Data Science Curriculum for Professionals

1,011 views

Published on

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

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,011
On SlideShare
0
From Embeds
0
Number of Embeds
157
Actions
Shares
0
Downloads
18
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

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 10.04.2013 1
  2. 2. INFLUENCES10.04.2013 2
  3. 3. Euclid10.04.2013 3
  4. 4. BIG Project10.04.2013 4
  5. 5. Teaching semanticprogramming since late 70s• Developed own languages, a nd environments• 500 – 1000 students per year10.04.2013 5
  6. 6. ISSUES AND LESSONS LEARNT10.04.2013 6
  7. 7. Crowd-sourced real-time radiation monitoring
  8. 8. 10.04.2013 8
  9. 9. 10.04.2013 9
  10. 10. Who to Train?Diversity; citizen engagement; empowerment;avoiding disenfranchisement; understanding privacy issues 10.04.2013 10
  11. 11. Constructivist Approach• Students create their own programs• Non-computer scientists are able to do this with the right hand-holding 10.04.2013 11
  12. 12. Coherent Easy-to-use environments10.04.2013 12
  13. 13. Clear Virtual Machine10.04.2013 13
  14. 14. Cradle-to-Grave10.04.2013 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 Resources10.04.2013 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 students10.04.2013 16

×