Occupational Skills and Tasks

Understanding the Role of Skills in Online Job Ads

By [source]

About this dataset

This dataset provides an invaluable resource to better understand the connection between occupational skills and related tasks associated with them. Drawing from online job advertisements, it reflects how the range of skills and tasks an individual needs to have within a job role changes over time. The data has been reconciled with the JRC-Eurofound Task Taxonomy, making this dataset a powerful tool for researchers who are looking to understand an occupation's profile and competency requirements. This includes two columns SKILL and TASK which provide descriptors that have been reconciled with the Task Taxonomy respective to their positions respectively. With such insights found in this data, one can not only recognize skilled-based jobs along bettering their hiring practices but also facilitate a more holistic understanding of talent identification during modern recruitment processes

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How to use the dataset

• Get familiar with the two columns - SKILL and TASK. The SKILL column describes skill descriptors found in online job advertisements that have been reconciled with the JRC-Eurofound Task Taxonomy, whilst TASK provides the task for each skill description entry.
• Explore how different occupations rely on different sets of skills/tasks or look into trends over time by examining datasets from different years or by filtering them by type/labour market.
• Consider utilizing data visualization techniques like heat maps in order to more easily recognize patterns in large data sets such as those found in this dataset
• Make sure you check out other similar datasets available on kaggle's platform (e.g., Education, Professional Background), as they may have useful connections or overlap with this one based on common data points like geography/location, occupation type etc..

By following these tips you’ll be able to benefit more fully from this great resource!

Research Ideas

• Analyzing the correlation between specific jobs and growth rate of certain skills over time.
• Examining how certain skills may be trending in a particular job market or industry sector.
• Comparing and contrasting occupational skill profiles between different professions or geographical locations to better allocate resources appropriately for hiring and training purposes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: skill_task_dictionary.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | SKILL | A description of the skill required for the job. (Text) | | TASK | A description of the task associated with the skill. (Text) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

🔹 Overview: This dataset contains 1,000+ synthetic resumes with key details such as skills, experience, education, job roles, certifications, AI screening scores, and recruiter decisions.

🔹 Features:

Resume_ID: Unique identifier Name: Candidate's name Skills: List of relevant technical skills Experience (Years): Total work experience Education: Highest qualification Certifications: Relevant industry certifications Job Role: Target job position Recruiter Decision: Hire or Reject Salary Expectation ($): Expected salary Projects Count: Number of projects completed AI Score (0-100): AI-based resume ranking score 🔹 Use Cases:

Resume screening automation HR analytics & hiring trends Salary prediction models AI-powered hiring research

🚀 Use this dataset to build AI models that can predict hiring decisions, analyze job market trends, or optimize HR processes!

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count : 708736  Domain Name : monster.usa.com  Date Range : 01st Oct 2020 - 31st Dec 2020   File Extension : ldjson

Available Fields : uniq_id, crawl_timestamp, url, job_title, category, company_name, country, post_date, job_description, apply_url, job_board, geo, job_post_lang, html_job_description, inferred_iso2_lang_code, inferred_iso3_lang_code, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, inferred_salary_currency, has_expired, last_expiry_check_date, latest_expiry_check_date, duplicate_status, dataset, is_remote, postdate_in_indexname_format, fitness_score

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud, DataStock and live job data from JobsPikr.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

This information was extracted from the data-science section of naukri.com. It has 1000 jobs in various Data Science fields, together with the necessary skills and pay. The goal is to obtain a thorough study of the market trends and abilities that are in demand in the data science field.

A The dataset was obtained from multiple sources, including surveys, job posting sites, and other publicly available sources. A total of 100 data points were collected. The dataset included five variables: age, experience, job role, and education level and salary

This dataset consists of 10,000 distinct job postings/listings, from 17 different IT-related jobs. Those jobs are mostly related to roles that we typically observe in the data-driven economy of today (data extracted on Feb-2019).

Dataset Columns - ID: A unique number enumerating the jobs extracted - Query: The terms we have used to find the jobs; in other words, the job titles we are search for. - Job Title: The titles of the jobs returned. At this point we should note that the latter returns to the user results that either match exactly the given job title or they are close to it. For example, if we look for “Data Analyst” jobs we will also get “Business Analyst” jobs. - Description: This is the main body of a job offer as it is displayed. The job description is no cleaned or pre-processed.

This dataset contains information on salaries for data science jobs in Karachi, Pakistan. This dataset can be used to gain insights into the salaries offered for data science jobs in Karachi and can be helpful for professionals who are looking to explore career opportunities in this field.

Context

This dataset was created by PromptCloud and Datastock. This dataset has 30K record counts of various data fields. You can download the full dataset here.

Content

This file contains data fields of: - uniq_id, - crawl_timestamp, - URL, - job_title, - company_name, - city, state, - country, - inferred_city, - inferred_state, - inferred_country, - post_date, - job_description, - job_type, - job_board, - geo, - fitness_score 

Acknowledgements

We owe it to the in house web scraping and data mining team at PromptCloud and Datastock.

This dataset provides a comprehensive view of the job market, highlighting the companies and cities that have the highest number of job opportunities.

The Tarta.ai dataset is a valuable resource for anyone interested in the job market and provides a comprehensive view of the employment landscape across different industries and regions.

This dataset was created by Tarta.ai and contains information on the number of jobs by company and city in Utah, with features such as:

• Company name • City • State • Number of active jobs

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

Context

This dataset was created by PromptCloud and Datastock. This dataset has 30K record counts of Job feed data from Indeed.com. You can download the full dataset here.

Content

This File contains the following data fields: - uniq_id, - crawl_timestamp, - URL, - job_title, - category, - company_name, - city, - state, - country, - post_date, - job_description, - company_description, - job_board, - geo, - job_post_lang, - site_name, - domain, - postdate_yyyymmdd, - postdate_in_indexname_format, - inferred_city, - inferred_state, - inferred_country, - fitness_score 

Acknowledgements

We couldn't have made this dataset without the help from our in house web scraping team at PromptCloud and Datastock. We owe it to them.

Inspiration

This dataset was created for those who want to know more about job feed data from USA

Dataset Overview

The "AI and ML Job Listings USA" dataset provides a comprehensive collection of job postings in the field of Artificial Intelligence (AI) and Machine Learning (ML) across the United States. The dataset includes job listings from 2022 to 2024, capturing the evolving landscape of AI/ML job opportunities. This dataset is valuable for researchers, job seekers, and data scientists interested in understanding trends, demands, and opportunities in the AI/ML job market.

Data Science Applications

This dataset can be utilized for various data science applications, including: - Trend Analysis: Identifying trends in job titles, locations, and required skills over time. - Demand Forecasting: Predicting future demand for AI/ML roles based on historical data. - Skills Gap Analysis: Analyzing the skills and experience levels in demand versus the available workforce. - Geospatial Analysis: Mapping job opportunities across different regions in the USA. - Salary Prediction: Developing models to predict salaries based on job descriptions and other attributes. Some job descriptions include salary information, which can be identified by exploring the 'description' column for mentions of compensation, pay, or salary-related terms.

Column Descriptors

1. title: The job title (e.g., AI/ML Engineer).
2. location: The location of the job (e.g., New York, NY).
3. publishedAt: The date the job was published (e.g., 2024-05-29).
4. companyName: The name of the company offering the job (e.g., Wesper).
5. description: A detailed description of the job (e.g., responsibilities, qualifications, and sometimes salary information).
6. applicationsCount: The number of applications received (e.g., Over 200 applicants).
7. contractType: The type of contract (e.g., Full-time).
8. experienceLevel: The level of experience required (e.g., Mid-Senior level).
9. workType: The type of work (e.g., Engineering and Information Technology).
10. sector: The industry sector of the job (e.g., Internet Publishing).

Ethically Mined Data

This dataset has been ethically mined using an API, ensuring no private information has been revealed. Sensitive data, such as the recruiter name, has been removed to protect privacy and comply with ethical standards.

Acknowledgments

• LinkedIn: For providing the platform where these job listings were originally posted.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset provides a rich resource for analyzing and understanding the AI and ML job market in the USA, offering insights into job trends, requirements, and opportunities in this rapidly growing field.

This is a fictional dataset created to help the data analysts to play around with the trends and insights on employee jab satisfaction index.

It has the following attributes. - emp_id - Unique ID - age - Age - Dept - Department - location - Employee location - education - Employee's education status - recruitment_type - Mode of recruitment - job_level - 1 to 5. The job level of the employee. 1 being the least and 5 being the highest position - rating - 1 to 5. The previous year rating of the employee. 1 being the least and 5 being the highest position - onsite - Has the employee ever went to an onsite location? 0 and 1 - awards - No. of awards - certifications - Is the employee certified? - salary - Net Salary - satisfied - Is the employee satisfied with his job?

Disclaimer: This is purely fictional and does not represent any organization.

This dataset provides a comprehensive collection of salary information from various industries and regions across the globe. Sourced from reputable employment websites and surveys, it includes details on job titles, salaries, job sectors, geographic locations, and more. Analyze this data to gain insights into job market trends, compare compensation across different professions, and make informed decisions about your career or hiring strategies. The dataset is cleaned and preprocessed for ease of analysis and is available under an open license for research and data analysis purposes.

Education Level: 0 : High School 1 : Bachelor Degree 2 : Master Degree 3 : Phd

Currency : US Dollar

Senior : It shows that is this employee has a senior position or no.(Binary)

Wuzzuf Job Listings Dataset

This dataset contains 8,000+ job listings extracted from Wuzzuf.net, one of Egypt's leading job search platforms. The data includes detailed job information across various industries and positions in Egypt.

Dataset Features:

• Job Titles: The titles of the job positions listed.
• Company Names: The names of the companies offering the jobs.
• Locations: The geographical locations of the jobs (Cities in Egypt).
• Job Types: The employment type (e.g., Full-time, Part-time, Contract).
• Job Category: Detailed job descriptions, providing insight into the required skills and responsibilities.

Ideal Use Cases:

• Job Market Analysis: Gain insights into trends, demands, and industries.
• Career Prediction Modeling: Build models to predict career paths and job transitions.
• Natural Language Processing (NLP): Analyze and categorize job descriptions for job matching, skill extraction, and more.

This dataset is perfect for researchers, data scientists, and developers interested in exploring job markets, improving recruitment algorithms, or working on NLP tasks related to job search and descriptions.

Additional Notes:

This dataset was scraped and preprocessed to ensure clean, usable data for your analysis. The accompanying Jupyter Notebook outlines the process of web scraping, cleaning, and transforming the data, enabling you to further explore and build on this dataset.

Content

This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated weekly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Quino Al on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Context

This dataset was created by PromptCloud and Datastock. This data holds 30K record counts of job feed data from CareerBuilder.com USA.

The full dataset can be downloaded here.

Content

This file contains the following data:

• Total Records Count : 2213215 
• Domain Name : careerbuilder.usa.com 
• Date Range : 25th Mar 2018 - 31st Dec 2018  
• File Extension : xml
• Available Fields: -- uniq_id, -- crawl_timestamp, -- url, -- job_title, -- category, -- company_name, -- city, -- state, -- post_date, -- job_description, -- job_type, -- company_description, -- contact_person, -- job_board, -- geo

Acknowledgements

We wouldn't be here without the help of the in house team at PromptCloud and Datastock.

Context

This dataset was created by PromptCloud and Datastock. This dataset holds 30K record counts of job feed data from dice.com USA.

You can download the full dataset here.

Content

This file contains the following data fields:

• Total Records Count : 9856000 
• Domain Name : dice.com 
• Date Range : 25th Mar 2018 - 31st Dec 2018  
• File Extension : xml
• Available Fields : -- uniq_id, -- crawl_timestamp, -- url, -- job_title, -- company_name, -- city, -- state, -- post_date, -- job_description, -- job_requirements, -- job_type, -- job_board, -- geo, -- location 

Acknowledgements

We owe it to the Team at PromptCloud and DataStock.

GitHub Issues & Kaggle Notebooks

  Description

GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the StarCoder2 model training corpus, precisely the bigcode/StarCoder2-Extras dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/issues-kaggle-notebooks.

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can come from many different sources and be presented in various formats. Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles.