Career Radar

#Data Visualization #User Experience Design
Project Overview
Career Radar is an online platform designed to assist current and future professionals in comprehending their place in an ever-changing job market and in discovering alternative career trajectories.
To view the interactive 3D data visualization:
https://lmxy0212.github.io/OccupationExplorer/
 
This project explored the convergence of data processing, three-dimensional and two-dimensional data visualization, and user experience design, utilizing data from the Occupational Information Network (O-NET), a database supported by the US Department of Labor.
Backgound
We live in an era where job transitions are increasingly frequent and the labor market is in a constant state of flux.

In the United States, the average worker changes jobs every 4.1 years, a rate that has been accelerating over the past decade. This trend is even more pronounced among workers aged 25 to 34, who switch companies approximately every 2.8 years. Moreover, a staggering 52% of U.S. workers are contemplating a job change, with nearly 29% having completely shifted their career fields since their first post-college job.

These statistics underscore a significant challenge: the daunting task of navigating career changes, often hindered by a lack of clear opportunities or resources.
Critical Questions
  • How are various occupations interconnected?
  • How do I compare to a particular occupation?
  • How should I prepare to make my next career move?
Solutions
  • Standardized job evaluation system for easy comparison between different occupations.
  • Analytical tool to identify job similarities and correlations.
  • User-friendly interface for comparing and contrasting jobs.
  • Feature to discover similar or distinct job options based on user preferences.

Processes

Matrices Creation
  • Matrix Creation: Developed matrices for five key job-related categories - Skills, Knowledge, Work Tasks, Interests, and Values.
  • Score Calculation: Updated each category score using the O-NET system, multiplying importance by required level, and storing results in new matrices.
  • Score Normalization: Standardized all scores on a 0 to 10 scale for uniform comparison.
  • Dataset Sorting: Organized the dataset by job title to align score matrices for consistency.
Distance Calculation
  • Euclidean Distance Calculation: Computed N-dimensional Euclidean distances between every pair of jobs for each normalized matrix.
  • Distance Normalization: Standardized all calculated distances on a 0 to 10 scale.
  • Weighted Sum Application: Applied weighted sums to distances, reflecting the importance of each factor for personalized results.
  • Distance Filter: Extracted the top 7000 nearest jobs based on calculated distances.

Brainstorm, Sketch, and Prototype

Macro Level Visualization：Personalized 3D Network of Occupations

Force Directed Graph
  • Dynamic Graph Construction: Created force-directed graphs from distance matrices, with each graph varying due to different weight vectors.
  • Job Nodes and Similarity Links: Each node represents a job, and links indicate job similarities.
  • Clustering and Sparsity: Clusters represent groups of similar jobs, while sparse areas in the graph denote greater job differences.

Macro Level Visualization：Interactive Force-Directed Graph

Interactivity
  • Interactive Node Selection: Clicking a node highlights it and its immediate neighbors, while dimming others, allowing users to clearly identify the selected job and its similar counterparts.
  • Dynamic Re-centering: The graph dynamically reorients around the selected node, updating highlights to reflect current selections.

Mezzo Level Visualization

Visualization Explained
  • Mezzo-Level Visualization: 'Subgraph' illustrates job relationships within the larger 3D network, providing a focused view.
  • Inner and Outer Circles: Features an inner circle for occupations closely linked to the user's current job, and an outer circle for a broader range of relevant job options.
  • Compact Graph Overview: Includes a smaller version of the graph (bottom left) for a wider perspective, offering clear insights into job interconnections at a glance.

Micro Level Visualization

Visualization Explained
  • Radar Chart Visualization: Offers an intuitive method for comparing multiple variables, highlighting strengths and weaknesses.
  • Similarity and Difference Indicators: Overlapping areas show similarities, while non-overlapping sections reveal differences.
  • Interactive Detail View: Clicking on a radar chart allows users to access a detailed comparison, presenting additional parameters for analysis.
Additional Features
Future Features
  • GenAI Integration: Future iterations will include GenAI to create personalized reports from existing data.
  • Detailed Comparison Reports: These reports will compare a user's profile with their desired job, providing clear, actionable insights.
  • External Resource Links: Reports will feature links to external resources, aiding users in transitioning to new positions.
  • Comprehensive Summary: Aimed at offering a complete overview, equipping users with vital information for a smoother career path change.
  • Enhanced User Experience: By leveraging advanced technology, the platform will provide tailored guidance for navigating the evolving job market.
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