代做CIS 400、代做Project Proposal、代写Python设计、Java/c++程序代写

日期：2019-03-30 10:59

CIS 400 Project Proposal

Project topic: How far students from top 100 universities are willing to travel for job after

graduation.

Summary of what we would do:

Our sample is the students from the top 100 school in US according to U.S News. We first

randomly select 50 users that were graduated in each school on the list by using LinkedIn API.

By the sample size formula

Where Z= Z value (e.g. 1.96 for 95% confidence level)

p = percentage picking a choice, expressed as decimal

c = confidence interval, expressed as decimal (0.04=±4)

We get we only need 384 people to get the 95% accurate analyze among 1 million

people. We choose 50 people for each school to make it more accurate and avoid

unexpected situation.

Then by using LinkedIn API, we will gather users’ information about their graduation school and

current working place for each user. To further analyze what affects users’ choices, we will also

gather information of users about their major, GPA, internship or work experience, how many

years after graduation, hometown (native or international?). In dealing with data we get, we

would draw some conclusion about the relation between users’ graduation place and their

working place.

Significance of the idea:

By analyze the student’s choice of work city in top 100 Universities, we could predict how far

students will go for work in different university. We would generate the list of the distance and

the student’s university by increasing order, and create the graph of the relationship between

graduate school and present working place. Therefore, we could predict how far and where

students would work after their graduation according to their school and major.

Work Plan

LinkedIn Part

Linkedin API

Find the connection between user’s graduation place and present working place.

Main goal: find the average distance people would travel to find a job. Use google maps to draw a travel

path for each user. (America only)

Step 1: make a table of top 100 US school

50 User per school

school usersA, userB, ….

Step 2: fetch user information

To learn why users choose their current work place, we may consider and fetch those information about

users as follows: major, GPA, internship or work experience, how many years after graduation,

hometown(native or international?).

In addition, we may also consider the weather of related place.

User ID: Graduation School Current working place

Step 3 :

Classification by major, Top five City choice

Google Maps Part

Calculate distance, Illustration.

Step 4:

Use Google Maps api for each school location and company location coordinate

School location coordinate Present work location coordinate distance

Step 5 :Calculate the average distance

School name Average travel distance Maximum travel

distance

% of people who travel

above average

Step 6:

Analyze top five city that students would choose by major and school

Extra jobs:

Use Google API to draw the path that user travels.

Data visualization