1. Steve Brewer University of Southampton
Building the data science profession Digital Infrastructure for Research: DI4R2016, Krakow 30 September 2016
Steve Brewer
University of Southampton
EDISON – Education for Data Intensive Science to Open New science frontiers
Grant (INFRASUPP : CSA)

2. Outline Welcome and Introductions – Steve Brewer
Data Science as a Profession & EDISON project – Steve Brewer
Certification and Accreditation – Malgorzata Krakowian
The Data Lab, Edinburgh - Joshua Ryan-Saha
Text and Data Mining skill set - Freyja van den Boom
Pathways for impact and growth – Ruben Riestra
Discussion – mapping and comparing career paths and opportunities for Continuing Professional Development (CPD)
Wrap up and actions points - Steve
EDISON
EDISON Liaison Groups

3. Welcome and introductions
Building the data science profession:
Who are we?
Why are we doing this?
How can we do this?
What do we do next?
RDA7
Data Science Competences and BoK

4. Visionaries and Drivers: Seminal works, High level reports, Initiatives
The Fourth Paradigm: Data-Intensive Scientific Discovery. By Jim Gray, Microsoft, Edited by Tony Hey, et al.
Riding the wave: How Europe can gain from the rising tide of scientific data.
Final report of the High Level Expert Group on Scientific Data. October
The Data Harvest: How sharing research data can yield knowledge, jobs and growth.
An RDA Europe Report. December 2014
NIST Big Data Working Group (NBD-WG) (since 2013)
ISO/IEC JTC1 Big Data Study Group (SGBD) (2014)
EDISON
EDISON Liaison Groups

5. Background to EDISON: Riding the Wave
Data are becoming infrastructure themselves (Report “Riding the Wave”)
This requires large infrastructure resources to collect, store, process and archive heterogeneous multi-faceted and linked data.
Data centric/data driven infrastructure has to support different types of data, including text data, structured and unstructured data, relational and vector data, linked data.
Data appear in various contexts: large number strings from experiments or sensors, in software code, music, films, publications, digital art, web pages, social media, public and business statistics, and also orphan data.
We need data scientists with the knowledge and skill to work with existing and future data intensive infrastructure and tools.
EDISON
EDISON Liaison Groups

6. Emergent model on data harvesting and consumption
EDISON
EDISON Liaison Groups

7. EDISON: From Idea to Community Initiative to H2020 Project
1st RDA Plenary meeting – March 2013
1st BoF on Education and Skills Development in Data Intensive Science
Attended by 16 representatives from universities, libraries, e-Science, data centers, research coordination bodies
3rd RDA Plenary meeting – March 2014, Dublin
3rd BoF on Education and Skills Development in Data Intensive Science
EDISON (Education for Data Intensive Science to Open New science frontiers) Initiative announced
4th RDA Plenary meeting – September 2014, Amsterdam
IG Education and Training on Handling of Research Data (ETHRD) established
EDISON Workshop – 21 Sept 2014, Science Park Amsterdam
Decision to form a consortium and submit a proposal to IINFRASUPP call
8th RDA Plenary meeting – September 2016, Denver, USA
BoFs and IG meetings – now developing Certification and Accreditation proposal
EDISON
EDISON Liaison Groups

8. The Data Science supply chain
Potential
Data Scientists
Data Scientist “Producers”
- SUPPLY-
DS Employers
- DEMAND -
“Competitive product”: Skilled DS
Universities
Industry
Other Training Centres
Research
Organisations
In-house training centres
Research
Infrastructures
Self-made DS channels
Public
Administration
EDISON
EDISON Liaison Groups

9. EDISON actions and impact
Dramatically increase the number of data scientists
IMPACT
Create a Data Science profession
Services to education and training
Engage stakeholder communities
Data Science professional profiles
Support for accreditation and certification
Sustain platforms of communities of practice
Interact with demand and supply sides
Service for collaborating and sharing expertise and materials
Organise “champion” universities
Career path building and skills transferability
Design model curricula
Interact with Expert Liaison Groups
“Competence Framework” and “Body of Knowledge”
EDISON
EDISON Liaison Groups

10. Overview of EDISON project
Horizon-funded EU project
2-year project (started September 2015) with the purpose of:
Accelerating the creation of the Data Science profession
Within DG Connect: Communications Networks, Content & Technology
Directorate C - Digital Excellence & Science Infrastructure
C.1: eInfrastructure & Science Cloud
Focus on Research Infrastructures
Reaching out to wider context: economic landscape / flux
We need to grasp the problem and communicate the options
Then listen and revise our understanding and message
EDISON
EDISON Liaison Groups

11. Project timeline Project time line
Year 1 – ground work and foundations
EDSF
ELG formed and meetings
Champions established
Year 2 – Establishing network
Supporting Research Infrastructures
RDA Working Groups
Post EDISON
Community Portal
EDSF?
ELG?
Champions?
EDISON
EDISON Liaison Groups

12. EDISON Education and Training Champions
Renewed focus from existing formal Champions
Reinforced links with existing collaborators such as those in RDA groups
Reinforced at partner institutions eg. Southampton DS and also EDSA project
EDISON Womens’ group created
New Forest Milestone emerged as an output – see next slide
Motivation for a couple more meetings reaching out to other regions
Madrid – spring 2017
Warsaw – summer 2017
EDISON
EDISON Liaison Groups

13. Certification and Accreditation
Building the data science profession
30th September 2016
EDISON – Education for Data Intensive Science to Open New science frontiers
Grant (INFRASUPP : CSA)

14. Terminology Certification Accreditation
Formal recognition that an individual demonstrates proficiency in specific knowledge
Accreditation
Official recognition that a school, course, etc., has met standards established by external regulators
DI4R2016
Kraków 30th September 2016

15. Certification: Goal
To ensure that certifications designed by EDISON are able to support the formalised learning requirements of the Data Science profession.
To design a Data Science certification scheme that can be offered as a formal, recognised certification for individuals that are of value to the career objectives of the certification candidates.
DI4R2016
Kraków 30th September 2016

16. Certification: Interest
Data scientist
Setting themselves apart from the competition and be recognized
Employers
Trusted means to identify and recruit the best
Academies
Give their students an opportunities to be recognized and better start
Commercial organizations
Business opportunity to deliver certification training
DI4R2016
Kraków 30th September 2016

17. Certification: Plans
The EDISON project will plan to define certifications were each certification is:
aligned to EDISON Competence Framework, Body of Knowledge and Model Curriculum.
independent.
Independent certifications will allow the candidates
To choose a certificate at a given/chosen level while still covering all knowledge and competences.
Not have to take multiple certifications to demonstrate a professional level as there are no prerequisites between levels.
DI4R2016
Kraków 30th September 2016

18. Certification: Types Certifications for: Learners
wishing to demonstrate an understanding of the fundamental knowledge, terminology and activities of Data Science.
Experienced Data Scientists
who would like to prove expertise and improve their proficiency.
who would like to prove expertise in a given Data Science domain
DI4R2016
Kraków 30th September 2016

19. Accreditation and Certification: RDA 8th Plenary BoF
Aim: contribute to the sustainable development of the data science profession. Goal: deliver a report that presents a concise but representative picture of the various accreditation and certification schemes that exist around the world Outcome: Need to develop 9 months working group proposal centered on supporting the members of RDA to develop their own professional career paths around their own skills, interests and contexts.
DI4R2016
Kraków 30th September 2016

20. Accreditation and Certification: RDA 8th Plenary BoF
Discussed topics for potential actions:
Competences and skills
Identify core/essential data science competences and skills
Identify differences between DSP roles
Develop skills matrix to map core/key skills to parallel (currently) career paths relating to DS, eg. Librarian, Computer Scientist, Informatician, Statistician, Archivist.
Education
Education paths – degrees, summer schools, short-courses
Catalogue of courses currently available
Career
Job description – a useful working job description for a data science professional
Collect examples of real job ads
Reality
Capture user stories both for individuals and teams in real life
DI4R2016
Kraków 30th September 2016