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Global Workforce Resume Dataset
Data Science and Analytics
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Free
About
This dataset, curated and processed by Neuralframe AI, serves as a valuable resource for resume parsing, candidate profiling, and job matching applications. It includes structured information on career objectives, skills, education, work experience, certifications, and other pertinent details. The data has been collected from both open-source platforms and Neuralframe AI's proprietary sources, with all data obtained with explicit consent. The dataset was initially utilised in the Datathon Competition at Bitfest 2025, offering participants a practical dataset to develop and refine resume parsing algorithms and candidate evaluation systems.
Columns
The dataset contains 35 columns. Key columns include:
- address: Candidate's address (if available).
- career_objective: A brief summary of the candidate's career goals or objectives.
- skills: A list of skills possessed by the candidate, such as technical and soft skills.
- educational_institution_name: Names of educational institutions attended by the candidate.
- degree_names: Degrees obtained by the candidate (e.g., B.Tech, MBA).
- passing_years: Year(s) of graduation or programme completion.
- educational_results: Results or grades achieved in educational qualifications, such as GPA, percentage, or division.
- result_types: The format or type of the educational results, such as GPA, percentage, or classification (e.g., Distinction).
- major_field_of_studies: The main fields or subjects studied during the candidate’s education (e.g., Computer Science, Mathematics).
- professional_company_names: Names of the companies or organisations where the candidate has worked professionally.
Distribution
- Filename:
resume_data.csv - Format: CSV (Comma-Separated Values)
- Size: 17 MB
- Number of Columns: 35
- Number of Rows: 9544
Usage
This dataset is ideal for:
- Developing and refining resume parsing algorithms.
- Creating candidate profiling systems.
- Building job matching applications.
- Enhancing candidate evaluation systems.
- Research in natural language processing (NLP) and machine learning on textual data.
Coverage
The dataset's region coverage is global. Specific details regarding time range or detailed demographic scope are not explicitly provided within the available information.
License
CC-BY
Who Can Use It
This dataset is particularly useful for:
- Data Scientists and Analysts: For building predictive models and extracting insights from resume data.
- Machine Learning Engineers: For training and testing NLP models for text analysis on resumes.
- HR Professionals and Recruiters: For automating aspects of candidate screening and matching.
- Academic Researchers: For studies related to human resources, labour markets, or AI applications in recruitment.
- Participants in Datathons and Competitions: Seeking a practical dataset for developing real-world solutions.
Dataset Name Suggestions
- Candidate Profile Dataset
- Resume Data for AI Models
- Global Workforce Resume Data
- Structured Career Data
- Job Applicant Skills Dataset
Attributes
Original Data Source: Resume Dataset
QUALITY