Side Track at the EDF 2013 on Curriculum development: Towards a data science curriculum for professionals

1. BIG Public Private Forum
Data Science Curriculum
for Professionals
John Domingue, KMi, The Open University &
STI International
Dublin, April 2013
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2. INFLUENCES
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3. Euclid
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4. BIG Project
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5. Teaching semantic
programming since late 70s
• Developed
own
languages, a
nd
environments
• 500 – 1000
students per
year
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6. ISSUES AND LESSONS
LEARNT
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7. Crowd-sourced real-time
radiation monitoring

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10. Who to Train?
Diversity; citizen engagement; empowerment;
avoiding disenfranchisement; understanding privacy issues
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11. Constructivist Approach
• Students create their own programs
• Non-computer scientists are able to do this
with the right hand-holding
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12. Coherent Easy-to-use
environments
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13. Clear Virtual Machine
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14. Cradle-to-Grave
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15. Differences today
• Constructivist, immersive study easier since
necessary computational resources and test
data easily available
• eLearning approaches (MOOC-style or not)
can fit with Big Data infrastructures
– tutor-student, peer-to-peer, historical
collaborations all possible
• Big Data can also support learning
– Learning analytics allow tuning of teaching
– Linked Data/Open Data enable discovery and use
of available Open Educational Resources
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16. Final Thought
• Imagine an open online Data Science Lab
– Repository for available learning materials
– Educationally significant datasets
– Computational resources
– Programming tools
– Learning dialogues between educationalists,
tutors and students
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