Data Science Academy Student Demo day--Richard Sheng, kinvolved school attendance
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Increasing New York student attendance with Kinvolved and Data Science Richard Sheng @rcsheng Data Science and Strategic Analytics TE Connectivity NYC Data Science Academy
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Data Science Academy, Student Demo day, Data science by R, Vivian S. Zhang, see www.nycdatascience.com for more details.
Transcript of Data Science Academy Student Demo day--Richard Sheng, kinvolved school attendance
Increasing New York student attendance with Kinvolved and Data Science Richard Sheng @rcsheng Data Science and Strategic Analytics TE Connectivity
NYC Data Science Academy
Data Science & Strategic Analytics
Investment Banking Associate
NYU MBA
Principal Consultant, SAP Data Science
Application Engineer
Disclaimer: My views are my own
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.
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 $24 billion
1. Source: http://www.attendanceworks.org/wordpress/wp-content/uploads/2010/04/Schoeneberger_2011.pdf
read.delim("attendance-2009-2014.csv",as.is=TRUE,header=TRUE,stringsAsFactors=FALSE,fill=TRUE,fileEncoding="UTF-16LE")
Date conversion dist_attnd09to14 <- subset(attnd09to14,District==School) dist_attnd09to14 <- subset(dist_attnd09to14,City!=District) districts2 <- districts[grep("^DISTRICT",districts)] ds <- dist_attnd09to14[dist_attnd09to14$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]
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 = "New York School Attendance - '13 to '14 \n" )
print(ds_map)
Kinvolved found that majority of absenteeism of students related to Asthma issues
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
Looks fairly similar in problematic areas
Just looking at Districts, 67% of exam results variance can be attributed to attendance
Q&A Richard Sheng @rcsheng [email protected] Data Science and Strategic Analytics TE Connectivity
NYC Data Science Academy
