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Side Track at the EDF 2013 on Curriculum development: Towards a data science curriculum for professionals
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10.04.2023 1
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