The purpose of this study is to develop a system which will assist us to determine the Earnings generated by international students during their study years in USA. We have examined the relationship between New International Enrollments and Institutional Fees at Public Colleges, Universities and institutions in the USA. This involves examining large scale data of international students coming to USA every year and thereby calculating the revenue generated by them while being on F1 visa. The broad purpose of this study is to understand the economic impact of increased international student enrollment on net revenue generation in USA. In this paper, we classify the impact of international students on revenue generation and employment opportunities in different states of US. Furthermore, we implement the data analysis systems using Hive, PyHive in Azure cloud computing. Keywords: Data Analysis, Hive, Pyhive, Azure

1. Revenue & employment Analysis of
International Students in USA
Team Members: Priyanka Kale, Apekshit Bhingardive, Aditya Verma
Guide: Dr. Jongwook Woo
24th Annual Student Symposium, CSULA
26th February 2016

2. What is Big Data?
 Big data is a term that describes the large volume of data – both structured and
unstructured – that inundates a business on a day-to-day basis.
 It's not the amount of data that's important. It's what we do with the data that
matters.
 Machine Learning: big data often doesn't ask why and simply detects patterns.
 Digital footprint: big data is often a cost-free byproduct of digital interaction.

3. Purpose of Analysis
 To develop a system which will assist
us to determine the revenue
generated by international students.
 Examining the relationship between
new international enrollments and
institutional income at public
colleges, universities and
professional organizations in the US.

4. Continued..
 To understand the effects of increased international student enrollment
on net revenue generation in US
 Find out the income from Universities
 Predict the impact of international students on revenue generation
 Predict employment opportunities in the US

5. • Basic formula for calculating economic Benefit

6. Analysis is done using:
 Analysis on huge data is done using the Hadoop File system (HDFS)
 Hadoop environment using Horton Sandbox on Azure
 Using Python and HIVE [Pyhive] – iPython Notebook
 HUE
 Google Fusion tables
 WEKA Framework

7. Loading data into HDFS:
 File has been uploaded using Hadoop command line Interface

8. Hortonworks Sandbox configuration
Number of nodes: 3 Size : Basic A4 with 8 cores 14 Gb memory

9. Creating tables in HUE from existing data

10. Connecting HIVE through Python
 Using Ipython notebook for writing the python code
 Embedding HiveQL inside python code.

11. Executing the Hive script from python code:

12. Visualizing data with Graphs
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ALABAMA
ALASKA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
DISTRICTOFCOLUMBIA
FEDERATEDSTATESOFMICRONESIA
FLORIDA
GEORGIA
GUAM
HAWAII
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
LOUISIANA
MAINE
MARSHALLISLANDS
MARYLAND
MASSACHUSETTS
MICHIGAN
MINNESOTA
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEWHAMPSHIRE
NEWJERSEY
NEWMEXICO
NEWYORK
NORTHCAROLINA
NORTHDAKOTA
OHIO
OKLAHOMA
OREGON
PALAU
PENNSYLVANIA
PUERTORICO
RHODEISLAND
SOUTHCAROLINA
SOUTHDAKOTA
TENNESSEE
TEXAS
Billions
TOTAL EARNING FROM FEES

13. Major earning states
California,
9.55%
New York,
10.84%
Pennsylvania,
7.36%
Percentage of total income
California
New York
Pennsylvania

14. Visualizing Data in Google Fusion Tables

15. Supervised Learning using Classification:
 WEKA framework has been used to classify the states depending on there
total value of earnings.
 UserClassifier Algorithm provided by WEKA tool has been used to generate
graph of classification.
 Final outcome of the Hive script executed in python has been processed
using above mentioned algorithm.

16. Continued..
The class color differentiate the states into categories : For instance New York lies
in orange color zone with being the among the top revenue generating state

17. Value Proposition:
 International Students mobility trends: By 2017, the global
middle class is projected to increase its spending on
educational products and services by nearly 50 percent.
 Institutions can take this growth into consideration!
 United States a more welcoming nation!

18. Predictive Modelling:

19. Employment Analysis – How ?
 Finding data where international student work after their graduation
 Based on the number students employed in current and past years
 Number of employers hiring international students in every filed of the grad
study [Job positions]

20. References :
 https://nces.ed.gov/ipeds/datacenter/
 https://github.com/priya708/Project-528
 https://gitlab.com/Addylad/Project528BigData/tree/47b3e6469bff4e9b7cbe0d743cb8ad95
20dbb786/DataSource
 https://cwiki.apache.org/confluence/display/Hive/Tutorial
 https://hortonworks.com/tutorials
 http://www.nafsa.org/


21. Thank You!