Presentation given by Peter Bull of DrivenData at the August 2017 Data for Good meetup in San Francisco.
Crowdsourcing Data for Good: Lessons from the social sector and how to get involved
Just like every major corporation today, nonprofits and governments have more data than ever before. And just like those corporations, they are eager to tap into the power of their data. But the social sector doesn’t have the same resources to attract talent. Jeff Hammerbacher put it best: “The best minds of my generation are thinking about how to make people click ads. That sucks.” At DrivenData our goal is to make the world suck a little less by empowering impact organizations to get the most from their data. Peter Bull, co-founder at DrivenData, will speak on the ways in which statistics, computer science, and machine learning can be applied to the challenges in the social sector. The talk has two parts: the first is the big-picture context of the data for good movement, how to get involved. The second is an in-depth case study of the methods which won DrivenData’s recent machine learning competitions.
7. Transformers: Age of Extinction (2014) 5.8/10 stars on IMDB
Simple coding…ALGORITHMS!MATH!Why can’t we make what we want to make the way we want to make it?
12. THE DATA CAPACITY GAP
$135,000
average salary
executive director
US nonprofit
140k – 180k
shortage of data
scientists
$118,700
average salary of
data scientist
13. “Finding ways to make big data
useful to humanitarian decision
makers is one of the great
challenges and opportunities of
the network age.”
UN Office for the Coordination of Humanitarian Affairs
14. “The best minds of my generation
are thinking about how to make
people click ads… That sucks.”
Jeff Hammerbacher, Former Data Manager, Facebook
26. DATA FOR
ACTION
Collection Plan Data Collection Data Storage Data Analysis Results
DATA FOR IMPACT
data
scientist
domain
expert
data
scientist
domain
expert
30. PETRO-VEND FUEL AND FLUIDS
MAINT MATERIALS
SATELLITE COOK
UPPER EARLY INTERVENTION PROGRAM 4-5
Regional Playoff Hosts
Supp.- Materials
ITEMGH EXTENDED DAY
FURNITURE AND FIXTURES
NON-CAPITALIZED AV
Water and Sewage *
Instructional Materials
Food Services - Other Costs
Capital Assets - Locally Defined Groupings
31. Regular *
TEACHE
R
Special
Instruction
TCHR, SCNDY
MATH
Certificated
Employees
Salaries And
Wages
IN OTH
CERTIFICAT
ED PERSON
1
Disadvantage
d Youth *
$104,928.19
Title I -
Disadvantaged
Children/Target
ed Assistance
BUILDING
ALLOCATION
S
LABEL COLUMN Function Sharing Object_Type Student_Type Position_Type
MOST LIKELY Teacher Compensation School Reported
Base
Salary/Compensatio
n
Poverty Teacher
2ND LIKELY Food Services
School on Central
Budgets
Benefit Unspecified Coordinator/Manager
3RD LIKELY NO_LABEL NO_LABEL Supplies/Materials NO_LABEL NO_LABEL
New Data
Predictions!
86. Donate your skills and your
knowledge in the way that works
for you.
Data for Good Meetup
Domino Data Labs – 30 August 2017
Data has the power to change the world.
