O'Reilly Learning is focusing on evolving learning experiences using Jupyter notebooks. Jupyter notebooks allow combining code, outputs, and explanations in a single document. O'Reilly is using Jupyter notebooks as a new authoring environment and is exploring features like computational narratives, code as a medium for teaching, and interactive online learning environments. The goal is to provide a better learning architecture and content workflow that leverages the capabilities of Jupyter notebooks.

1. Jupyter for Education:
Beyond Gutenberg and Erasmus
2015-07-25 • Seattle
Paco Nathan, @pacoid
O’Reilly Learning

2. Who We Are:

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. Not These People …

5. These People …

6. O’Reilly Learning
Objective:
Examine, make sense of, and organize
our various training products and learning
channels – for ourselves and our customers

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. 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. 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. 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. In the words of Michael Pollan,
“You are what you eat eats.”
michaelpollan.com/reviews/you-are-what-you-eat/
Learning Architecture

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. Background:

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. 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. 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. Interactive notebooks:
Sharing the code
Helen Shen
Nature (2014-11-05)
nature.com/news/interactive-notebooks-
sharing-the-code-1.16261
Background:

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. 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. Background:
Atlas is our content platform backed by Git,
for project collaboration among authors,
editors, et al.
https://atlas.oreilly.com/

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. Background:
Beta is our proof of concept:
https://beta.oreilly.com/learning

23. Tech Stack:
production presentation
Thebe:
player
Jupyter:
notebook
Docker:
container
web page:
interaction
Git:
versioning
Atlas:
publications
various
formats
authoring
cloud
infra

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. Great Examples:

26. Great Examples:
Seeing what Microsoft is doing with Jupyter
notebooks in Cortana Analytics – that’s brilliant
http://gallery.azureml.net/Experiment/3fe213e3ae6244c5ac84a73e1b451dc4

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. 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. 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. Learning:

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Looking Ahead:

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. 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. Thank You!