Using your powers for good:

Data science in the social sector

www.drivendata.org

PART I: THE SOCIAL SECTOR

WWADSD?

DISASTER RELIEF

WHAT ACTUALLY GETS DONE:

ü COUNTS!!

ü SUMS!!

ü AVERAGES!!

DISASTER RELIEF

THE DATA LITERACY GAP

When it comes to data science, nonprofits don’t know what they don’t know.

•  Running and publicizing projects with organizations

•  Formal education, resources, and training

•  Encouraging data scientists to work in the social sector

THE OPPORTUNITY

THE DATA CAPACITY GAP

•  McKinsey predicts a 140k – 180k shortage of data scientists

•  Average salary of data scientist:$118,709

•  Average salary of the Executive Director of a nonprofit (budget $500k – $5m):

•  $133,000

LITERACY

CAPACITY

“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

PART II: CASE STUDY

R

Training set Data shared with contestants to use in generating innovative predictive algorithms

COMPETITION DATASET

T Test set Output data retained to test submissions and automatically rank by predictive accuracy

MEASURING PERFORMANCE

A B C D E F G H

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32% Competitors submit predictions based on their

statistical algorithms 987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2 32%

987 xldlk 3.98 sdsd 8.3 9 6.2

987 xldlk 3.98 sdsd 8.3 9 6.2

987 xldlk 3.98 sdsd 8.3 9 6.2

987 xldlk 3.98 sdsd 8.3 9 6.2

987 xldlk 3.98 sdsd 8.3 9 6.2

987 xldlk 3.98 sdsd 8.3 9 6.2

987 xldlk 3.98 sdsd 8.3 9 6.2

31%

32%

29%

30%

27%

32%

31%

Education Resource Strategies

Budget  

We are budget-ing our things!

$  Expenditures

Monies for

making students

good

Beware there be dollars here

Lots of Labels! 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

Regular * TEACHER Special

Instruction TCHR, SCNDY MATH

Certificated Employees

Salaries And Wages

IN OTH CERTIFICATED PERSON

1 Disadvantaged

Youth * $104,928.19

Title I - Disadvantaged

Children/Targeted

Assistance

BUILDING ALLOCATIONS

LABEL COLUMN Function Sharing Object_Type Student_Type Position_Type

MOST LIKELY Teacher Compensation School Reported Base Salary/Compensation

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!

Budget  

We are budget-ing our things! $  

Expenditures Monies for

making students

good

Beware there be dollars here

All about the features!

Text features in scikit-­‐learn

from sklearn.feature_extraction.text import CountVectorizer!!vec = CountVectorizer()!!vectorized_data = vec.fit_transform(text_data)!                          

Processing: Tokenize on Punctuation

PETRO-VEND FUEL AND FLUIDS

PETRO VEND FUEL AND FLUIDS

PETRO-VEND

FUEL AND FLUIDS

Processing: Tokenize on Punctuation

from sklearn.feature_extraction.text import CountVectorizer!!alpha_tokens = lambda text: re.split("[^a-z]", text.lower())!!vec = CountVectorizer(tokenizer=alpha_tokens)!!vectorized_data = vec.fit_transform(text_data)!                

Processing: 2-grams and 3-grams

PETRO VEND FUEL AND FLUIDS

1-grams

2-grams

3-grams

from sklearn.feature_extraction.text import CountVectorizer!!alpha_tokens = lambda text: re.split("[^a-z]", text.lower())!!vec = CountVectorizer(tokenizer=alpha_tokens,! ngram_range=(1, 3))!!vectorized_data = vec.fit_transform(text_data)!!!!!!!!!!

Processing: 2-grams and 3-grams

Computational: Hashing Trick

PETRO VEND FUEL AND FLUIDS

2954 9384 4569 1197 8947

Computational: Hashing Trick

from sklearn.feature_extraction.text import HashingVectorizer!!alpha_tokens = lambda text: re.split("[^a-z]", text.lower())!!vec = HashingVectorizer(tokenizer=alpha_tokens,! ngram_range=(1, 3))!!vectorized_data = vec.fit_transform(text_data)!!!!!!!!!!!

Statistical: Pairwise Interaction Terms

from sklearn.feature_extraction.text import HashingVectorizer!from sklearn.preprocessing import PolynomialFeatures!!alpha_tokens = lambda text: re.split("[^a-z]", text.lower())!!vec = HashingVectorizer(tokenizer=alpha_tokens,! ngram_range=(1, 3))!!vectorized_data = vec.fit_transform(text_data)!!prep = PolynomialFeatures(degree=2, !

! ! ! ! ! ! ! interaction_only=True,!! ! ! ! ! ! ! include_bias=False)!

!preprocessed_data = prep.fit_transform(vectorized_data)!!!!

Statistical: Pairwise Interaction Terms

Countable Care: A different kind of dataset

So Many NaNs!

Not Building Features? Build Ensembles.

2nd Place: Stacking

WHAT CAN I DO NOW?

WHAT ELSE CAN I DO?

Questions?

www.drivendata.org @drivendataorg