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Data Science Curriculum for Professionals

John Domingue, KMi, The Open University & STI International

Dublin, April 2013

BIG Public Private Forum

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INFLUENCES

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Euclid

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BIG Project

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Teaching semantic programming since late 70s

• Developed own languages, and environments

• 500 – 1000 students per year

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ISSUES AND LESSONS LEARNT

Crowd-sourced real-time radiation monitoring

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Who to Train?

Diversity; citizen engagement; empowerment;avoiding disenfranchisement; understanding privacy issues

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Constructivist Approach

• Students create their own programs• Non-computer scientists are able to do

this with the right hand-holding

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Coherent Easy-to-use environments

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Clear Virtual Machine

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Cradle-to-Grave

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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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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