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Jupyter for Education: Beyond Gutenberg and Erasmus

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PyData Seattle 2015 sponsored talk about O'Reilly Learning

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Jupyter for Education: Beyond Gutenberg and Erasmus

  1. 1. Jupyter for Education: 
 Beyond Gutenberg and Erasmus 2015-07-25 • Seattle Paco Nathan, @pacoid
 O’Reilly Learning
  2. 2. Who We Are:
  3. 3. O’Reilly Learning O’Reilly Learning is a new business unit focused on the (rapid) evolution of learning experiences for our audience, spanning across the range of product offerings at O'Reilly Media
  4. 4. Not These People …
  5. 5. These People …
  6. 6. O’Reilly Learning Objective: Examine, make sense of, and organize 
 our various training products and learning channels – for ourselves and our customers
  7. 7. Content flows through a maze of editorial process, production workflows, delivery channels, etc., from authors to audience… Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships O’Reilly Learning
  8. 8. Content flows through a maze of editorial process, production workflows, delivery channels, etc., from authors to audience… Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships O’Reilly Learning regarded by authors as a relatively “agile” process, 
 more than most – even so, 
 it needs much improvement
  9. 9. IMHO, here’s the crux of the issue, which impedes the industry in general: Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships O’Reilly Learning Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships
  10. 10. The Learning Architecture: Defining Development and Enabling Continuous Learning David Mallon, Dani Johnson Bersin (2014-05-06) http://www.bersin.com/Practice/Detail.aspx? docid=17435&mode=search&p=Learning-@-Development This report is designed to help leaders 
 and talent development and learning 
 professionals to take positive steps 
 toward understanding and implementing 
 learning architectures. Learning Architecture
  11. 11. In the words of Michael Pollan, “You are what you eat eats.” michaelpollan.com/reviews/you-are-what-you-eat/ Learning Architecture
  12. 12. We live within a community of makers, innovators, learners, implementers… Our objective initially is to provide a learning architecture within our company, leveraging it as a pattern that can help 
 our customers build their learning architectures, subsequently deployed 
 on behalf of their customers Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships Learning Architecture
  13. 13. Background:
  14. 14. On Demand Analytic and Learning Environments with Jupyter
 Kyle Kelley, Andrew Odewahn
 lambdaops.com/jupyter-environments-odsc2015/ Exploring a couple themes, in particular: • computational narratives - exploratory data analysis - software development/collaboration - API exploration - technical papers - reports/exec dashboards • code-as-media - Thebe project, etc. Background:
  15. 15. Personal experience in 2012-15 as 
 an independent author and instructor… Just Enough Math
 Paco Nathan
 O’Reilly Media (2014)
 http://justenoughmath.com Background:
  16. 16. Personal learnings, based on working 
 on this project with Kyle and Andrew… How to transit from the role of data scientist, software developer, engineering director – 
 into a role of author, teacher and vice versa Background:
  17. 17. Interactive notebooks: 
 Sharing the code Helen Shen Nature (2014-11-05) nature.com/news/interactive-notebooks- sharing-the-code-1.16261 Background:
  18. 18. Embracing Jupyter Notebooks at O'Reilly
 Andrew Odewahn, 2015-05-07 https://beta.oreilly.com/ideas/jupyter-at-oreilly “O'Reilly Media is using our Atlas platform to 
 make Jupyter Notebooks a first class authoring environment for our publishing program.” Jupyter, Thebe, Docker, etc. Background:
  19. 19. Embracing Jupyter Notebooks at O'Reilly Andrew Odewahn https://beta.oreilly.com/ideas/jupyter-at-oreilly “O'Reilly Media is using our Atlas platform to make Jupyter Notebooks a first class authoring environment for our publishing program.” Jupyter Background:
  20. 20. Background: Atlas is our content platform backed by Git, for project collaboration among authors, editors, et al. https://atlas.oreilly.com/
  21. 21. Background: Thebe (a moon of Jupiter) provides a layer atop Jupyter that is needed for publishing, white-labeled content, etc. https://github.com/oreillymedia/thebe
  22. 22. Background: Beta is our proof of concept: https://beta.oreilly.com/learning
  23. 23. Tech Stack: production presentation Thebe: player Jupyter: notebook Docker: container web page: interaction Git: versioning Atlas: publications various formats authoring cloud infra
  24. 24. Question: What’s the delta between our current 
 author workflow and this new world of 
 Jupyter + Docker +Thebe + cloud, etc.? production presentation Thebe: player Jupyter: notebook Docker: container web page: interaction Git: versioning Atlas: publications various formats authoring cloud infra
  25. 25. Great Examples:
  26. 26. Great Examples: Seeing what Microsoft is doing with Jupyter notebooks in Cortana Analytics – that’s brilliant http://gallery.azureml.net/Experiment/3fe213e3ae6244c5ac84a73e1b451dc4
  27. 27. Most definitely check out CodeNeuro, both online and the conf/hackathon… 
 for example: Jeremey Freeman, HHMI Janelia Farm
 http://notebooks.codeneuro.org/ Matthew Conlen, NY Data Company
 http://lightning-viz.org/ Olga Botvinnick, UCSD
 http://yeolab.github.io/flotilla/docs/gallery/ Great Examples:
  28. 28. Curating a list of examples, as a shared doc online, and some exemplars include… Lorena Barba, GWU
 http://lorenabarba.com/ Anita Raichand
 https://github.com/painterly/data_py Chris Fonnesbeck,Vanderbilt
 https://plot.ly/ipython-notebooks/computational-bayesian- analysis/ Donne Martin, NemetschekVectorworks
 https://bit.ly/data-notes Great Examples:
  29. 29. Compare/contrast Jupyter with other interesting notebooks impls… Databricks
 https://class01.cloud.databricks.com/#notebook/76328 R Markdown
 http://rmarkdown.rstudio.com/ Andy Petrella, Data Fellas
 https://github.com/andypetrella/spark-notebook IBM Knowledge Anyhow
 https://knowledgeanyhow.org Mathematica
 https://www.wolfram.com/learningcenter/tutorialcollection/ NotebooksAndDocuments/ Great Examples:
  30. 30. Learning:
  31. 31. A few features on the wish list for notebooks: • integrating video content • social aspects, collaboration • a spectrum of learning modes engaged • how to integrate classroom experience • expert mentoring • learning paths • remote learning environments, e.g., massive open online somethingorother Learning meets Data Science:
  32. 32. MOOCs, such as edX, provide excellent features for learning at scale, however: • costly for authors producing content • difficult to instrument • relatively low ROI (completion rates)
 Typesafe as a rare counterexample • lacking social context that reinforces learning … it’s difficult to staff a 
 small army of TAs who are needed What about MOOCs?
  33. 33. Peter Norvig @ Future Learning 2020 Summit, 2015-05-30: • search engines surface too many choices for available learning content • (“Thanks Google”) • need to get people to want to interact with the material – generally due to social context What about MOOCs?
  34. 34. Significant improvement in the notion 
 of “flipped” a.k.a. inverted classrooms For a good example, see: Caltech Offers Online Course with 
 Live Lectures in Machine Learning Yaser Abu-Mostafa (2012-03-30) http://www.caltech.edu/news/caltech-offers-online- course-live-lectures-machine-learning-4248 Learning meets Data Science:
  35. 35. There are other pedagogical issues to address, e.g., how to differentiate which content or mode will be most effective 
 for a learner’s needs and learning style Patterns of Code as Media
 Andrew Odewahn, O’Reilly Media
 odewahn.github.io/patterns-of-code-as-media/www/ introduction.html Learning meets Data Science:
  36. 36. total newbie good overview Do you have sufficient familiarity with the topic? utterly confused familiar territory Can you build on familiarity with a related topic? must get unstuck send pull request Do you have necessary proficiency in the topic? learner topic experience concise topic inter- disciplinary How many boundaries must you span to achieve structural literacy for this topic? want to for myself have to for my job What is your primary motivation to learn this topic? bleeding edge COBOL 2020 Where are you on the "diffusion of innovation" curve w.r.t. the topic? on- demand major event How high is the transaction cost for the experience delivered to you? "go read the code" full-team participation Does the learning experience immerse you within a diverse, supportive social context? Learning meets Data Science: BTW, did we mention the intense needs 
 for data analytics at scale and, in particular, dimensional reduction? :)
  37. 37. Education is more than lessons, exams, certifications, instructor evals, etc., … 
 though tooling often reduces it to that level Is it possible to measure the “distance” between a learner and the subject community? From Amateurs to Connoisseurs:
 Modeling the Evolution of User 
 Expertise through Online Reviews
 Julian McAuley, Jure Leskovec
 http://i.stanford.edu/~julian/pdfs/www13.pdf Learning meets Data Science:
  38. 38. Learning Curves are forever – In some sense, this is essence 
 of Data Science: 
 How well do you learn? In my experience, much of the risk encountered in managing a Data Science team is about budgeting for learning curve Learning meets Data Science:
  39. 39. ThrowYour Life a Curve
 Whitney Johnson blogs.hbr.org/johnson/2012/09/ throw-your-life-a-curve.html For example, notions of continuous learning: • deconstruction of the cognitive bias One Size Fits All • “makes a compelling case for personal disruption” • “plan your career around learning curves” • hire people who learn/re-learn efficiently Learning meets Data Science:
  40. 40. So who (or where) are the experts in this graph?! Diffusion of Innovation
 Everett M. Rogers (1962)
 http://sphweb.bumc.bu.edu/otlt/MPH-Modules/SB/ SB721-Models/SB721-Models4.html Learning meets Data Science:
  41. 41. Looking Ahead:
  42. 42. Moving beyond books, beyond Kindle, beyond MOOCs … Moving forward, important aspects include: learning paths, continuous learning, inverted classroom, computational thinking, learner segmentation, etc. Also, it’s not so much about how an individual learns, rather our focus should include social context, e.g., learning within 
 a team Looking Ahead:
  43. 43. Moving beyond books, beyond Kindle, beyond MOOCs Moving forward, important aspects include: learning paths classroom segmentation Also, it’s not so much about how an individual learns, rather our focus should include a team Looking Ahead: we’re eager to work with great new notebook authors!!
 #pioneers
  44. 44. Thank You!

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