"Data Science" panel intro slides at Digital Research 2013, St Anne's, Oxford, September 2013 hosted by e-Research South and Oxford e-Research Centre - see http://digital-research.oerc.ox.ac.uk/

1. Data Science Panel – Challenges and Curriculum
Chair
Dave De Roure
Panelists
Yuri Kalnishkan (Royal Holloway)
Jeremy Frey (University of Southampton)
Sarah Quinton (Oxford Brookes)
Eric Meyer (Oxford Internet Institute)

2. ChristineBorgman

3. What is e-Research?
• Research in every domain is increasingly data- and
computationally-intensive, carried out collaboratively over
distributed infrastructures
• e-Research is the continuous technological and
methodological innovation in digital methods to achieve
new research outcomes – using new forms of data and
emerging infrastructural capabilities
• The Oxford e-Research Centre is a digital methods
incubator with around 30 early-adopter researchers
working in and across all disciplines

4. More people
Moremachines
Big Data and
Computation
Conventional
Computation
Crowd
& Cloud
Social
Networking
Cyberinfrastructure
e-infrastructure
Science 2.0
Citizen Science
e-Research
David De Roure

5. Economic and Social Research Council
Shaping Society
• Digital Social Research Program
• Administrative Data Research
Network
• Business Datasafe
• Big Data Network
• Centre for International Social
Media Analytics

6. F i r s t
BioEssays,,26(1):99–105,January2004
http://research.microsoft.com/en-us/collaboration/fourthparadigm/

7. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT.

The Problem
signal
understanding
Ich Fujinaga

8. The challenge is to foster the co-constituted socio-technical system
on the right i.e. a computationally-enabled sense-making network
of expertise, data, models and narratives.
Big data elephant versus sense-making network?
Iain Buchan

9. data
method

11. 1. What is your own "story" is as a data scientist?
2. What are the three most important skills we need
to teach data scientists and why?
3. How will these skills be different in 10 years?
4. Are we providing adequate training to meet needs
from your point of view?
5. Are we teaching people how to deal with data but
not how to really understand it?
6. Is there sufficient critical thinking in data science?
Questions