BIG Public Private Forum        Data Science Curriculum           for Professionals                John Domingue, KMi, The...
INFLUENCES18.04.2013      2
Euclid18.04.2013            3
BIG Project18.04.2013                 4
Teaching semanticprogramming since late 70s• Developed  own  languages,  and  environments• 500 – 1000  students per  year...
ISSUES AND LESSONS   LEARNT18.04.2013              6
Crowd-sourced real-time  radiation monitoring
18.04.2013   8
18.04.2013   9
Who to Train?Diversity; citizen engagement; empowerment;avoiding disenfranchisement; understanding privacy issues   18.04....
Constructivist Approach• Students create their own programs• Non-computer scientists are able to do this  with the right h...
Coherent Easy-to-use        environments18.04.2013                  12
Clear Virtual Machine18.04.2013                 13
Cradle-to-Grave18.04.2013                     14
Differences today• Constructivist, immersive study easier since  necessary computational resources and test  data easily a...
Final Thought• Imagine an open online Data Science Lab     – Repository for available learning materials     – Educational...
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EDF2013: Data Science Curriculum: John Domingue: Data Science Curriculum for Professionals

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Data Science Curriculum: John Domingue, KMi, The Open University & STI International, at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Data Science Curriculum for Professionals

Published in: Education, Technology

EDF2013: Data Science Curriculum: John Domingue: Data Science Curriculum for Professionals

  1. 1. BIG Public Private Forum Data Science Curriculum for Professionals John Domingue, KMi, The Open University & STI International Dublin, April 2013 18.04.2013 1
  2. 2. INFLUENCES18.04.2013 2
  3. 3. Euclid18.04.2013 3
  4. 4. BIG Project18.04.2013 4
  5. 5. Teaching semanticprogramming since late 70s• Developed own languages, and environments• 500 – 1000 students per year18.04.2013 5
  6. 6. ISSUES AND LESSONS LEARNT18.04.2013 6
  7. 7. Crowd-sourced real-time radiation monitoring
  8. 8. 18.04.2013 8
  9. 9. 18.04.2013 9
  10. 10. Who to Train?Diversity; citizen engagement; empowerment;avoiding disenfranchisement; understanding privacy issues 18.04.2013 10
  11. 11. Constructivist Approach• Students create their own programs• Non-computer scientists are able to do this with the right hand-holding 18.04.2013 11
  12. 12. Coherent Easy-to-use environments18.04.2013 12
  13. 13. Clear Virtual Machine18.04.2013 13
  14. 14. Cradle-to-Grave18.04.2013 14
  15. 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 Resources18.04.2013 15
  16. 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 students18.04.2013 16

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