Data Science Academy, Student Demo day, Data science by R, Vivian S. Zhang, see www.nycdatascience.com for more details.

1. Increasing NewYork
student attendance with
Kinvolved and Data
Science
Richard Sheng
@rcsheng
Data Science and Strategic Analytics
TE Connectivity
NYC Data Science Academy

2.  Data Science & Strategic Analytics
 Investment Banking Associate
 NYU MBA
 Principal Consultant, SAP Data Science
 Application Engineer
Disclaimer: My views are my own

3.  Kinvolved was Co-founded by a former educator (Teach For
America, NYC, 2008) and a parent advocate. Miriam and
Alex began this journey while graduate students at the
Robert F. Wagner School of Public Service at NYU in 2012.
They completed an accelerator in August 2013, and are
currently based at the Blue Ridge Foundation in Brooklyn,
NY.

4.  Stakeholders:
 Kinvolved
 School Principals
 External funders
 Goals:
 Help drive adoption of Kinvolved’s product to improve
attendance rates, an early predictor of drop-outs
 Impact1:
 Estimated lost lifetime revenue for male dropouts
between the ages of 25 and 34 is approximately $944
billion dollars, and costs associated with poor health and
criminal activity have been estimated at $24 billion
1. Source: http://www.attendanceworks.org/wordpress/wp-content/uploads/2010/04/Schoeneberger_2011.pdf

7.  read.delim("attendance-2009-
2014.csv",as.is=TRUE,header=TRUE,stringsAsFactors=FALSE,fill=TRUE,f
ileEncoding="UTF-16LE")
 Date conversion
 dist_attnd09to14 <- subset(attnd09to14,District==School)
 dist_attnd09to14 <- subset(dist_attnd09to14,City!=District)
 districts2 <- districts[grep("^DISTRICT",districts)]
 ds <- dist_attnd09to14[dist_attnd09to14$District %in% districts2,]
 school.years <- c("09-10","10-11","11-12","12-13","13-14")
 coln <- c(1:2,which(colnames(ds) %in% school.years))
 df <- ds[coln]

8.  newyork_ds <- paste("new york school district",1:32)
 ds_code <- geocode(newyork_ds)
 df3 <- df[order(df$District),c("District","13-14")]
 data <- cbind(df3,newyork_ds,ds_code)
 colnames(data)[2] <- "attnd“
 ds_map <- ggmap(get_googlemap(center = 'new york',
zoom=11,maptype='terrain'),extent='device') +
geom_point(data=data,aes(x=lon,y=lat,colour=attnd,size=1/attnd))+
scale_colour_gradientn(colours=c("red", "blue")) +
scale_size_area() +
labs(title = "NewYork SchoolAttendance - '13 to '14 n" )
 print(ds_map)

9. Kinvolved found that majority of absenteeism of students related to Asthma issues

10.  Level 4: Exceeding the
proficiency standard
 Level 3: Meeting the
proficiency standard
 Level 2: Meeting the
basic standard
 Level 1: Scoring below
the learning standard
 % Proficiency = % Level
3 & 4 / all students

11. Looks fairly similar in problematic areas

12. Just looking at Districts, 67% of exam results variance can be attributed to attendance

13. Q&A
Richard Sheng
@rcsheng
rcsheng@gmail.com
Data Science and Strategic Analytics
TE Connectivity
NYC Data Science Academy