Data Science Academy Student Demo day--Richard Sheng, kinvolved school attendance
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The document discusses Kinvolved, a company co-founded to improve student attendance in New York, launched by former educators and advocates in 2012. It highlights the significant impact of poor attendance on dropout rates and associated economic costs, along with data science methods for analyzing attendance patterns. Additionally, it mentions that a large portion of student absenteeism is linked to health issues, particularly asthma.
![ 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$District %in% districts2,]
 school.years <- c("09-10","10-11","11-12","12-13","13-14")
 coln <- c(1:2,which(colnames(ds) %in% school.years))
 df <- ds[coln]](https://image.slidesharecdn.com/richard-140728203004-phpapp01/85/Data-Science-Academy-Student-Demo-day-Richard-Sheng-kinvolved-school-attendance-7-320.jpg)
![ newyork_ds <- paste("new york school district",1:32)
 ds_code <- geocode(newyork_ds)
 df3 <- df[order(df$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)](https://image.slidesharecdn.com/richard-140728203004-phpapp01/85/Data-Science-Academy-Student-Demo-day-Richard-Sheng-kinvolved-school-attendance-8-320.jpg)
Data Science Academy Student Demo day--Richard Sheng, kinvolved school attendance
- 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 [email protected] Data Science and Strategic Analytics TE Connectivity NYC Data Science Academy