Proposed set of actions– 4. Train a new generation of data scientists, and broaden public understanding
We urge that the European Commission promote, and the member-states adopt, newpolicies to foster the development of advanced-degree programmes at our major universities
for the emerging field of data scientist. We also urge the member-states to include data
management and governance considerations in the curricula of their secondary schools, aspart of the IT familiarisation programmes that are becoming common in European education.

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The Data Harvest (2014): How sharing research data
can yield knowledge, jobs and growth• Planning the data harvest – John Wood
• The era of data driven science• We want the right minds, with the right data, at the right time. That’s a tallorder that requires change in:– The way science works and scientists think– How scientific institutions operate and interact– How scientists are trained and employed

Recommendation 2• DO promote data literacy across society, from researcher to citizen.
Embracing these new possibilities requires training and cultural education– inside and outside universities. Data science must be promoted– A first-class science: Data sharing provides the foundation for a new branch ofscience.
– Data education: Training in the use, evaluation and responsible management ofdata needs to be embedded in curricula, across all subjects, from primary school
to university.
– Training within EU projects– Government and public sector training
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HLEG EOSC Report Essentials – Core Data
Experts•

Core Data Experts is a new class of colleagues with core scientific professional
competencies and the communication skills to fill the gap between the two
cultures.
– Core data experts are neither computer savvy research scientists nor are they hardcore data or computer scientists or software engineers.
– They should be technical data experts, though proficient enough in the content domain
where they work routinely from the very beginning (experimental design, proposal
writing) until the very end of the data discovery cycle– Converge two communities:• Scientists need to be educated to the point where they hire, support and respect Core Data
Experts• Data Scientists (Core Data Experts) need to bring the value to scientific research andorganisations•

Implementation of the EOSC needs to include instruments to help train, retain
and recognise this expertise,– In order to support the 1.7 million scientists and over 70 million people working ininnovation.

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EOSC Report Recommendations –Implementation on training and skills•

I2.1: Set initial guiding principles to kick-start the initiative as quickly as
possible.
––•A first cohort of core data experts should be trained to translate the needs for data driven science
into technical specifications to be discussed with hard-core data scientists and engineers.
This new class of core data experts will also help translate back to the hard- core scientists thetechnical opportunities and limitations

I3: Fund a concerted effort to develop core data expertise in Europe.
––

Substantial training initiative in Europe to locate, create, maintain and sustain the required core dataexpertise.
By 2022, to train (hundreds of thousands of) certified core data experts with a demonstrable
effect on ESFRI/e-INFRA activities and prospects for long-term sustainability of this criticalhuman resource••

Consolidate and further develop assisting material and tools for Data Management Plans and Data
Stewardship plans (including long-term preservation in FAIR status)

I7: Provide a clear operational timeline to deal with the early preparatory
phase of the EOSC.
–

Define training needs for the necessary data expertise and draw models for the necessary
training infrastructure

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Initiatives: GO FAIR and IFDS
• Global Open FAIR
– Findable – Accessible – Interoperable - Reusable• IFDS – Internet of FAIR Data and Services = EOSC
• GO FAIR implementation approach– GO-BUILD– GO-CHANGE– GO-TRAIN: Training of data stewards capable of providing FAIR dataservices• A critical success factor is availability of expertise in data
stewardship– Training of a new generation of FAIR data experts is urgently needed
to provide the necessary capacity.

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Industry reports on Data Science Analytics and Dataenabled skills demand•

Final Report on European Data Market Study by IDC (Feb 2017)– The EU data market in 2016 estimated EUR 60 Bln (growth 9.5% fromEUR 54.3 Bln in 2015)• Estimated EUR 106 Bln in 2020
– Number of data workers 6.1 mln (2016) - increase 2.6% from 2015
• Estimated EUR 10.4 million in 2020
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply estimated 769,000 (2020) or 9.8%•

PwC and BHEF report “Investing in America’s data science andanalytics talent: The case for action” (April 2017)
–
–
–

•

http://www.bhef.com/publications/investing-americas-data-science-andanalytics-talent2.35 mln postings, 23% Data Scientist, 67% DSA enabled jobs
DSA enabled jobs growing at higher rate than main Data Science jobs

Citing EDISON and EDSF

Burning Glass Technology, IBM, and BHEF report “The Quant
Crunch: How the demand for Data Science Skills is disrupting the
job Market” (April 2017)–––https://public.dhe.ibm.com/common/ssi/ecm/im/en/iml14576usen/IML14576
USEN.PDF
DSA enabled jobs takes 45-58 days to fill: 5 days longer than average
Commonly required work experience 3-5 yrs
Influenced by EDISONEDISON 2017 Workshop

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PwC&BHEF: Demand for DSA enabled jobs
Demand for business people withanalytics skills, not just data
scientists• Of 2.35 million job postings in
the US– 23% Data Scientist– 67% DSA enabled jobs

• Strong demand for managers
and decision makers with Data
Science (data analytics)
skills/understanding– Challenge to deliver actionableknowledge and competences to
CEO level managers

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PwC&BHEF: Data Science and Data Analytics
Competences for Managers and Decision Makers

Percent of employers who say data science and analytics skills will be ‘required of all managers’ by 2020•
Source: BHEF and Gallup, Data Science and Analytics Business Survey (December 2016).
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PwC&BHEF: Skills that are tough to findFaster growing jobs require both analyticaland social skills

To be mapped to Competences, Knowledge, Skills and Personal (soft) Skills
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PwC&BHEF: Data Science and Analytics skills, by
2021: The supply-demand challenge

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Closer look at Data related Jobs and Salaries (2016)

Source: The Jon Market for Data Professionals, by Robert R Downs, SciDataCon2016
http://www.scidatacon.org/2016/sessions/98/poster/51/
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2017: What is the answer from RI and Science?

• New study? New report focused on Scientific Data
Infrastructure?
• Who is the Rider? Who is the Harvester?
• Demand for new data driven competences and skills
and new type of professionals – Core data experts– The whole family of professions is required

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Challenge for Education:
Sustainable ICT and Data Skills Development• Educate vs Train– Training is a short term solution– Education is a basis for sustainable skills development• Technology focus changes every 3-4 years– Study: 50% of academic curricula are outdated at the time of graduation• Lack of necessary skills leads to underperforming projects andorganisations and loose of competitiveness– Challenge: Policy and decision makers still don’t include planning human factor(competences and skills) as a part of the technology strategy• Need to change the whole skills management paradigm– Dynamic (self-) re-skilling: Continuous professional development and shared
responsibility between employer and employee– Professional and workplace skills and career management as a part ofprofessional orientation• Millennials factor and changing nature of workforce
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EDISON Products for Data Science Skills
Management and Tailored Education•

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 Core
Data 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 )EDISON 2017 Workshop

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EDISON Data Science Framework (EDSF)
DS-BoK

MC-DS

EDISON Online
Educational Environ

CF-DS
Taxonomy andVocabulary

Data Science
Framework

Edu&Train Marketpltz
and Directory

DS Prof Profiles

Foundation & Concepts

Services

Roadmap &Sustainability• Community
Portal (CP)• Professional
certification• Data Science
career & prof
development

Biz Model

EDISON Framework components

Methodology

–
–
–
–
–

•

ESDF development based on job market
study, existing practices in academic,
research and industry.

•

Review and feedback from the ELG, expert
community, domain experts.

•

Input from the champion universities andcommunity of practice.

–

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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What challenges related to skills management the
EDSF can help to address?
1. Guide researchers in using right methods and tools, latest Data
Analytics technologies to extracting value from scientific data
2. Educate and train RI engineers dev to build modern data intensive
research infrastructure and understand trends and project for future
3. Develop new data analytics tools and ensure continuous improvement(agile model, DevOps)
4. Correctly organise and manage data, make them accessible (adhering
FAIR principles), education new profession of Data Stewards
5. Help managers to facilitate career dev for researchers and organise
effective teams
6. Ensure skills and expertise sustain in organisation
7. Help research institutions to sustain in competition with industry andbusiness in data science talent hunting

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Competences Map to Knowledge and Skills• Competence is a demonstrated ability to apply knowledge,
skills and attitudes for achieving observable resultsCompetences

Knowledge

Ability toperform Org
functions

SkillsExperience &Workplace Train

Education (BoK)
Professional
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Data Scientist definition
Based on the definitions by NIST SP1500 – 2015, extended by EDISON•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
to manage the end-to-end scientific method process
through each stage in the big data lifecycle till the deliveryof an expected scientific and business value toorganisation or project.
•Core Data Science competences and skills groups
–
–
–

•

EDISON identified 2 additional competence groups demanded by organisations––•

Data Science Analytics (including Statistical Analysis, Machine Learning, Business Analytics)
Data Science Engineering (including Software and Applications Engineering, Data
Warehousing, Big Data Infrastructure and Tools)
Domain Knowledge and Expertise (Subject/Scientific domain related)
Data Management, Data Governance, Stewardship, Curation, Preservation
Research Methods and/vs Business Processes/Operations

Data Science professional skills: Thinking and acting like Data Scientist – required tosuccessfully develop as a Data Scientist and work in Data Science teams

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Data Science Competence Groups - Research
Data Science Competences
include 5 groups
•
•
•
•
•

Data Science Analytics
Data Science Engineering
Domain Knowledge and Expertise
Data Management
Scientific Methods or Business
Process Management

Scientific Methods
•
•
•
•
•
•

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

Business Operations
•
•
•
•
•

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Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design22Data Science Competences Groups – Business
Data Science Competences
include 5 groups
•
•
•
•
•

Data Science Analytics
Data Science Engineering
Domain Knowledge and Expertise
Data Management
Scientific Methods or Business
Process Management

Scientific Methods
•
•
•
•
•
•

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

Business Process
Operations/Stages
•
•
•
•
•
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DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design23

Identified Data Science Competence Groups
Data Science Analytics Data Science Engineering(DSDA)(DSENG)0

Use appropriate data
analytics andstatistical techniqueson available data todeliver insights into
research problem ororg. processes andsupport decision
makingUse engineering principles
and modern computer
technology to research,
design, implement new data
analytics applications,develop experiments,
processes, instruments,
systems and infrastructures
to support data handlingduring the whole data
lifecycle

1

DSDA01
Effectively use variety
of data analytics
techniques

DSENG012

DSDA02
Apply designatedquantitative techniques

DSENG02
Develop and apply computer
methods to domain related
problems

Use engineering principles (generaland software) to research, design,develop and implement new
instruments and applications

Data Management and Research/Scientific Methods Data Science Domain
Governance (DSDM)
and Project Management
Knowledge, e.g. Business(DSRMP)
Analytics (DSDK/DSBPM)
Develop andCreate new understandings DSDK/DSBA
implement dataand capabilities by using
Use domain knowledge
management strategy the scientific method(scientific or business) tofor data collection,(hypothesis, test/artefact,
develop relevant data
storage, preservation, evaluation) or similar
analytics applications; adopt
and availability forengineering methods togeneral Data Science methods
further processing.
discover new approaches to to domain specific data types
create new knowledge and and presentations, data andachieve research orprocess models,organisational goals
organisational roles andrelations
DSDM01Develop and implement
data strategy, inparticular, DataManagement Plan (DMP)

DSRMP01
Create new understandings andcapabilities by using scientific/
research methods

DSBPM01
Understand business and provide
insight, translate unstructured
business problems into an
abstract mathematical framework

DSDM02Develop data models
including metadata

DSRMP02
Direct systematic study toward
a fuller knowledge orunderstanding of theobservable facts
DSRMP03
Undertakes creative work

DSBPM02
Participate strategically andtactically in financial decisions

DSDA03
DSENG03
DSDM03Pull together data from Develop and prototype data
Collect integrate data
diff sources …
analytics applications
DSENG04
DSDM04
DSRMP04
4 DSDA04
Use diff perform
Develop, deploy operate Big Data Maintain repository
Translate strategies into actions
techniquesstorage
DSENG05
DSDM05
DSRMP05
5 DSDA05
Develop analytics applic Apply security mechanisms
Visualise cmplx data
Contribute to organis goals
DSDA06
DSENG06
DSRM06
DSRMP06
6
Visualise
resultsof
Design,build,
operateSQLandDevelopandmanageDevelop and guide data driven
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analysis, dashboards
NoSQL
prolicies
projects
3

DSBPM03
Provides support services to other
DSBPM04
Analyse data for marketing
DSBPM05
Analyse optimise customer relatio
DSBPM06
Analyse data for marketing 24

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 visualisation
Cloud based platforms and tools

Group 3: Data analytics 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 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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Key Data Science Analytics Competencesby EDISON and DARE Project for APEC countries• Data Science Analytics, Data Science Engineering, Data Management,
Research Methods, Domain Knowledge– 3 levels of proficiency defined: Entry/Associate, Professional, Expert/Lead• Foundational competences (starting from entry level to expert level)– Statistics, Probability theory, mathematics, calculus– Statistical programming languages, frameworks, tools– Computational methods and document processing tools (including Excel,
Office visualization, or similar)– Data Visualisation and tools• 21st Century Skills and Data Science Professional skills

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Data Science Professional Skills:
Thinking and Acting like Data Scientist
1.
2.
3.
4.
5.
6.
7.
8.

9.
10.
11.
12.
13.
14.

15.

Recognise value of data, work with raw data, exercise good data intuition, use SN and open data
Accept (be ready for) iterative development, know when to stop, comfortable with failure, accept thesymmetry of outcome (both positive and negative results are valuable)
Good sense of metrics, understand importance of the results validation, never stop looking at
individual examples
Ask the right questions
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
Be aware about power and limitations of the main machine learning and data analytics algorithms
and tools
Understand that most of data analytics algorithms are statistics and probability based, so any
answer or solution has some degree of probability and represent an optimal solution for a number
variables and factors
Recognise what things are important and what things are not important (in data modeling)
Working in agile environment and coordinate with other roles and team members
Work in multi-disciplinary team, ability to communicate with the domain and subject matter experts
Embrace online learning, continuously improve your knowledge, use professional networks andcommunities
Story Telling: Deliver actionable result of your analysis
Attitude: Creativity, curiosity (willingness to challenge status quo), commitment in finding new
knowledge and progress to completion
Ethics and responsible use of data and insight delivered, awareness of dependability (data scientist isa feedback loop in data driven companies)

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Data Science Professional Skills:
Thinking and Acting like Data Scientist (1)
1.
2.

3.
4.
5.
6.
7.
8.

Recognise value of data, work with raw data, exercise good data intuition, use
SN and Open Data
Accept (be ready for) iterative development, know when to stop, comfortablewith failure, accept the symmetry of outcome (both positive and negative results
are valuable)
Good sense of metrics, understand importance of the results validation, neverstop looking at individual examples
Ask the right questions
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
Be aware about power and limitations of the main machine learning and data
analytics algorithms and tools
Understand that most of data analytics algorithms are statistics andprobability based, so any answer or solution has some degree of probability
and represent an optimal solution for a number variables and factors

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Data Science Professional Skills:
Thinking and Acting like Data Scientist (2)
1.
2.
3.
4.
5.
6.
7.
8.

S
S
s
Ss
S
S
S
s

9.

Recognise what things are important and what things are not important (in data
modeling)
Working in agile environment and coordinate with other roles and team members
Work in multi-disciplinary team, ability to communicate with the domain and subject
matter experts
Embrace online learning, continuously improve your knowledge, use professional
networks and communities
Story Telling: Deliver actionable result of your analysis
Attitude: Creativity, curiosity (willingness to challenge status quo), commitment in finding
new knowledge and progress to completion
Ethics and responsible use of data and insight delivered, awareness of dependability(data scientist is a feedback loop in data driven companies)

10.
11.

12.
13.
14.
15.

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21st Century Skills (DARE & BHEF & EDISON)
1.

Critical Thinking: Demonstrating the ability to apply critical thinking skills to solve
problems and make effective decisions
2. Communication: Understanding and communicating ideas
3. Collaboration: Working with other, appreciation of multicultural difference
4. Creativity and Attitude: Deliver high quality work and focus on final result, intitiative,
intellectual risk
5. Planning & Organizing: Planning and prioritizing work to manage time effectively andaccomplish assigned tasks
6. Business Fundamentals: Having fundamental knowledge of the organization and theindustry
7. Customer Focus: Actively look for ways to identify market demands and meet customer
or client needs
8. Working with Tools & Technology: Selecting, using, and maintaining tools andtechnology to facilitate work activity
9. Dynamic (self-) re-skilling: Continuously monitor individual knowledge and skills as
shared responsibility between employer and employee, ability to adopt to changes
10. Professional networking: Involvement and contribution to professional network activities
11. Ethics: Adhere to high ethical and professional norms, responsible use of power data
driven technologies, avoid and disregard un-ethical use of technologies and biased datacollection and presentation

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Practical Application of the CF-DS• Basis for the definition of the Data Science Body of Knowledge(DS-BoK) and Data Science Model Curriculum (MC-DS)– CF-DS => Learning Outcomes (MC-DS) => Knowledge Areas (DS-BoK)– CF-DS => Data Science taxonomy of scientific subjects and vocabulary• Data Science professional profiles definition– Extend existing EU standards and occupations taxonomies: e-CFv3.0,
ESCO, others• Professional competence benchmarking– For customizable training and career development– Including CV or organisational profiles matching• Professional certification– In combination with DS-BoK professional competences benchmarking• Vacancy construction tool for job advertisement (for HR)– Using controlled vocabulary and Data Science Taxonomy

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Data Science Professions Family – By Groups

Icons used: Credit to [ref] https://www.datacamp.com/community/tutorials/data-science-industry-infographic

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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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EDSF for Education and Training• Foundation and methodological base
– Data Science Body of Knowledge (DS-BoK)• Taxonomy and classification of Data Science related scientific subjects– Data Science Model Curriculum (MC-DS)• Set Learning Units mapped to CF-DS Learning and DS-BoK Knowledge
Areas/Units– Instructional methodologies and teaching models• Platforms and environment– Virtual labs, datasets, developments platforms– Online education environment and courses management• Services– Individual benchmarking and profiling tools (competence assessment)– Knowledge evaluation tools– Certifications and training for self-made Data Scientists practitioners– 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: Research Methods and Project Management group• KAG5-DSBA: Business Analytics and Business Intelligence• KAG* - DSDK: Data Science domain knowledge to be defined by related
expert groups

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Data Science Body of Knowledge (1)KA Groups

Suggested DS Knowledge Areas (KA)

KAG1-DSDA: Data
Science Analytics

KA01.01 (DSDA.01/SMDA) Statistical methods fordata analysis
KA01.02 (DSDA.02/ML) Machine Learning
KA01.03 (DSDA.03/DM) Data Mining
KA01.04 (DSDA.04/TDM) Text Data Mining
KA01.05 (DSDA.05/PA) Predictive Analytics
KA01.06 (DSDA.06/MODSIM) Computationalmodelling, simulation and optimisation
KA02.01 (DSENG.01/BDI) Big Data Infrastructure
and Technologies
KA02.02 (DSENG.02/DSIAPP) Infrastructure andplatforms for Data Science applications
KA02.03 (DSENG.03/CCT) Cloud Computing
technologies for Big Data and Data Analytics
KA02.04 (DSENG.04/SEC) Data and Applications
security
KA02.05 (DSENG.05/BDSE) Big Data systems
organisation and engineering
KA02.06 (DSENG.06/DSAPPD) Data Science (Big
Data) applications design
KA02.07 (DSENG.07/IS) Information systems (tosupport data driven decision making)

KAG2-DSENG: Data
Science Engineering

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Knowledge Areas from existing BoK and CCS2012
scientific subject groups
There is no formal BoK defined for Data Analytics.
Data Science Analytics related scientific subjects
from CCS2012:
CCS2012: Computing methodologies
CCS2012: Mathematics of computing
CCS2012: Computing methodologies
ACM CS-BoK selected KAs:
AR - Architecture and Organization (including
computer architectures and networkarchitectures)
CN - Computational Science
IM - Information Management
SE - Software Engineering (can be extended withspecific SWEBOK KAs)SWEBOK selected KAs•
Software requirements•
Software design•
Software engineering process•
Software engineering models and methods•
Software quality
Data Science Analytics related scientific subjects
from CCS2012

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Data Science Body of Knowledge (2)KA Groups

Suggested DS Knowledge Areas (KA)

KAG3-DSDM: DataManagement

KA03.01 (DSDM.01/DMORG) General principles
and concepts in Data Management andorganisation
KA03.02 (DSDM.02/DMS) Data management
systems
KA03.03 (DSDM.03/EDMI) Data Management andEnterprise data infrastructure
KA03.04 (DSDM.04/DGOV) Data GovernanceKA03.05 (DSDM.05/BDST0R) Big Data storage(large scale)
KA03.06 (DSDM.05/DLIB) Digital libraries andarchives
KA04.01 (DSRMP.01/RM) Research Methods
KA04.01 (DSRMP.02/PM) Project Management

KAG4-DSRM: Research
Methods and ProjectManagement

KAG5-DSBPM:
Business Analytics

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KA05.01 (DSBA.01/BAF) Business Analytics
Foundation
KA05.02 (DSBA.02/BAEM) Business Analytics
organisation and enterprise management

Knowledge Areas from existing BoK and CCS2012
scientific subject groups
DM-BoK selected KAs(1) Data Governance,(2) Data Architecture,(3) Data Modelling and Design,(4) Data Storage and Operations,(5) Data Security,(6) Data Integration and Interoperability,(7) Documents and Content,(8) Reference and Master Data,(9) Data Warehousing and Business Intelligence,(10) Metadata, and(11) Data Quality.
There are no formally defined BoK for researchmethods
PMI-BoK selected KAs•
Project Integration Management•
Project Scope Management•
Project Quality•
Project Risk Management
BABOK selected KAs *)Business Analysis Planning and Monitoring
Requirements Life Cycle Management
Solution Evaluation and improvements
recommendation

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Data Science Model Curriculum (MC-DS)
Data Science Model Curriculum includes• Learning Outcomes (LO) definition based on CF-DS– LOs are defined for CF-DS competence groups and for all
enumerated competences– Knowledge levels: Familiarity, Usage, Assessment (based in Bloom’s
Taxonomy)• LOs mapping to Learning Units (LU)
– LUs are based on CCS(2012) and universities best practices– Data Science university programmes and courses inventory(interactive)
http://edison-project.eu/university-programs-list• LU/course relevance: Mandatory Tier 1, Tier 2, Elective,
Prerequisite• Learning methods and learning models (in progress)
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Knowledge levels for Learning Outcomes
Level
Familiarity

Action Verbs
Choose, Classify, Collect, Compare, Configure, Contrast, Define,
Demonstrate, Describe, Execute, Explain, Find, Identify, Illustrate,
Label, List, Match, Name, Omit, Operate, Outline, Recall, Rephrase,
Show, Summarize, Tell, Translate

Usage

Apply, Analyze, Build, Construct, Develop, Examine, Experiment
with, Identify, Infer, Inspect, Model, Motivate, Organize, Select,
Simplify, Solve, Survey, Test for, Visualize

Assessment

Adapt, Assess, Change, Combine, Compile, Compose, Conclude,
Criticize, Create, Decide, Deduct, Defend, Design, Discuss,
Determine, Disprove, Evaluate, Imagine, Improve, Influence,
Invent, Judge, Justify, Optimize, Plan, Predict, Prioritize, Prove,
Rate, Recommend, SolveEDISON 2017 Workshop

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Data Science Data Analytics (KAG1 – DSDA)
related courses
•

•
•

•

•
•

KA01.01 (DSDA/SMDA) Statistical methods, including Descriptive statistics,
exploratory data analysis (EDA) focused on discovering new features in the data,and confirmatory data analysis (CDA) dealing with validating formulated
hypotheses;
KA01.02 (DSDA/ML) Machine learning and related methods for information
search, image recognition, decision support, classification;
KA01.03 (DSDA/DM) Data mining is a particular data analysis technique that
focuses on modelling and knowledge discovery for predictive rather than purely
descriptive purposes;
KA01.04 (DSDA/TDM) Text analytics applies statistical, linguistic, and structural
techniques to extract and classify information from textual sources, a species of
unstructured data;
KA01.05 (DSDA/PA) Predictive analytics focuses on application of statisticalmodels for predictive forecasting or classification;
KA01.06 (DSDA/MODSIM) Computational modelling, simulation and optimisation.

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Data Science Engineering (KAG2-DSENG)
•
•
•
•
•
•
•

KA02.01 (DSENG/BDI) Big Data infrastructure and technologies, including
NOSQL databased, platforms for Big Data deployment and technologies forlarge-scale storage;
KA02.02 (DSENG/DSIAPP) Infrastructure and platforms for Data Science
applications, including typical frameworks such as Spark and Hadoop, dataprocessing models and consideration of common data inputs at scale;
KA02.03 (DSENG/CCT) Cloud Computing technologies for Big Data and Data
Analytics;
KA02.04 (DSENG/SEC) Data and Applications security, accountability,
certification, and compliance;
KA02.05 (DSENG/BDSE) Big Data systems organization and engineering,
including approached to big data analysis and common MapReduce algorithms;
KA02.06 (DSENG/DSAPPD) Data Science (Big Data) application design,
including languages for big data (Python, R), tools and models for data
presentation and visualization;
KA02.07 (DSENG/IS) Information Systems, to support data-driven decision
making, with focus on data warehouse and data centers.

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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

Other Knowledge Areas motivated by
RDA, 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

EDISON 2017 Workshop(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data, Open
Access, ORCID(15) Responsible data use•

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

Data Science Profession and Education42

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)(OBELearning
LearningModel)
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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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

DSDA0110
9
8

DSDA02
DSDA03
DSDA04

7

DSBPM03

Individual Education/Training Pathbased on Competence benchmarking

Candidate - Data Scientist

DSDA05

6
DSBPM02

DSDA06

5
4

DSBPM01

DSDM01

3

2
DSRM06• 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

1
0

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 DSP
Profiles.

DSENG01

DSENG06
DSENG05

DSENG02
DSENG03
DSENG04

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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
EDISON 2017 Workshop

Data Analyst/
Business Analyst

Data Scientist

Data Steward
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Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE
Data Science or Data Management Group/Department>> Reporting to CDO/CTO/CEO• Providing cross-organizational(Managing) Data Science Architect (1)services
Data Scientist (1), Data Analyst (1)Data Science Application programmer (2)Data Infrastructure/facilities administrator/operator: storage, cloud,computing (1)• Data stewards, curators, archivists (3-5)

•
•
•
•

Estimated: Group of 10-12 data specialists for research institution of 200300 research staff.

Growing role and demand for Data Stewards and data stewardshipEDISON 2017 Workshop

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Data Stewards – A rising new role in Data Science
ecosystem• Data Stewards as a key bridging role between Data
Scientists as (hard)core data experts and scientific domain
researchers (HLEG EOSC report)• Current definition of Data Steward (part of Data Science
Professional profiles)– Data Steward is a data handling and management professional
whose responsibilities include planning, implementing and managing(research) data input, storage, search, and presentation.
– Data Steward creates data model for domain specific data, support
and advice domain scientists/ researchers during the whole research
cycle and data management lifecycle.

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Research Data Management Model Curriculum –Part of the EDISON Data Literacy Training
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

Collaboration with the Research
Data Alliance (RDA) on developing
model curriculum on Research
Data Literacy:• Modular, Customisable,
Localised, Open Access• Supported by the network of
trainers via resource swap
board

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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Data Science education and training in Europe:
Roadmap recommendations
A. Policy recommendations for the EC and Member States• R1. Critical skills management and training• R2. Gender balance and multi-cultural environment• R3. Common data literacy• R4. Data driven technology and education divide.
• R5. European studies on demand and role of Data Science and Analytics skills• R6. Include the above actions in the European Digital Single Market Scoreboard
B. Recommendations to universities and professional training organisations• R7. EDSF adoption by universities• R8. Addressing Data Science professional and workplace skills in university curricula• R9. Multiple delivery form
• R10. Sharing experience, courses and instructors• R11. Supporting technical infrastructure for Data Science and data related education andprofessional training
C. Recommendations for Research (e) Infrastructures• R12. Critical skills management in Research (e) Infrastructures
D. Required support and contribution to the standardisation bodies• R13. The following are essential measures to achieve EDSF support existing and new standards.

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Next steps (1) – Further development andexploitation• EDSF Release 3 (October 2017)– Fully enumerated CF, BoK, MC, DSPP in machine readable format• Development of EDSF based tools:– Self assessment and Market monitoring• Certification Framework for at least two levels of Data Science
competences proficiency: Associates and Professionals– Data Science knowledge and competences for decision makers•

Toward EDSF and Data Science profession standardisation– ESCO (European Skills, Competences and Occupations) taxonomy– CEN TC428 (European std body) – Extending current eCFv3.0 and ICT profiles
towards e-CF4 with Data Science related competences– Work with the IEEE and ACM curriculum workshop to define Data Science Curriculumand extend current CCS2012 (Classification Computer Science 2012)

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Next steps (2) – Community Activities and Initiatives• Data Science Manifesto – Primarily focused on professional and ethicalissues in Data Science, new type of professional– Professional and ethical issues as a primary focus• Inter-universities initiative “Data Science for UN’s Sustainable
Development Goals” to focus in-curricula research (projects) on UN
priority goals• Build a wider network of early adopters, champions and ambassadors in
Europe and world wide
– EDISON team information and advice support will continue through out 2017-2018

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Questions and discussion
Links to EDISON Resources• 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

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Other related links•

Amsterdam School of Data Science––•

Research Data Alliance interest Group on Education and Training on Handling of
Research Data (IG-ETHRD)–•

http://www.bhef.com/publications/investing-americas-data-science-and-analytics-talentBurning Glass Technology, IBM, and BHEF report “The Quant Crunch: How thedemand for Data Science Skills is disrupting the job Market” (April 2017)––•https://ec.europa.eu/digital-single-market/en/news/final-results-european-data-market-study-measuringsize-and-trends-eu-data-economy

PwC and BHEF report “Investing in America’s data science and analytics talent: Thecase for action” (April 2017)–•https://www.rd-alliance.org/groups/education-and-training-handling-research-data.html

Final Report on European Data Market Study by IDC (Feb 2017)–•https://www.schoolofdatascience.amsterdam/
https://www.schoolofdatascience.amsterdam/education/

http://www.bhef.com/publications/quant-crunch-how-demand-data-science-skills-disrupting-job-market
https://public.dhe.ibm.com/common/ssi/ecm/im/en/iml14576usen/IML14576USEN.PDF

Millennials at work: Reshaping the workspace (2016)–

https://www.pwc.com/m1/en/services/consulting/documents/millennials-at-work.pdf

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Additional materials

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EDSF Recognition, Endorsement and Implementation•

DARE (Data Analytics Rising Employment) project by APEC (Asia Pacific
Economic Cooperation)– DARE project Advisory Council meeting 4-5 May 2017, Singapore•

PcW and BHEF Report “Investing in America’s data science andanalytics talent” April 2017
– Quotes EDSF and Amsterdam School of Data Science•

Dutch Ministry of Education recommended EDSF as a basis for university
curricula on Data Science– Workshop “Be Prepared for Big Data in the Cloud: Dutch Initiatives for
personalized medicine and health research & toward a national action programmefor data science training”, Amsterdam 28 June 2016
– Currently working with Dutch Gov on re-skilling IT/data workers for DSA
competences•

European Champion Universities network– 1st Conference (13-14 July, UK)– 2nd Conference (14-15 March, Madrid, Spain)– 3rd Conference 19-20 June 2017, Warsaw

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How to become a Data Scientist• Understand required Data Science and Analytics
competences and skills• Build your own learning path
– Assess your knowledge and start from basics– Statistics is foundation of Data (Science) Analytics• Develop statistical/probabilistic thinking• Difference between Data Science and statistics– Learn from others experience: read blogs, join forums andcommunities– Decide about academic degree, professional certificate, selfeducation/training, join local Meetup• Start applying for job– Remember variety of Data Scientist roles and profiles
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Becoming a Data Scientist by Swami Chandrasekaran(2013) http://nirvacana.com/thoughts/becoming-a-data-scientist/• Good andpractical advice
how to learn
Data Science,
step by step
• Follow the route

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