MIT J-WEL project on employment pathways of the future.
All data from the project was stored using MySQL. Data sources were drawn from O*NET and can be downloaded in the zip file.
Analysis was done using Jupyter Notebook.
Demographic data: AgesandWages.ipynb
Career Changer Matrix Modifications: CareerChangerReplication.ipynb
Comparison of Skills: SkillTransitions.ipynb
Other Notebooks: Random stuff and descriptive statistics are contained in other Notebooks...feel free to poke around, I guess.
Pandas: Pandas Dataframe objects were used to manipulate data for visualization and analysis
NetworkX: NetworkX was used to build occupation paths using data from O*NET's Career Changer Matrix as well as to implement Dijkstra' shortest path algorithm.
The main visualization tools used were Plotly and matplotlib. In most cases, Plotly was prefered for its interactivity with data. However, matplotlib was used when Plotly's documentation was actually too trash to use or quick anaylsis was favored over interactivity.
(Yes, I know that some of the code are not the cleanest but it works for the time being)
Email: [email protected]