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Degrees That Pay You Back

Explore the salary potential of college majors with a k-means cluster analysis.

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  • 12 tasks
  • 565 participants
  • 1,500 XP

Project Description

Wondering if that Philosophy major will really help you pay the bills? Think you're set with an Engineering degree? Whether you're in school or navigating the postgrad world, this project will guide you in exploring the short- and long-term financial implications of this major decision. After doing some data clean up, you'll compare the recommendations from three different methods for determining the optimal number of clusters, apply a k-means cluster analysis, and visualize the results.

This project assumes familiarity with standard tidyverse tools for R. While not required, it would also be helpful to have a prior understanding of clustering unsupervised data with k-means. To learn or review these skills, check out Introduction to the Tidyverse and Cluster Analysis in R.

The dataset used in this project is made available here by the Wall Street Journal.

Project Tasks

  • 1Which college majors will pay the bills?
  • 2Currency and strings and percents, oh my!
  • 3The elbow method
  • 4The silhouette method
  • 5The gap statistic method
  • 6K-means algorithm
  • 7Visualizing the clusters
  • 8A deeper dive into the clusters
  • 9The liberal arts cluster
  • 10The goldilocks cluster
  • 11The over achiever cluster
  • 12Every major's wonderful
Instructor Avatar
Jaclyn Burge

Senior Data Consultant at The Walt Disney Company

Jaclyn has a degree in Cognitive Science from the University of California, Berkeley, where she helped teach The Beauty and Joy of Computing and worked on a tutoring startup. By day, Jaclyn is a fortune teller for The Walt Disney Company (in other words, she derives insights from data to forecast digital media consumption). By night, she is a competitive martial artist, an avid visual artist, and a voracious reader.

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Technology

  • R LogoR
  • Topics

    Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data