Becoming a Data Scientist, Teaching Data
Scientists
How to develop the Data Science and Analytics relatedcompetences and professional skills

Yuri Demchenko, University of
Amsterdam
EDISON Project and Initiative

Meetup 19 October 2018, Kiev
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

Outline
Part 1• Background: Data driven research and demand for new skills– Foundation, recent reports, studies and facts

Part 2• 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
Pat 3• Use of EDSF and Example curricula– Competences assessment– Building Data Science team•Discussion

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Yuri Demchenko,
Senior Researcher, Lecturer, UvA•

Graduated and PhD from National Technical University of Ukraine “Kiev
Polytechnic Institute”– University of Amsterdam – since 2003•

Research areas– Big Data Infrastructure and Data Science platforms– Cloud architecture, cloud automation and DevOps– Cloud security and compliance•

Teaching courses (on campus and online)
–
–
–
–

•

Big Data Infrastructure and Technologies
Cloud powered Software Engineering and DevOps
Data Science Foundations, Professional Issues in Data Science
Security Engineering

Recent projects––––

EDISON: Building the Data Science Profession for Europe
MATES: Digitalisation of the European Blue Economy
CYCLONE: Multi-cloud automation platform for cloud based applications
GEANT4 Research: Cloud aware networking infrastructure provisioning on-demand

BDIT for DA 2018

Big Data and Cloud based Infrastructure3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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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)
https://ec.europa.eu/research/openscienc
e/pdf/realising_the_european_open_scie
nce_cloud_2016.pdf

Kiev 2018https://rd-alliance.org/dataharvest-report-sharing-dataknowledge-jobs-and-growth.html

Emergence of Cognitive Technologies(IBM Watson, Cortana and others)
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The Fourth Paradigm of Scientific Research
1.
2.

Theory, hypothesis and logical reasoning
Observation or Experiment, e.g.
––

3.

Simulation of theory or model–4.

Newton observed apples falling to design his
theory of mechanics
Gallileo Galilei made experiments with falling objects
from the Pisa leaning tower
Digital simulation can prove theory or model

Data-driven Scientific Discovery (aka Data Science)– More data beat hypothesized theory– e-Science as computing and Information Technologies empowered science

5. Computer-human - driven science?
––
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Machine discovers new patterns and formulates hypothesis in one or
multiples knowledge spaces
Scientist validates and designs additional texts or experiments
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HLEG EOSC Report Essentials – Core Data
Experts [ref]•Core Data Experts is a new class of colleagues with core scientificprofessional competencies and the communication skills to fill thegap between the two cultures.
– Core data experts are neither computer savvy research scientists nor
are they hard-core 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.
[ref] https://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud_2016.pdf

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Gartner Technology Hypercycle (August 2017)

Big Data and Cloud Computing:
In a maturity stage –already commodity services[ref] https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
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Gartner Technology Hypercycle (August 2017)
We are in post Big Data and post Cloud Computing stage
Big Data and Cloud Computing:
In a maturity stage –already commodity services[ref] https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
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Cloud Computing and Use Cases

9International and EU studies on data-driven skills

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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 EDISON

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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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IBM&BGT: DSA Jobs Time to Fill and Salary(2016-2017)•

On average, DSA jobs in Professional Services remain open for 53 days, eightdays longer than the overall DSA average. (IBM, BGT 2017 Study)

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Closer look at Data related Jobs and Salaries (2016)

Source: The Job Market for Data Professionals, by Robert R Downs, SciDataCon2016
http://www.scidatacon.org/2016/sessions/98/poster/51/
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OECD and UN on Digital Economy and Data Literacy
OECD (Organisation for Economic Coopration and Development)• Demand for new type of “dynamic self-re-skilling workforce”• Continuous learning and professional development to become ashared responsibility of workers and organisations[ref] Skills for a Digital World, OECD, 25-May-2016
http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/ICCP/IIS(2015)10/FINAL&docLanguage=EnUN• Data Revolution Report "A WORLD THAT COUNTS"
Presented to Secretary-General (2014)
http://www.undatarevolution.org/report/• Data Literacy is defined as key for digital revolution
and Industry 4.0• Data literacy = critically analyse data collected anddata visualised
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PwC study: Millennials at work (2016) - 1
Confirmed results of previous studies:•

Loyalty-lite to company– The power of employer brands and the waning
importance of corporate responsibility••

A time of compromise: benefit from individual
package negotiation
Development and work/life balance are more
important than position or salary– Work/life balance and diversity promises are
not being kept

•
•
•

•

Financial reward is secondary but cash bonuses are valued
A techno generation avoiding face time and prefer network
communication
Moving up the ladder faster expectation but often not confirmedby hard work required
Generational communication but not without tensions

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PwC study: Millennials at work (2016) - 2•

What organisation is an attractive employer?
–
–
–

•

Factors most influenced decision to accept yourcurrent job?
–
–
–

•

Opportunities for career progression
Competitive wages/other financial incentives
Excellent training/development programmes

The opportunity for personal development
The reputation of the organisation
The role itself

Which three benefits would you most value from an
employer?
–––

Training and development
Flexible working hours
Cash bonuses•
Business leaders and HR need to work together •to:•• Understand this generation•• Get the ‘deal’ right•• Help millennials grow

What can employers do?

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Feedback, feedback and more feedback
Set them free
Encourage learning
Allow faster advancement
Expect millennials to go

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Data Driven Victories and Failures - Politics
Very high impact events and facts•

US Election 2012 – Obama’s campaign and rise of Big Data analytics– Micro-targeting and Social Networks analysis•

Brexit 2016
– “Data driven Brexit” – first serious ring for right use of Data Science technologies•

US Election 2016
– Clinton’s campaign – “Data driven” but using only upper layer of Social Network (SN)web
– Trump’s campaign – Targeting bottom SN web and “forgotten people not to beforgotten”• Matt Oczkowski, leader on Trump’s campaign: “If he was going to win this election, it was going
to be because of a Brexit style mentality and a different demographic trend than other people
were seeing.”•

France election 2017
– Awakening

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Data-Driven Brexit: A Wakeup Call for Analysts
By Barry Devlin, June 28, 2016
Book: In Data We Trust:
How Customer Data is
Revolutionising Our
Economy (Aug 2012)• A strategy for
tomorrow's data world

Data-Driven Brexit: A
Wakeup Call for Analysts
By Barry Devlin, June 28, 2016••

Article “In Data we trust” by T.Edsall in The
New York Times
Multimillion-dollar contract for data
management and collection services
awarded May 1, 2013 to Liberty Work (for
Republicans) to build advanced list ofvotersKiev 2018• There are significant lessons for believers in data-driven business tolearn from how data was and wasn’t used for decision making before,
during, and after the Brexit vote.
• Human attitude -- including emotion, intuition, and social empathy -and motivation are at the heart of decision making and the action that
follows• Information will only be accepted when it conforms to preconceived
notions. Expertise is not sufficient and, in extremis, will be dismissedwith ridicule.
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US elections 2016, other facts and Data Analytics• On-going scandal with Cambridge Analytica

This area intentionally left blank• Growing importance of ethical factor– Education is essential to tame new element/dimension of our life –Data– Existing ICT Manifesto for ICT Professionals• Increasing impact of EU GDPR (General Data Protection
Regulation) to be in force from 25 May 2016
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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
Importance of workplace or professional attitude skills (not covered in academic curricula)Technology focus changes every 3-4 years–

Study: 50% of academic curricula are outdated at the time of graduation•

Growing influence of Big5 technology companies: Amazon, Microsoft, Google,
Facebook, Apple•

Lack of necessary skills leads to underperforming projects and organisations andloose of competitiveness–•

Need to change the whole skills management paradigm––•

Challenge: Policy and decision makers still don’t include planning human factor (competences andskills) as a part of the technology strategy

Dynamic (self-) re-skilling: Continuous professional development and shared responsibility
between employer and employee
Professional and workplace skills and career management as a part of professional orientation

Millennials factor and changing nature of workforce

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Data Scientist definitions: From Math to Hacking•

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Strongly depend on the
background of the Data
Scientist24

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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EDISON Products for Data Science Skills
Management and Curriculum Design•

EDISON Data Science Framework (EDSF)– Compliant with EU standards on competences and professional
occupations e-CFv3.0, ESCO
– Customisable courses design for targeted education and training•

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•

Cooperation with International professional organisations
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EDISON Data Science Framework (EDSF)

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

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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
Research Methods and ProjectManagement– Business Process Management (biz)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-design30Data Science Competences Groups – Business
Data Science Competences
include 5 groups
•
•
•
•
•

Data Science Analytics
Data Science Engineering
Domain Knowledge and Expertise
Data Management
Research Methods and ProjectManagement– Business Process Management (biz)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-design31

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

DSDA03Pull together data fromdiff sources …
4 DSDA04
Use diff perform
techniques
5 DSDA05
Develop analytics applic
6 DSDA06
Visualise
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2018results ofanalysis, dashboards
3

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

DSENG03
DSDM03Develop and prototype data
Collect integrate data
analytics applications
DSENG04
DSDM04
DSRMP04
Develop, deploy operate Big Data Maintain repository
Translate strategies into actions
storage
DSENG05
DSDM05
DSRMP05
Apply security mechanisms
Visualise cmplx data
Contribute to organis goals
DSENG06
DSRM06
DSRMP06
Design, build, operate SQL and
DevelopandmanageDevelop and guide data driven
Data Science Profession and Education
NoSQL
prolicies
projects

DSBPM03
Provides support services to other

DSBPM04
Analyse data for marketing
DSBPM05
Analyse optimise customer relatio
DSBPM06
Analyse data for marketing 32

Identified Data Science Skills/Experience Groups
Skills Type A – Based on knowledge acquired•Group 1: 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 or Business Process Management
Application/subject domain related (research or business)

Group 2: Mathematics and statistics–

Mathematics and Statistics and others

Skills Type B – Base on practical or workplace experience•

Group 3: Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Group 4: Data analytics programming languages and IDE–•

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
General and specialized development platforms for data analysis and statistics

Group 5: Soft skills and Workplace skills––

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Data Science professional skills: Thinking and Acting like Data Scientist
21st Century Skills: Personal, inter-personal communication, team work, professional network
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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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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, initiative,
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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Maritime Industry Digital Transformationand Skills Strategy – Toward Industry 4.0

OCCUPATIONAL
PROFILESCURRICULUMDESIGNEDUCATION& TRAINING

Digital Transformation• Digitalisation and IoT
Big Data• Intelligent Information• Data Management• Digital Assets Manage• Data Driven Optimisation
Additive• Agile Continuous
Manufacturing
Improvement• Customer Experience• People and skills

KNOWLEDGE
TRANSFER

SystemIntegration

Internet ofThingsLONG-TERMSKILLS STRATEGY

OCEAN
LITERACY

Digital Competences/Skills• Automation, robotics,
electrical vehicles• Information and data
literacy• Communication andcollaboration• Digital content creation,
safety• Problem solving andcritical thinking

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– Candidates’ CV assessment
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Data Science Professions Family

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

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CF-DS and Data Science Professional Profiles

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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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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

Kiev 2018(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)

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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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Outcome Based Educations and Training Model

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

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DSP04 – Data Scientist MC structure

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DSP10 – Data Steward MC structure

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DSP04 Data Scientist – Required practical skills
and Hands-on labs
Data Science curriculum should include the following elements
to achieve necessary skills Type B:• Python (or R) and corresponding data analytics libraries• NoSQL and SQL Databases (Hbase, MongoDB, Cassandra, Redis,
Accumulo, MS SQL, My SQL, PostgreSQL, etc.)• Big Data Analytics platforms (Hadoop, Spark, Data Lakes, others)• Real time and streaming analytics systems (Flume, Kafka, Storm)• Kaggle competition, resources and community platform, including richdata sets, forum and computing resources• Visualisation software (D3.js, Processing, Tableau, Julia, Raphael, etc.)• Web API management and web scrapping• Git versioning system as a general platform for software development• Development Frameworks: Python, Java or C/C++, AJAX (AsynchronousJavascript and XML), D3.js (Data-Driven Documents), jQuery, others• Cloud based Big Data and data analytics platforms and services,
including large scale storage systems– Essential for workplace adjustment
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Individual Competences Benchmarking
Individual Education/Training Pathbased on Competence benchmarking• 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•

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.

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Building a Data Science Team

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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 stewardship
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Data Stewardship in Research and FAIR
Principles•

FAIR Initiative by Dutch Techcentre for Life Science(DTLS) – Prof. Barend Mons
–
–

•
•

•

Supported by Germany, France, Spain, UK, USA
Part of Horizon 2020 Programme

FAIR Principles for research data:
Findable – Accessible – Interoperable - Reusable
Data Stewards as a key bridging role between Data
Scientists as (hard)core data experts and scientificdomain researchers (HLEG EOSC report)
Current definition of the 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 thewhole research cycle and data management lifecycle.

IPL Webinar 2018

Data Governance and Data Management

HLEG report on
European OpenScience Cloud(October 2016)51

Data Governance Organisation Parts and Roles• Separation or
governanceresponsibilities• Multi-layer
• CDO
• CIO
• Councils• Data Stewardship
DAMA DataManagement Bodyof Knowledge(DMBOK)[ref] DAMA-DMBOK Data Management Body of Knowledge, 2nd Edition, 2017
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Discussion: How to become a Data Scientist• A lot of information and different paths• There are essential knowledge and competences– However most of them require strong background inmathematics, statistics, programming, infrastructure, etc.

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Discussion: 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– Understand what company is actually looking forKiev 2018

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Data Science and Data Mining• Data mining (knowledge discovery from data)– Extraction of interesting (non-trivial, implicit, previously unknown
and potentially useful) patterns or knowledge from huge amountof data• Alternative names– Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, data
dredging, information harvesting, business intelligence, etc.

• Watch out: Is everything “data mining”?
– Simple search and query processing– (Deductive) expert systems

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CRISP DM process: Processes and Data Lifecycle

Business
Understanding

Data
Understanding

Data Preparation
Deployment

Data
Modeling

Evaluation

Cross Industry Standard Process for Data Mining (CRISP-DM)
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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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Online Educational and training resources• LinkedIn Education• Microsoft Virtual Academy (MVA)• (IBM – in transition)• DataCamp• Coursera, Udacity• Certification and training PMI, DAMA, IIBA

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Open Data and Educational Datasets• Amazon Web Services (AWS)• Google• Microsoft Azure• Kaggle• KDNuggets• Emerging - https://www.datasciencepro.eu/

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Questions and discussion

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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

This work is licensed under the Creative Commons Attribution 4.0 International License.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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Additional materials

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Types of Data Mining (branch of Data Analysis)

Regression

Classification

Feature Selection

Clustering

Data Mining

Text Mining

Association

Anomaly detection, Time Series

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Data Mining: Confluence of Multiple Disciplines

MachineLearning

Applications

Algorithm

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Pattern
Recognition

Data Mining

Database
Technology

Data Science Profession and Education

Statistics

Visualization

Cloud Computingand HPC

64

CRISP DM process: Processes and Data Lifecycle

Business
Understanding

Data
Understanding

Data Preparation
Deployment

Data
Modeling

Evaluation

Cross Industry Standard Process for Data Mining (CRISP-DM)
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Process of Data Analysis
Business
Understanding

Data Understanding

1. Prior Knowledge

Prepare Data

2. Preparation

Training Data

Building Model using Algorithms

3. Modeling

Test Data

Applying Model andperformance evaluation

4. Application

Deployment

5. Knowledge
Knowledge and Actions

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