SlideShare ist ein Scribd-Unternehmen logo
1 von 62
Downloaden Sie, um offline zu lesen
2015-08-24 • San Jose
Paco Nathan, @pacoid

Director, O’Reilly Learning
Data Science Reinvents Learning?
Beyond Gutenberg and Erasmus
meetup.com/SF-Bay-ACM/events/221693508/
2
Some Background…
• O’Reilly Learning: you may only hear about us in 

a few instances, if we do our job well; ACM is a great
forum for this discussion
• prior: built-out the community evangelism and training
program for Apache Spark at Databricks
• prior: led Data teams for several years, working on 

large-scale ML apps in industry, including: one of the
largest Hadoop instances running in AWS (2008); 

one of the first 100% AWS system architectures (2006)
• …
• ancient prior: Stanford CSD teaching fellowship (1984-86,
Alice Supton, Stuart Reges) peer-teaching CS course
which later became Residential Computing
WWSVD?
4
Intro
Quite candidly, the one common catch phrase 

in SiliconValley that I find most terrifying:
“It’s like Uber, for ___”
5
Intro
Ostensibly that leads to a question, how might 

an “Uber for Education” look?
6
Intro
Ostensibly that leads to a question, how might 

an “Uber for Education” look?
a) Similar to Cthulhu, we might regret actually seeing that
7
Intro
Ostensibly that leads to a question, how might 

an “Uber for Education” look?
a) Similar to Cthulhu, we might regret actually seeing that
8
Intro
Ostensibly that leads to a question, how might 

an “Uber for Education” look?
a) Similar to Cthulhu, we might regret actually seeing that
b) Would we really need that anywho?
9
Intro
Ostensibly that leads to a question, how might 

an “Uber for Education” look?
a) Similar to Cthulhu, we might regret actually seeing that
b) Would we really need that anywho?
c) Uber itself might not take that approach …
10
Intro
Ostensibly that leads to a question, how might 

an “Uber for Education” look?
a) Similar to Cthulhu, we might regret actually seeing that
b) Would we really need that anywho?
c) Uber itself might not take that approach …
Perhaps “Uber for Learning” might be somewhat

more apt?
In any case, what comes after Books,
Kindle, MOOCs?
11
Some Definitions…
“Learning”
ergo…
“Education”
ergo…
“School”
“Learning”
ergo…
“Education”
ergo…
“School”
X
12
Some Definitions…
Schools are great to have…
If you need a school, pick a 

good one and go
To be clear, we’re not a school
13
Some Definitions…
Even the best schools these days question

what they will become in 5-10 years
Not-so-best schools are perhaps questioning 

much more than that
14
Some Definitions…
Oh BTW, too many (funded) teams seem to 

have this mediocre idea for “education”:
1. assessment: collect test scores ➜
2. define “quantified student” ➜
3. reuse online marketing funnel ad-tech ➜
4. invoke agile coding teams ➜
5. ship mobile/cloud-based SaaS platform ➜
6. ...
7. profit
Oh BTW, too many (funded) teams seem to
have this mediocre idea for “education”
1. assessment: collect test scores
2. define “quantified student”
3. reuse online marketing funnel ad-tech
4. invoke agile coding
5. ship a mobile/cloud-based SaaS platform
6. ...
7. profit
15
Some Definitions…
LMS
K-12 not so much, except perhaps in the
case of Safari for Schools
undergrad textbooks?
graduate textbooks, conferences?
professional focus of our audience
16
Some Definitions…
17
• vocational: 

making a career move
• aspirational: 

improvement within a career path
• proficiency: 

has a specific pain-point, needs to resolve it
• familiarity: 

wants to join in a team dialog about a topic, 

e.g., conversational programmer
Learner Personas for professional category
What about MOOCs?
19
What about MOOCs?
Massive Open Online Courses – 

seven year trend, beginning with:
Connectivism and Connective Knowledge

George Siemens, Stephen Downes

University of PEI (2008)

http://cck11.mooc.ca/
20
What about MOOCs?
21
What about MOOCs?
Anthony Joseph

UC Berkeley
early Jun 2015
edx.org/course/uc-berkeleyx/uc-
berkeleyx-cs100-1x-
introduction-big-6181
Ameet Talwalkar

UCLA
late Jun 2015
edx.org/course/uc-berkeleyx/
uc-berkeleyx-cs190-1x-
scalable-machine-6066
22
What about MOOCs?
Pros:
• cost-effective to reach a large audience
• popular with students
• ¿ addresses “train the trainers” bottleneck ?
Cons:
• expensive to produce and curate
• most students are sampling
• low completion rates
• somewhat chaotic
• lecture fatigue
• ¿ reinforces advantage of the elites ?
23
What about MOOCs?
Online education: MOOCs taken by educated few

Ezekiel Emanuel, Nature 503, 342 (2013-11-21)
• 80% students already have an advanced degree
• 80% come from the richest 6% of the population
Michael Shanks @Stanford: retrenchment around traditional
disciplines will make disparities even more pronounced
An Early Report Card on Massive Open Online Courses

Geoffrey Fowler, WSJ (2013-10-08)
Amherst, Duke, etc., have rejected edX
see: Open edX Universities Symposium @GWU, 2015-11-11
24
• search engines surface too many choices 

among the available learning content
• we must get people wanting to interact with
the material – generally due to social context
• academe strives to decontextualize, which 

is the opposite of learning in context
• how do we recognize that learning has
occurred?
• what is the learning promise?
What about MOOCs?
Examples for Consideration
26
Introduction to Robotics
Peter Corke @QUT
https://moocs.qut.edu.au/learn/introduction-to-
robotics-august-2015
• effective use of peer review for scaling
• worked well reaching into Africa, India
Peer Review
27
EffectiveThinkingThrough Mathematics
Michael Starbird @UT/Austin
https://www.edx.org/course/effective-thinking-
through-mathematics-utaustinx-ut-9-01x
• getting students to articulate their
epiphany moments is more interesting 

than other results – Donna Kidwell
Epiphany Moments
28
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
• significant improvement through the use
of “flipped” a.k.a. inverted classrooms
Inverted Classrooms
29
Scalable Learning

David Black-Schaffer @Uppsala

Sverker Janson @KTH SICS
https://www.scalable-learning.com/
• active learning: Flipped Classroom and Just-in-timeTeaching
• exams built directly into specific diagrams within videos
• metrics for where in video+code that students get stuck
• instructor can customize subsequent classroom discussions 

(active teaching phase) based on stuck/unstuck metrics
Inverted Classrooms
30
How to Flip a Class 

CLT @UT/Austin

http://ctl.utexas.edu/teaching/flipping-a-class/how
1. identify where the flipped classroom model makes 

the most sense for your course
2. spend class time engaging students in application
activities with feedback
3. clarify connections between inside and outside 

of class learning
4. adapt your materials for students to acquire course
content in preparation of class
5. extend learning beyond class through individual 

and collaborative practice
Inverted Classrooms
31
Learning programming at scale
Philip Guo 

O’Reilly Radar (2015-08-13)
http://radar.oreilly.com/2015/08/learning-
programming-at-scale.html
• PythonTutor
• Codechella
Tutors could keep an eye on around 

50 learners during a 30-minute session, 

start 12 chat conversations, and 

concurrently help 3 learners at once
Collaborative Learning
32
Data-driven Education and the Quantified Student
Lorena Barba @GWU
PyData Seattle 2015
https://youtu.be/2YIZ2SY9mW4
• keynote talk: abstract, slides
• homepage
If you study just one link in this entire talk…
Project Jupyter
34
If by some bizarre chance you haven’t used 

it already, go to https://jupyter.org/
• 50+ different language kernels
• new funding 2015-07
• UC Berkeley, Cal Poly
• nbgrader autograder by Jess Hamrick
• jupyterhub multi-user server
• curating a list of examples
• repeatable science!
see also:

Teaching with Jupyter Notebooks

http://tinyurl.com/scipy2015-education
Project Jupyter
35
Deploying JupyterHub for Education

Jessica Hamrick

Rackspace blog (2015-03-24)

https://developer.rackspace.com/blog/deploying-
jupyterhub-for-education/
Project Jupyter
36
Literate Programming

Don Knuth

Univ of Chicago Press (1992)

literateprogramming.com/
Instead of imagining that our main task is 

to instruct a computer what to do, let us

concentrate rather on explaining to human

beings what we want a computer to do
Evoking some earlier works…
37
Most definitely check out CodeNeuro,
both online and the conf/hackathon…
Some great examples:
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
38
http://mybinder.org/
turn a GitHub repo into a collection 

of interactive notebooks powered by
Jupyter and Kubernetes
Launch Vehicles
Jupyter, Thebe, Atlas, Docker
40
Embracing Jupyter Notebooks at O'Reilly

Andrew Odewahn

O’Reilly Media (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, Atlas, Docker, etc.
Content Toolchain
41
Embracing Jupyter Notebooks at O'Reilly
Andrew Odewahn
O’Reilly Media (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
Content Toolchain
42
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.
Content Toolchain
43
Personal experiences during 2012-2015 

as an author and instructor…
Just Enough Math

Paco Nathan

O’Reilly Media (2014)

http://justenoughmath.com
Content Toolchain
44
Learnings based on working on this
project with Kyle and Andrew…
How to transit from roles of data scientist,
software developer, engineering director – 

into roles of author, teacher – and vice versa
Content Toolchain
45
Interactive notebooks: 

Sharing the code
Helen Shen
Nature (2014-11-05)
nature.com/news/interactive-notebooks-
sharing-the-code-1.16261
Content Toolchain
46
Content Toolchain
Atlas is our content platform backed by Git,
for project collaboration among authors,
editors, et al.
https://atlas.oreilly.com/
47
Content Toolchain
Thebe (a moon of Jupiter) provides a layer
atop Jupyter that is needed for publishing,
white-labeled content, etc.
https://github.com/oreillymedia/thebe
48
Content Toolchain
Beta is our new site design:
https://beta.oreilly.com/learning
49
Content Toolchain
Contrast our current talent workflow and this 

new world of Jupyter+Docker+Thebe+cloud …
How would it work with known successes such 

as Head First?
production presentation
Thebe:
player
Jupyter:
notebook
Docker:
container
web page:
interaction
Git:
versioning
Atlas:
publications
various
formats
authoring
cloud
infra
Does Science begin with
Phenomenology?
51
Audience Patterns for Learning: ad-hoc
52
Audience Patterns for Learning: architecture
events inverted on-demand
Mostly
Synchronous
Mostly
Asynch
Inverted
Classroom
Paywall
Subscription
Free
Content
53
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
Sidebar: Learning Architecture
Think of a favorite open source framework …
who (or where) are the experts in this graph?
Sidebar: Innovators vs. Experts
Diffusion of Innovation

Everett Rogers (1962)

http://sphweb.bumc.bu.edu/otlt/MPH-Modules/SB/SB721-
Models/SB721-Models4.html
54
55
Building Blocks
In software engineering, we rarely hand a 

developer the spec for some app and say 

“Start from scratch, then come back when
you’re done.” Instead:
• focus on MVP
• leverage APIs, libraries, microservices, etc.
• iterate on small, incremental changes
• this allows for TDD, CI, etc.
• plus, customer experiments ➜ data science
Compare/contrast that with how publishers
approach authors, speakers, instructors?
56
Building Blocks
Proposing a new format spec to replace 

EPUB, MOBI, etc.:
• video segments + transcripts
• notebooks in Jupyter+Thebe+Docker
• metadata (persona, topics, cues, etc.)
• links to Git repos, Dat data
• annotations atop existing content
• webcast/livestream
• social interaction (TA/mentoring)
• evaluation modules
• discourse analytics
most reused across a spectrum
of synchronous to async
instrumented for experiments,
analytics, iteration
57
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?
Dimensional Reduction
Did we mention intense needs 

for data analytics at scale?
58
Is it possible to measure “distance” between 

a learner and a 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
Recommender Systems
59
Back to “Uber for Learning” – approaching from a learner
(audience) perspective, generally within a social context
Given that:
• books aren’t used by learners as much anymore
• experts don’t have time to write books anymore
If we can:
• fit learners’ needs to topics w.r.t. subject communities, 

based on their S-curve positions
• personalize lectures for learners’ pain-points
• reuse containerized building blocks
Imagine the extent to which our current data science 

tooling and techniques can be leveraged?
Summary
60
PS: If you are interested in opportunities 

to write, speak, teach, mentor, code, etc., 

based on these approaches, let us know
Get Involved!
Thank You!
and
Stay Tuned…
presenter:
Just Enough Math
O’Reilly (2014)
justenoughmath.com
monthly newsletter for updates, 

events, conf summaries, etc.:
liber118.com/pxn/

Weitere ähnliche Inhalte

Was ist angesagt?

Hector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business AnalyticsHector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business AnalyticsErika Marr
 
Watson: An Academic's Perspective
Watson: An Academic's PerspectiveWatson: An Academic's Perspective
Watson: An Academic's PerspectiveJames Hendler
 
GALE: Geometric active learning for Search-Based Software Engineering
GALE: Geometric active learning for Search-Based Software EngineeringGALE: Geometric active learning for Search-Based Software Engineering
GALE: Geometric active learning for Search-Based Software EngineeringCS, NcState
 
[Webinar] How Big Data and Machine Learning Are Transforming ITSM
[Webinar] How Big Data and Machine Learning Are Transforming ITSM[Webinar] How Big Data and Machine Learning Are Transforming ITSM
[Webinar] How Big Data and Machine Learning Are Transforming ITSMSunView Software, Inc.
 
Crowdsourced Data Processing: Industry and Academic Perspectives
Crowdsourced Data Processing: Industry and Academic PerspectivesCrowdsourced Data Processing: Industry and Academic Perspectives
Crowdsourced Data Processing: Industry and Academic PerspectivesAditya Parameswaran
 
The Unreasonable Effectiveness of Metadata
The Unreasonable Effectiveness of MetadataThe Unreasonable Effectiveness of Metadata
The Unreasonable Effectiveness of MetadataJames Hendler
 
The Other HPC: High Productivity Computing
The Other HPC: High Productivity ComputingThe Other HPC: High Productivity Computing
The Other HPC: High Productivity ComputingUniversity of Washington
 
Machine Learning using Big data
Machine Learning using Big data Machine Learning using Big data
Machine Learning using Big data Vaibhav Kurkute
 
Why Watson Won: A cognitive perspective
Why Watson Won: A cognitive perspectiveWhy Watson Won: A cognitive perspective
Why Watson Won: A cognitive perspectiveJames Hendler
 
Open Data, Big Data and Machine Learning
Open Data, Big Data and Machine LearningOpen Data, Big Data and Machine Learning
Open Data, Big Data and Machine LearningSteven Van Vaerenbergh
 
Machine Learning in the Cloud with GraphLab
Machine Learning in the Cloud with GraphLabMachine Learning in the Cloud with GraphLab
Machine Learning in the Cloud with GraphLabDanny Bickson
 
KR in the age of Deep Learning
KR in the age of Deep LearningKR in the age of Deep Learning
KR in the age of Deep LearningJames Hendler
 
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...James Hendler
 
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014The Hive
 
Big Data & Machine Learning - TDC2013 Sao Paulo
Big Data & Machine Learning - TDC2013 Sao PauloBig Data & Machine Learning - TDC2013 Sao Paulo
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
 
Semantic Web: The Inside Story
Semantic Web: The Inside StorySemantic Web: The Inside Story
Semantic Web: The Inside StoryJames Hendler
 

Was ist angesagt? (20)

Hector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business AnalyticsHector Guerrero- Road to Business Analytics
Hector Guerrero- Road to Business Analytics
 
Watson: An Academic's Perspective
Watson: An Academic's PerspectiveWatson: An Academic's Perspective
Watson: An Academic's Perspective
 
GALE: Geometric active learning for Search-Based Software Engineering
GALE: Geometric active learning for Search-Based Software EngineeringGALE: Geometric active learning for Search-Based Software Engineering
GALE: Geometric active learning for Search-Based Software Engineering
 
[Webinar] How Big Data and Machine Learning Are Transforming ITSM
[Webinar] How Big Data and Machine Learning Are Transforming ITSM[Webinar] How Big Data and Machine Learning Are Transforming ITSM
[Webinar] How Big Data and Machine Learning Are Transforming ITSM
 
Crowdsourced Data Processing: Industry and Academic Perspectives
Crowdsourced Data Processing: Industry and Academic PerspectivesCrowdsourced Data Processing: Industry and Academic Perspectives
Crowdsourced Data Processing: Industry and Academic Perspectives
 
The Unreasonable Effectiveness of Metadata
The Unreasonable Effectiveness of MetadataThe Unreasonable Effectiveness of Metadata
The Unreasonable Effectiveness of Metadata
 
The Other HPC: High Productivity Computing
The Other HPC: High Productivity ComputingThe Other HPC: High Productivity Computing
The Other HPC: High Productivity Computing
 
Machine learning
Machine learningMachine learning
Machine learning
 
Machine Learning using Big data
Machine Learning using Big data Machine Learning using Big data
Machine Learning using Big data
 
Why Watson Won: A cognitive perspective
Why Watson Won: A cognitive perspectiveWhy Watson Won: A cognitive perspective
Why Watson Won: A cognitive perspective
 
Open Data, Big Data and Machine Learning
Open Data, Big Data and Machine LearningOpen Data, Big Data and Machine Learning
Open Data, Big Data and Machine Learning
 
Machine Learning in the Cloud with GraphLab
Machine Learning in the Cloud with GraphLabMachine Learning in the Cloud with GraphLab
Machine Learning in the Cloud with GraphLab
 
BDACA - Lecture8
BDACA - Lecture8BDACA - Lecture8
BDACA - Lecture8
 
KR in the age of Deep Learning
KR in the age of Deep LearningKR in the age of Deep Learning
KR in the age of Deep Learning
 
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...
Knowledge Representation in the Age of Deep Learning, Watson, and the Semanti...
 
BDACA - Lecture6
BDACA - Lecture6BDACA - Lecture6
BDACA - Lecture6
 
Future se oct15
Future se oct15Future se oct15
Future se oct15
 
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
 
Big Data & Machine Learning - TDC2013 Sao Paulo
Big Data & Machine Learning - TDC2013 Sao PauloBig Data & Machine Learning - TDC2013 Sao Paulo
Big Data & Machine Learning - TDC2013 Sao Paulo
 
Semantic Web: The Inside Story
Semantic Web: The Inside StorySemantic Web: The Inside Story
Semantic Web: The Inside Story
 

Andere mochten auch

Jupyter for Education: Beyond Gutenberg and Erasmus
Jupyter for Education: Beyond Gutenberg and ErasmusJupyter for Education: Beyond Gutenberg and Erasmus
Jupyter for Education: Beyond Gutenberg and ErasmusPaco Nathan
 
Data Science in 2016: Moving Up
Data Science in 2016: Moving UpData Science in 2016: Moving Up
Data Science in 2016: Moving UpPaco Nathan
 
Use of standards and related issues in predictive analytics
Use of standards and related issues in predictive analyticsUse of standards and related issues in predictive analytics
Use of standards and related issues in predictive analyticsPaco Nathan
 
GraphX: Graph analytics for insights about developer communities
GraphX: Graph analytics for insights about developer communitiesGraphX: Graph analytics for insights about developer communities
GraphX: Graph analytics for insights about developer communitiesPaco Nathan
 
Microservices, containers, and machine learning
Microservices, containers, and machine learningMicroservices, containers, and machine learning
Microservices, containers, and machine learningPaco Nathan
 
SF Python Meetup: TextRank in Python
SF Python Meetup: TextRank in PythonSF Python Meetup: TextRank in Python
SF Python Meetup: TextRank in PythonPaco Nathan
 
Apache Spark and the Emerging Technology Landscape for Big Data
Apache Spark and the Emerging Technology Landscape for Big DataApache Spark and the Emerging Technology Landscape for Big Data
Apache Spark and the Emerging Technology Landscape for Big DataPaco Nathan
 
How Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscapeHow Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscapePaco Nathan
 
GalvanizeU Seattle: Eleven Almost-Truisms About Data
GalvanizeU Seattle: Eleven Almost-Truisms About DataGalvanizeU Seattle: Eleven Almost-Truisms About Data
GalvanizeU Seattle: Eleven Almost-Truisms About DataPaco Nathan
 
How Apache Spark fits in the Big Data landscape
How Apache Spark fits in the Big Data landscapeHow Apache Spark fits in the Big Data landscape
How Apache Spark fits in the Big Data landscapePaco Nathan
 
Databricks Meetup @ Los Angeles Apache Spark User Group
Databricks Meetup @ Los Angeles Apache Spark User GroupDatabricks Meetup @ Los Angeles Apache Spark User Group
Databricks Meetup @ Los Angeles Apache Spark User GroupPaco Nathan
 
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and More
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MoreStrata 2015 Data Preview: Spark, Data Visualization, YARN, and More
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MorePaco Nathan
 
Microservices, Containers, and Machine Learning
Microservices, Containers, and Machine LearningMicroservices, Containers, and Machine Learning
Microservices, Containers, and Machine LearningPaco Nathan
 
QCon São Paulo: Real-Time Analytics with Spark Streaming
QCon São Paulo: Real-Time Analytics with Spark StreamingQCon São Paulo: Real-Time Analytics with Spark Streaming
QCon São Paulo: Real-Time Analytics with Spark StreamingPaco Nathan
 
What's new with Apache Spark?
What's new with Apache Spark?What's new with Apache Spark?
What's new with Apache Spark?Paco Nathan
 
Graph Analytics in Spark
Graph Analytics in SparkGraph Analytics in Spark
Graph Analytics in SparkPaco Nathan
 
Data Science in Future Tense
Data Science in Future TenseData Science in Future Tense
Data Science in Future TensePaco Nathan
 
#MesosCon 2014: Spark on Mesos
#MesosCon 2014: Spark on Mesos#MesosCon 2014: Spark on Mesos
#MesosCon 2014: Spark on MesosPaco Nathan
 
OSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningOSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningPaco Nathan
 
Big Data is changing abruptly, and where it is likely heading
Big Data is changing abruptly, and where it is likely headingBig Data is changing abruptly, and where it is likely heading
Big Data is changing abruptly, and where it is likely headingPaco Nathan
 

Andere mochten auch (20)

Jupyter for Education: Beyond Gutenberg and Erasmus
Jupyter for Education: Beyond Gutenberg and ErasmusJupyter for Education: Beyond Gutenberg and Erasmus
Jupyter for Education: Beyond Gutenberg and Erasmus
 
Data Science in 2016: Moving Up
Data Science in 2016: Moving UpData Science in 2016: Moving Up
Data Science in 2016: Moving Up
 
Use of standards and related issues in predictive analytics
Use of standards and related issues in predictive analyticsUse of standards and related issues in predictive analytics
Use of standards and related issues in predictive analytics
 
GraphX: Graph analytics for insights about developer communities
GraphX: Graph analytics for insights about developer communitiesGraphX: Graph analytics for insights about developer communities
GraphX: Graph analytics for insights about developer communities
 
Microservices, containers, and machine learning
Microservices, containers, and machine learningMicroservices, containers, and machine learning
Microservices, containers, and machine learning
 
SF Python Meetup: TextRank in Python
SF Python Meetup: TextRank in PythonSF Python Meetup: TextRank in Python
SF Python Meetup: TextRank in Python
 
Apache Spark and the Emerging Technology Landscape for Big Data
Apache Spark and the Emerging Technology Landscape for Big DataApache Spark and the Emerging Technology Landscape for Big Data
Apache Spark and the Emerging Technology Landscape for Big Data
 
How Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscapeHow Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscape
 
GalvanizeU Seattle: Eleven Almost-Truisms About Data
GalvanizeU Seattle: Eleven Almost-Truisms About DataGalvanizeU Seattle: Eleven Almost-Truisms About Data
GalvanizeU Seattle: Eleven Almost-Truisms About Data
 
How Apache Spark fits in the Big Data landscape
How Apache Spark fits in the Big Data landscapeHow Apache Spark fits in the Big Data landscape
How Apache Spark fits in the Big Data landscape
 
Databricks Meetup @ Los Angeles Apache Spark User Group
Databricks Meetup @ Los Angeles Apache Spark User GroupDatabricks Meetup @ Los Angeles Apache Spark User Group
Databricks Meetup @ Los Angeles Apache Spark User Group
 
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and More
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MoreStrata 2015 Data Preview: Spark, Data Visualization, YARN, and More
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and More
 
Microservices, Containers, and Machine Learning
Microservices, Containers, and Machine LearningMicroservices, Containers, and Machine Learning
Microservices, Containers, and Machine Learning
 
QCon São Paulo: Real-Time Analytics with Spark Streaming
QCon São Paulo: Real-Time Analytics with Spark StreamingQCon São Paulo: Real-Time Analytics with Spark Streaming
QCon São Paulo: Real-Time Analytics with Spark Streaming
 
What's new with Apache Spark?
What's new with Apache Spark?What's new with Apache Spark?
What's new with Apache Spark?
 
Graph Analytics in Spark
Graph Analytics in SparkGraph Analytics in Spark
Graph Analytics in Spark
 
Data Science in Future Tense
Data Science in Future TenseData Science in Future Tense
Data Science in Future Tense
 
#MesosCon 2014: Spark on Mesos
#MesosCon 2014: Spark on Mesos#MesosCon 2014: Spark on Mesos
#MesosCon 2014: Spark on Mesos
 
OSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningOSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine Learning
 
Big Data is changing abruptly, and where it is likely heading
Big Data is changing abruptly, and where it is likely headingBig Data is changing abruptly, and where it is likely heading
Big Data is changing abruptly, and where it is likely heading
 

Ähnlich wie Data Science Reinvents Learning?

Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...Simon Bates
 
The furure of ple
The furure of pleThe furure of ple
The furure of pleayanda
 
Kelvin College final
Kelvin College finalKelvin College final
Kelvin College finalJoe Wilson
 
Are MOOC's past their peak?
Are MOOC's past their peak?Are MOOC's past their peak?
Are MOOC's past their peak?Cybera Inc.
 
Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014
Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014
Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014cccschamp
 
Conole learning design_final
Conole learning design_finalConole learning design_final
Conole learning design_finalGrainne Conole
 
Creating elearning courses
Creating elearning coursesCreating elearning courses
Creating elearning coursesMatleena Laakso
 
MOOCS@Work Working Group Session 1
MOOCS@Work Working Group Session 1MOOCS@Work Working Group Session 1
MOOCS@Work Working Group Session 1LearningCafe
 
Korean mooc presentation may 2015
Korean mooc presentation may 2015Korean mooc presentation may 2015
Korean mooc presentation may 2015Kyle Peck
 
Hack the MOOC: alternative MOOC use
Hack the MOOC: alternative MOOC useHack the MOOC: alternative MOOC use
Hack the MOOC: alternative MOOC useInge de Waard
 
Considering MOOCs: Pros, Cons, Questions
Considering MOOCs: Pros, Cons, QuestionsConsidering MOOCs: Pros, Cons, Questions
Considering MOOCs: Pros, Cons, QuestionsDoug Holton
 
Learning Portals – User Centric Gateway to Learning & Knowledge
Learning Portals – User Centric Gateway to Learning & KnowledgeLearning Portals – User Centric Gateway to Learning & Knowledge
Learning Portals – User Centric Gateway to Learning & KnowledgeLearningCafe
 
Research Webinar: OERS and Cognitive Science
Research Webinar: OERS and Cognitive ScienceResearch Webinar: OERS and Cognitive Science
Research Webinar: OERS and Cognitive ScienceiNACOL
 
Mooc teacher and student benefits
Mooc teacher and student benefitsMooc teacher and student benefits
Mooc teacher and student benefitsInge de Waard
 
Reimagining authentic curriculum in the age of AI
Reimagining authentic curriculum in the age of AIReimagining authentic curriculum in the age of AI
Reimagining authentic curriculum in the age of AICharles Darwin University
 
A University Guide to MOOCs
A University Guide to MOOCsA University Guide to MOOCs
A University Guide to MOOCsNomadWarMachine
 

Ähnlich wie Data Science Reinvents Learning? (20)

MOOCs: some popular claims people make
MOOCs: some popular claims people makeMOOCs: some popular claims people make
MOOCs: some popular claims people make
 
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...
Keynote lecture at 2016 NTU Learning and Teaching Seminar - Students as Partn...
 
The furure of ple
The furure of pleThe furure of ple
The furure of ple
 
Kelvin College final
Kelvin College finalKelvin College final
Kelvin College final
 
Are MOOC's past their peak?
Are MOOC's past their peak?Are MOOC's past their peak?
Are MOOC's past their peak?
 
Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014
Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014
Adventures in Designing a MOOC with OER--STEMTech Denver, CO Nov. 2014
 
Conole learning design_final
Conole learning design_finalConole learning design_final
Conole learning design_final
 
Creating elearning courses
Creating elearning coursesCreating elearning courses
Creating elearning courses
 
MOOCS@Work Working Group Session 1
MOOCS@Work Working Group Session 1MOOCS@Work Working Group Session 1
MOOCS@Work Working Group Session 1
 
Conole masterclass
Conole masterclassConole masterclass
Conole masterclass
 
Korean mooc presentation may 2015
Korean mooc presentation may 2015Korean mooc presentation may 2015
Korean mooc presentation may 2015
 
E learning ns mani
E learning ns maniE learning ns mani
E learning ns mani
 
Hack the MOOC: alternative MOOC use
Hack the MOOC: alternative MOOC useHack the MOOC: alternative MOOC use
Hack the MOOC: alternative MOOC use
 
Considering MOOCs: Pros, Cons, Questions
Considering MOOCs: Pros, Cons, QuestionsConsidering MOOCs: Pros, Cons, Questions
Considering MOOCs: Pros, Cons, Questions
 
MOOC Presentation for EDDE 801
MOOC Presentation for EDDE 801MOOC Presentation for EDDE 801
MOOC Presentation for EDDE 801
 
Learning Portals – User Centric Gateway to Learning & Knowledge
Learning Portals – User Centric Gateway to Learning & KnowledgeLearning Portals – User Centric Gateway to Learning & Knowledge
Learning Portals – User Centric Gateway to Learning & Knowledge
 
Research Webinar: OERS and Cognitive Science
Research Webinar: OERS and Cognitive ScienceResearch Webinar: OERS and Cognitive Science
Research Webinar: OERS and Cognitive Science
 
Mooc teacher and student benefits
Mooc teacher and student benefitsMooc teacher and student benefits
Mooc teacher and student benefits
 
Reimagining authentic curriculum in the age of AI
Reimagining authentic curriculum in the age of AIReimagining authentic curriculum in the age of AI
Reimagining authentic curriculum in the age of AI
 
A University Guide to MOOCs
A University Guide to MOOCsA University Guide to MOOCs
A University Guide to MOOCs
 

Mehr von Paco Nathan

Human in the loop: a design pattern for managing teams working with ML
Human in the loop: a design pattern for managing  teams working with MLHuman in the loop: a design pattern for managing  teams working with ML
Human in the loop: a design pattern for managing teams working with MLPaco Nathan
 
Human-in-the-loop: a design pattern for managing teams that leverage ML
Human-in-the-loop: a design pattern for managing teams that leverage MLHuman-in-the-loop: a design pattern for managing teams that leverage ML
Human-in-the-loop: a design pattern for managing teams that leverage MLPaco Nathan
 
Human-in-a-loop: a design pattern for managing teams which leverage ML
Human-in-a-loop: a design pattern for managing teams which leverage MLHuman-in-a-loop: a design pattern for managing teams which leverage ML
Human-in-a-loop: a design pattern for managing teams which leverage MLPaco Nathan
 
Humans in a loop: Jupyter notebooks as a front-end for AI
Humans in a loop: Jupyter notebooks as a front-end for AIHumans in a loop: Jupyter notebooks as a front-end for AI
Humans in a loop: Jupyter notebooks as a front-end for AIPaco Nathan
 
Humans in the loop: AI in open source and industry
Humans in the loop: AI in open source and industryHumans in the loop: AI in open source and industry
Humans in the loop: AI in open source and industryPaco Nathan
 
Computable Content
Computable ContentComputable Content
Computable ContentPaco Nathan
 
Computable Content: Lessons Learned
Computable Content: Lessons LearnedComputable Content: Lessons Learned
Computable Content: Lessons LearnedPaco Nathan
 
Strata EU 2014: Spark Streaming Case Studies
Strata EU 2014: Spark Streaming Case StudiesStrata EU 2014: Spark Streaming Case Studies
Strata EU 2014: Spark Streaming Case StudiesPaco Nathan
 
Tiny Batches, in the wine: Shiny New Bits in Spark Streaming
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingTiny Batches, in the wine: Shiny New Bits in Spark Streaming
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingPaco Nathan
 
Brief Intro to Apache Spark @ Stanford ICME
Brief Intro to Apache Spark @ Stanford ICMEBrief Intro to Apache Spark @ Stanford ICME
Brief Intro to Apache Spark @ Stanford ICMEPaco Nathan
 
How Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscapeHow Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscapePaco Nathan
 

Mehr von Paco Nathan (11)

Human in the loop: a design pattern for managing teams working with ML
Human in the loop: a design pattern for managing  teams working with MLHuman in the loop: a design pattern for managing  teams working with ML
Human in the loop: a design pattern for managing teams working with ML
 
Human-in-the-loop: a design pattern for managing teams that leverage ML
Human-in-the-loop: a design pattern for managing teams that leverage MLHuman-in-the-loop: a design pattern for managing teams that leverage ML
Human-in-the-loop: a design pattern for managing teams that leverage ML
 
Human-in-a-loop: a design pattern for managing teams which leverage ML
Human-in-a-loop: a design pattern for managing teams which leverage MLHuman-in-a-loop: a design pattern for managing teams which leverage ML
Human-in-a-loop: a design pattern for managing teams which leverage ML
 
Humans in a loop: Jupyter notebooks as a front-end for AI
Humans in a loop: Jupyter notebooks as a front-end for AIHumans in a loop: Jupyter notebooks as a front-end for AI
Humans in a loop: Jupyter notebooks as a front-end for AI
 
Humans in the loop: AI in open source and industry
Humans in the loop: AI in open source and industryHumans in the loop: AI in open source and industry
Humans in the loop: AI in open source and industry
 
Computable Content
Computable ContentComputable Content
Computable Content
 
Computable Content: Lessons Learned
Computable Content: Lessons LearnedComputable Content: Lessons Learned
Computable Content: Lessons Learned
 
Strata EU 2014: Spark Streaming Case Studies
Strata EU 2014: Spark Streaming Case StudiesStrata EU 2014: Spark Streaming Case Studies
Strata EU 2014: Spark Streaming Case Studies
 
Tiny Batches, in the wine: Shiny New Bits in Spark Streaming
Tiny Batches, in the wine: Shiny New Bits in Spark StreamingTiny Batches, in the wine: Shiny New Bits in Spark Streaming
Tiny Batches, in the wine: Shiny New Bits in Spark Streaming
 
Brief Intro to Apache Spark @ Stanford ICME
Brief Intro to Apache Spark @ Stanford ICMEBrief Intro to Apache Spark @ Stanford ICME
Brief Intro to Apache Spark @ Stanford ICME
 
How Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscapeHow Apache Spark fits into the Big Data landscape
How Apache Spark fits into the Big Data landscape
 

Kürzlich hochgeladen

Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.raviapr7
 
10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdf10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdfJayanti Pande
 
SOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptx
SOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptxSOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptx
SOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptxSyedNadeemGillANi
 
A gentle introduction to Artificial Intelligence
A gentle introduction to Artificial IntelligenceA gentle introduction to Artificial Intelligence
A gentle introduction to Artificial IntelligenceApostolos Syropoulos
 
Department of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdfDepartment of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdfMohonDas
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptxSandy Millin
 
Protein Structure - threading Protein modelling pptx
Protein Structure - threading Protein modelling pptxProtein Structure - threading Protein modelling pptx
Protein Structure - threading Protein modelling pptxvidhisharma994099
 
Quality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICEQuality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICESayali Powar
 
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRADUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRATanmoy Mishra
 
Vani Magazine - Quarterly Magazine of Seshadripuram Educational Trust
Vani Magazine - Quarterly Magazine of Seshadripuram Educational TrustVani Magazine - Quarterly Magazine of Seshadripuram Educational Trust
Vani Magazine - Quarterly Magazine of Seshadripuram Educational TrustSavipriya Raghavendra
 
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptxClinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptxraviapr7
 
3.21.24 The Origins of Black Power.pptx
3.21.24  The Origins of Black Power.pptx3.21.24  The Origins of Black Power.pptx
3.21.24 The Origins of Black Power.pptxmary850239
 
How to Send Emails From Odoo 17 Using Code
How to Send Emails From Odoo 17 Using CodeHow to Send Emails From Odoo 17 Using Code
How to Send Emails From Odoo 17 Using CodeCeline George
 
EBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting BlEBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting BlDr. Bruce A. Johnson
 
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...M56BOOKSTORE PRODUCT/SERVICE
 
How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17Celine George
 
CapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapitolTechU
 
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...Nguyen Thanh Tu Collection
 
Over the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxOver the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxraviapr7
 

Kürzlich hochgeladen (20)

Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.
 
10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdf10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdf
 
SOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptx
SOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptxSOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptx
SOLIDE WASTE in Cameroon,,,,,,,,,,,,,,,,,,,,,,,,,,,.pptx
 
A gentle introduction to Artificial Intelligence
A gentle introduction to Artificial IntelligenceA gentle introduction to Artificial Intelligence
A gentle introduction to Artificial Intelligence
 
Department of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdfDepartment of Health Compounder Question ‍Solution 2022.pdf
Department of Health Compounder Question ‍Solution 2022.pdf
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
 
Protein Structure - threading Protein modelling pptx
Protein Structure - threading Protein modelling pptxProtein Structure - threading Protein modelling pptx
Protein Structure - threading Protein modelling pptx
 
Quality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICEQuality Assurance_GOOD LABORATORY PRACTICE
Quality Assurance_GOOD LABORATORY PRACTICE
 
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRADUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
 
Vani Magazine - Quarterly Magazine of Seshadripuram Educational Trust
Vani Magazine - Quarterly Magazine of Seshadripuram Educational TrustVani Magazine - Quarterly Magazine of Seshadripuram Educational Trust
Vani Magazine - Quarterly Magazine of Seshadripuram Educational Trust
 
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptxClinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
 
3.21.24 The Origins of Black Power.pptx
3.21.24  The Origins of Black Power.pptx3.21.24  The Origins of Black Power.pptx
3.21.24 The Origins of Black Power.pptx
 
How to Send Emails From Odoo 17 Using Code
How to Send Emails From Odoo 17 Using CodeHow to Send Emails From Odoo 17 Using Code
How to Send Emails From Odoo 17 Using Code
 
EBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting BlEBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting Bl
 
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
 
How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17How to Add a New Field in Existing Kanban View in Odoo 17
How to Add a New Field in Existing Kanban View in Odoo 17
 
CapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptx
 
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
 
Over the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxOver the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptx
 
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdfPersonal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
 

Data Science Reinvents Learning?

  • 1. 2015-08-24 • San Jose Paco Nathan, @pacoid
 Director, O’Reilly Learning Data Science Reinvents Learning? Beyond Gutenberg and Erasmus meetup.com/SF-Bay-ACM/events/221693508/
  • 2. 2 Some Background… • O’Reilly Learning: you may only hear about us in 
 a few instances, if we do our job well; ACM is a great forum for this discussion • prior: built-out the community evangelism and training program for Apache Spark at Databricks • prior: led Data teams for several years, working on 
 large-scale ML apps in industry, including: one of the largest Hadoop instances running in AWS (2008); 
 one of the first 100% AWS system architectures (2006) • … • ancient prior: Stanford CSD teaching fellowship (1984-86, Alice Supton, Stuart Reges) peer-teaching CS course which later became Residential Computing
  • 4. 4 Intro Quite candidly, the one common catch phrase 
 in SiliconValley that I find most terrifying: “It’s like Uber, for ___”
  • 5. 5 Intro Ostensibly that leads to a question, how might 
 an “Uber for Education” look?
  • 6. 6 Intro Ostensibly that leads to a question, how might 
 an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that
  • 7. 7 Intro Ostensibly that leads to a question, how might 
 an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that
  • 8. 8 Intro Ostensibly that leads to a question, how might 
 an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that b) Would we really need that anywho?
  • 9. 9 Intro Ostensibly that leads to a question, how might 
 an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that b) Would we really need that anywho? c) Uber itself might not take that approach …
  • 10. 10 Intro Ostensibly that leads to a question, how might 
 an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that b) Would we really need that anywho? c) Uber itself might not take that approach … Perhaps “Uber for Learning” might be somewhat
 more apt? In any case, what comes after Books, Kindle, MOOCs?
  • 12. “Learning” ergo… “Education” ergo… “School” X 12 Some Definitions… Schools are great to have… If you need a school, pick a 
 good one and go To be clear, we’re not a school
  • 13. 13 Some Definitions… Even the best schools these days question
 what they will become in 5-10 years Not-so-best schools are perhaps questioning 
 much more than that
  • 14. 14 Some Definitions… Oh BTW, too many (funded) teams seem to 
 have this mediocre idea for “education”: 1. assessment: collect test scores ➜ 2. define “quantified student” ➜ 3. reuse online marketing funnel ad-tech ➜ 4. invoke agile coding teams ➜ 5. ship mobile/cloud-based SaaS platform ➜ 6. ... 7. profit
  • 15. Oh BTW, too many (funded) teams seem to have this mediocre idea for “education” 1. assessment: collect test scores 2. define “quantified student” 3. reuse online marketing funnel ad-tech 4. invoke agile coding 5. ship a mobile/cloud-based SaaS platform 6. ... 7. profit 15 Some Definitions… LMS
  • 16. K-12 not so much, except perhaps in the case of Safari for Schools undergrad textbooks? graduate textbooks, conferences? professional focus of our audience 16 Some Definitions…
  • 17. 17 • vocational: 
 making a career move • aspirational: 
 improvement within a career path • proficiency: 
 has a specific pain-point, needs to resolve it • familiarity: 
 wants to join in a team dialog about a topic, 
 e.g., conversational programmer Learner Personas for professional category
  • 19. 19 What about MOOCs? Massive Open Online Courses – 
 seven year trend, beginning with: Connectivism and Connective Knowledge
 George Siemens, Stephen Downes
 University of PEI (2008)
 http://cck11.mooc.ca/
  • 21. 21 What about MOOCs? Anthony Joseph
 UC Berkeley early Jun 2015 edx.org/course/uc-berkeleyx/uc- berkeleyx-cs100-1x- introduction-big-6181 Ameet Talwalkar
 UCLA late Jun 2015 edx.org/course/uc-berkeleyx/ uc-berkeleyx-cs190-1x- scalable-machine-6066
  • 22. 22 What about MOOCs? Pros: • cost-effective to reach a large audience • popular with students • ¿ addresses “train the trainers” bottleneck ? Cons: • expensive to produce and curate • most students are sampling • low completion rates • somewhat chaotic • lecture fatigue • ¿ reinforces advantage of the elites ?
  • 23. 23 What about MOOCs? Online education: MOOCs taken by educated few
 Ezekiel Emanuel, Nature 503, 342 (2013-11-21) • 80% students already have an advanced degree • 80% come from the richest 6% of the population Michael Shanks @Stanford: retrenchment around traditional disciplines will make disparities even more pronounced An Early Report Card on Massive Open Online Courses
 Geoffrey Fowler, WSJ (2013-10-08) Amherst, Duke, etc., have rejected edX see: Open edX Universities Symposium @GWU, 2015-11-11
  • 24. 24 • search engines surface too many choices 
 among the available learning content • we must get people wanting to interact with the material – generally due to social context • academe strives to decontextualize, which 
 is the opposite of learning in context • how do we recognize that learning has occurred? • what is the learning promise? What about MOOCs?
  • 26. 26 Introduction to Robotics Peter Corke @QUT https://moocs.qut.edu.au/learn/introduction-to- robotics-august-2015 • effective use of peer review for scaling • worked well reaching into Africa, India Peer Review
  • 27. 27 EffectiveThinkingThrough Mathematics Michael Starbird @UT/Austin https://www.edx.org/course/effective-thinking- through-mathematics-utaustinx-ut-9-01x • getting students to articulate their epiphany moments is more interesting 
 than other results – Donna Kidwell Epiphany Moments
  • 28. 28 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 • significant improvement through the use of “flipped” a.k.a. inverted classrooms Inverted Classrooms
  • 29. 29 Scalable Learning
 David Black-Schaffer @Uppsala
 Sverker Janson @KTH SICS https://www.scalable-learning.com/ • active learning: Flipped Classroom and Just-in-timeTeaching • exams built directly into specific diagrams within videos • metrics for where in video+code that students get stuck • instructor can customize subsequent classroom discussions 
 (active teaching phase) based on stuck/unstuck metrics Inverted Classrooms
  • 30. 30 How to Flip a Class 
 CLT @UT/Austin
 http://ctl.utexas.edu/teaching/flipping-a-class/how 1. identify where the flipped classroom model makes 
 the most sense for your course 2. spend class time engaging students in application activities with feedback 3. clarify connections between inside and outside 
 of class learning 4. adapt your materials for students to acquire course content in preparation of class 5. extend learning beyond class through individual 
 and collaborative practice Inverted Classrooms
  • 31. 31 Learning programming at scale Philip Guo 
 O’Reilly Radar (2015-08-13) http://radar.oreilly.com/2015/08/learning- programming-at-scale.html • PythonTutor • Codechella Tutors could keep an eye on around 
 50 learners during a 30-minute session, 
 start 12 chat conversations, and 
 concurrently help 3 learners at once Collaborative Learning
  • 32. 32 Data-driven Education and the Quantified Student Lorena Barba @GWU PyData Seattle 2015 https://youtu.be/2YIZ2SY9mW4 • keynote talk: abstract, slides • homepage If you study just one link in this entire talk…
  • 34. 34 If by some bizarre chance you haven’t used 
 it already, go to https://jupyter.org/ • 50+ different language kernels • new funding 2015-07 • UC Berkeley, Cal Poly • nbgrader autograder by Jess Hamrick • jupyterhub multi-user server • curating a list of examples • repeatable science! see also:
 Teaching with Jupyter Notebooks
 http://tinyurl.com/scipy2015-education Project Jupyter
  • 35. 35 Deploying JupyterHub for Education
 Jessica Hamrick
 Rackspace blog (2015-03-24)
 https://developer.rackspace.com/blog/deploying- jupyterhub-for-education/ Project Jupyter
  • 36. 36 Literate Programming
 Don Knuth
 Univ of Chicago Press (1992)
 literateprogramming.com/ Instead of imagining that our main task is 
 to instruct a computer what to do, let us
 concentrate rather on explaining to human
 beings what we want a computer to do Evoking some earlier works…
  • 37. 37 Most definitely check out CodeNeuro, both online and the conf/hackathon… Some great examples: 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
  • 38. 38 http://mybinder.org/ turn a GitHub repo into a collection 
 of interactive notebooks powered by Jupyter and Kubernetes Launch Vehicles
  • 40. 40 Embracing Jupyter Notebooks at O'Reilly
 Andrew Odewahn
 O’Reilly Media (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, Atlas, Docker, etc. Content Toolchain
  • 41. 41 Embracing Jupyter Notebooks at O'Reilly Andrew Odewahn O’Reilly Media (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 Content Toolchain
  • 42. 42 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. Content Toolchain
  • 43. 43 Personal experiences during 2012-2015 
 as an author and instructor… Just Enough Math
 Paco Nathan
 O’Reilly Media (2014)
 http://justenoughmath.com Content Toolchain
  • 44. 44 Learnings based on working on this project with Kyle and Andrew… How to transit from roles of data scientist, software developer, engineering director – 
 into roles of author, teacher – and vice versa Content Toolchain
  • 45. 45 Interactive notebooks: 
 Sharing the code Helen Shen Nature (2014-11-05) nature.com/news/interactive-notebooks- sharing-the-code-1.16261 Content Toolchain
  • 46. 46 Content Toolchain Atlas is our content platform backed by Git, for project collaboration among authors, editors, et al. https://atlas.oreilly.com/
  • 47. 47 Content Toolchain Thebe (a moon of Jupiter) provides a layer atop Jupyter that is needed for publishing, white-labeled content, etc. https://github.com/oreillymedia/thebe
  • 48. 48 Content Toolchain Beta is our new site design: https://beta.oreilly.com/learning
  • 49. 49 Content Toolchain Contrast our current talent workflow and this 
 new world of Jupyter+Docker+Thebe+cloud … How would it work with known successes such 
 as Head First? production presentation Thebe: player Jupyter: notebook Docker: container web page: interaction Git: versioning Atlas: publications various formats authoring cloud infra
  • 50. Does Science begin with Phenomenology?
  • 51. 51 Audience Patterns for Learning: ad-hoc
  • 52. 52 Audience Patterns for Learning: architecture events inverted on-demand Mostly Synchronous Mostly Asynch Inverted Classroom Paywall Subscription Free Content
  • 53. 53 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 Sidebar: Learning Architecture
  • 54. Think of a favorite open source framework … who (or where) are the experts in this graph? Sidebar: Innovators vs. Experts Diffusion of Innovation
 Everett Rogers (1962)
 http://sphweb.bumc.bu.edu/otlt/MPH-Modules/SB/SB721- Models/SB721-Models4.html 54
  • 55. 55 Building Blocks In software engineering, we rarely hand a 
 developer the spec for some app and say 
 “Start from scratch, then come back when you’re done.” Instead: • focus on MVP • leverage APIs, libraries, microservices, etc. • iterate on small, incremental changes • this allows for TDD, CI, etc. • plus, customer experiments ➜ data science Compare/contrast that with how publishers approach authors, speakers, instructors?
  • 56. 56 Building Blocks Proposing a new format spec to replace 
 EPUB, MOBI, etc.: • video segments + transcripts • notebooks in Jupyter+Thebe+Docker • metadata (persona, topics, cues, etc.) • links to Git repos, Dat data • annotations atop existing content • webcast/livestream • social interaction (TA/mentoring) • evaluation modules • discourse analytics most reused across a spectrum of synchronous to async instrumented for experiments, analytics, iteration
  • 57. 57 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? Dimensional Reduction Did we mention intense needs 
 for data analytics at scale?
  • 58. 58 Is it possible to measure “distance” between 
 a learner and a 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 Recommender Systems
  • 59. 59 Back to “Uber for Learning” – approaching from a learner (audience) perspective, generally within a social context Given that: • books aren’t used by learners as much anymore • experts don’t have time to write books anymore If we can: • fit learners’ needs to topics w.r.t. subject communities, 
 based on their S-curve positions • personalize lectures for learners’ pain-points • reuse containerized building blocks Imagine the extent to which our current data science 
 tooling and techniques can be leveraged? Summary
  • 60. 60 PS: If you are interested in opportunities 
 to write, speak, teach, mentor, code, etc., 
 based on these approaches, let us know Get Involved!
  • 62. presenter: Just Enough Math O’Reilly (2014) justenoughmath.com monthly newsletter for updates, 
 events, conf summaries, etc.: liber118.com/pxn/