Big Data Curricula at the UW eScience Institute, JSM 2013

Bill Howe, PhD
Director of
Research, Scalable Data
Analytics
University of Washington
eScience Institute
Big Data Curricula at the
University of Washington
eScience Institute
8/7/2013 Bill Howe, UW 1

2
“It’s a great time to be a data geek.”
-- Roger Barga, Microsoft Research
“The greatest minds of my generation are trying
to figure out how to make people click on ads”
-- Jeff Hammerbacher, co-founder, Cloudera

1. Theory (last 2000 yrs)
2. Experiment (last 200
yrs)
3. Simulation (last 50 yrs)
4. Data-Driven Discovery
(last 5 yrs)

The University of Washington
eScience Institute
• Rationale
– The exponential increase in sensors is transitioning all fields of science
and engineering from data-poor to data-rich
– As a result, the techniques and technologies of data science must be
widely practiced and widely adopted
• Mission
– Advance the forefront of research both in modern data science
techniques and technologies, and in the fields that depend upon them
• Strategy
– Provide an umbrella organization for Big Data activities at UW and
beyond (new curricula, collaborations, funding sources, hiring practices)
– Bootstrap a national network of partners and peer institutes
– Attract, develop, and retain “Pi-shaped people”

π-shaped researchers
Broad in many areas; deep in at least two

UW Data Science Education Efforts
Students Non-Students
CS/Informatics Non-Major
professionals researchers
undergrads grads undergrads grads
UWEO Data Science Certificate
Graduate Certificate in Big Data
CS Data Management Courses
eScience workshops
Intro to data programming
eScience Masters (planned)
MOOC: Intro to Data Science
Incubator: On-the-job-training
Previous courses:
Scientific Data Management, Graduate CS, Summer 2006, Portland State University
Scientific Data Management, Graduate CS, Spring 2010, University of Washington

Three Activities
• Massively Open Online Course
• New Phd Tracks in Big Data
• An Incubator for Data Science Projects
• Other actitivites I won’t discuss
– Undergraduate “Data Wizardry” Courses
– 2-day Bootcamps in Python, SQL, GitHub, …
– Certificate Programs in Data Science
– Hackathons

Three Activities
– Hackathons

• 8600 completed all programming assignments
• 7000 earned a certificate

Syllabus
• Data Science Landscape (~1 week)
• Data Manipulation at Scale
– Relational Databases (~1 week)
– MapReduce (~1 week)
– NoSQL (~1 week)
• Analytics
– Statistics Pearls (~1 week)
– Machine Learning Pearls (~1 week)
• Visualization (~1 week)

tools abstr.
desk cloud
structs stats
hackers analysts
This Course

What are the abstractions of
data science?
tools abstr.
“Data Jujitsu”
“Data Wrangling”
“Data Munging”
Translation: “We have no idea what
this is all about”

matrices and linear algebra?
relations and relational algebra?
objects and methods?
files and scripts?
data frames and functions?
What are the abstractions of
data science?
tools abstr.

16
Data Access Hitting a Wall
Current practice based on data download (FTP/GREP)
Will not scale to the datasets of tomorrow
• You can GREP 1 MB in a second
• You can GREP 1 GB in a minute
• You can GREP 1 TB in 2 days
• You can GREP 1 PB in 3 years.
• Oh!, and 1PB ~5,000 disks
• At some point you need
indices to limit search
parallel data search and analysis
• This is where databases can help
• You can FTP 1 MB in 1 sec
• You can FTP 1 GB / min (~1$)
• … 2 days and 1K$
• … 3 years and 1M$
desk cloud
[slide src: Jim Gray]

US faces shortage of 140,000 to 190,000
people “with deep analytical skills, as well
as 1.5 million managers and analysts with
the know-how to use the analysis of big
data to make effective decisions.”
--Mckinsey Global Institute
hackers analysts

Three types of tasks:
1) Preparing to run a model
2) Running the model
3) Interpreting the results
Gathering, cleaning, integrating, restructuring,
transforming, loading, filtering, deleting, combining,
merging, verifying, extracting, shaping, massaging
“80% of the work”
-- Aaron Kimball
“The other 80% of the work”
-- Aaron Kimball
structs stats

Three Activities
– Hackathons

New Phd Track: “Big Data U”
• Open to all departments
• New courses to “level the playing field”
– “Molecular Biology for Computer Scientists” offered this Fall
• Dual advising in two disciplines
• Joint projects leading to multiple theses
– Each methods thesis will include domain impact component
– Each domain thesis will include methods impact component
• Contribution to a shared cyberinfrastructure
– Software engineering experience as a side effect
• “Application Assistantships”
– Like RAs and TAs; focused on solving a concrete problem
Magda
Balazinska
Carlos
Guestrin

Three Activities
• An Incubator for Data Science
– Hackathons

Data Science Incubator: Motivation
• We need the right people
– We produce “builders,” but 99% of them go to industry to
“make people click on ads”
– They aren’t motivated by writing papers
– No viable career path in the academy
• We need the right processes
– Hands-on, extended, intensive experience is required to
produce π-shaped people
– Data-driven discovery requires intensive collaboration

Science Domains
Stats, Computer
Science, Applied Math
• “Where’s the funding?”
• “How does this help me write a paper in my field”?
• Thin collaborations; nobody to work on the short-
term, high-risk, high-impact “triage” projects
• “Does method X work on dataset Y?”

Domain Labs
Research Programmers
• Expensive; doesn’t scale
• “Code Monkey” – No viable career path
• Can’t attract top people
• No sharing, no community, no cross-pollination

Data Science Incubator: Structure
• Recruit top-flight data science talent
• Give them autonomy to select collaborations and projects
• Promote them according to “altmetrics” and project impact
– “Data Scientist”  “Senior Data Scientist”  “Technical Fellow”
– “Data Science Fellows”
• Perhaps non-tenure, but 3-5 year commitments
• Funded with contributions from Academic units, IT,
Libraries, and soft money

Data Science Incubator: Seed Grants
• Domain researchers submit Seed Grant applications
for short, intensive 1-6 month projects
– Reviewed by the Data Scientists themselves
• Awardees send 1+ students, postdocs, staff, or faculty
to come and physically sit in the incubator space X
days per week for the project duration
– Application may or may not include funding for the student

Domain Labs
Incubator
• Data Scientists have their own identity and prestige
• Cross-pollination between disciplines
• Awardees leave with skills and knowledge; become “disciples”

Three Activities
• An Incubator for Data Science
– Hackathons

MOOC “Introduction to Data Science:”
https://www.coursera.org/course/datasci
Certificate program:
http://www.pce.uw.edu/courses/data-science-intro
http://escience.washington.edu
billhowe@cs.washington.edu

Big Data Curricula at the UW eScience Institute, JSM 2013

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