The document discusses a data science curriculum for professionals presented by John Domingue in Dublin in April 2013. It covers influences on the curriculum such as Euclid and previous semantic programming teaching. Issues addressed include who to train, using a constructivist approach, providing coherent and easy to use environments, and differences in online learning today compared to past approaches. The presentation concludes by envisioning an open online data science lab that could support learning materials, datasets, computing resources, programming tools, and educational dialogues.

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,
and
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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