• Student Group: Northwestern University Master of Science in Analytics (MSiA)
  • Team Members: Vincent, Emma, Hao, Logan, Brooke
  • Client: Greenwich.hr
  • Tableau Dashboard

Image of US Labor Project Image of US Labor Project Image of US Labor Project

1. Purpose and Objectives

  • Collaborate with Greenwich to do exploratory analysis and visualize the labour market data last year by creating Tableau dashboard.
  • Conduct the geospatial visualize of the job opportunities and salary level of US Labor market based on the Greenwich database.
  • Find out the spatial correlation between job count, salary, and CPI.
  • Find out the correlation between the skills and salarys.

2. Dataset

  • Location data: zip code, address, long/lat
  • Time series: time to post, time to fill
  • Categories: Company, role, tags
  • Response: salary
  • External data:
    1. CPI by city
    2. Population density by zip code

Image of BP Project Image of BP Project

3. Data Cleansing

  • Remove NA first (how to impute the NA): long and lat are missing, impute them by city locations (city and state).
  • Merge three tables based on job-id
  • One job id has different observations with different salary and different locations (average salary, and remain the location and rows)
  • Reduce the data redundancy

4. EDA

  • Summary the response variable: charts (density functions), monthly data, scatter plot
  • Heatmap (nation wise) (city wise: Chicago, NYC, LA)

5. Sanity check

  • One job id has several locations
  • Job from NY CITY, some zipcodes are not in NY.

5. Tools

  • Tableau
  • Spatial Analysis(GIS)
  • Shinny dashboard

6. Business Impacts

  • Understand the US labor market,
  • Recommend recruiting strategy to company.
  • Help job seeker land best job
  • Help Greenwhich.hr find the data mistake from their database