Leverage LLMs to provide career insights based on user-created data and local documents.
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Updated
Feb 3, 2024
Leverage LLMs to provide career insights based on user-created data and local documents.
Relationships between highly-related occupations to Skillset
U.S. Bureau of Labor Statistics: Occupational Employment
Deep dive analysis into analytics-related career and occupations. Alternate website: https://colab.research.google.com/github/PRiauwindu/Analytics-Occupation-Deep-Dive/blob/main/United%20States%20Analytics%20Occupations%20Deep%20Dive%20.ipynb#scrollTo=ac95be20
NameSpy - REST API service
A horizontal calendar implemented as Vue.js components
Python App to Query Wikidata and Convert Result to Excel Workbook
Plain text lists of people, places, & things
The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and sleep disorders.
Experiments for use of labour market statistics from O*Net online
SILKC is an application meant to offer training recommendations based on users' profiles, including their skills and professional experiences, to reach their stated professional goal
A JSON version of the 2018 Standard Occupational Classification (SOC) system published by the United States Department of Labor Bureau of Labor Statistics. All categorisations (broad, major, minor, and detailed) are included in the JSON.
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