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Academic Foundations of Success
Education & Learning Analytics
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About
The academic journeys of distinguished individuals across various fields, including entrepreneurship, science, politics, entertainment, sports, and activism. This dataset collects key academic details for each figure, such as their highest completed degree or current enrolment status, field of study, awarding institution, graduation year, location, institutional ranking, academic performance metrics, and any notable scholarships or honours received. By structuring these educational paths, the dataset offers clear, cross-comparable insights into the academic foundations that often underpin world-renowned success. It features profiles spanning diverse professions, from Nobel laureates to Olympic champions, and includes rich academic metadata like institution country, global ranking, and GPA. Both completed degrees and alternative routes to success, such as in-progress studies or "dropped out" statuses, are noted.
Columns
- Name: The full name of the individual profiled. This serves as the primary identifier for each record and is unique within the dataset.
- Profession: The individual's primary occupation or role for which they are well-known, aiding in categorisation and analysis.
- Degree: The highest academic degree attained or their current enrolment status (e.g., Bachelor, PhD, or "Dropped Out"), capturing formal education and non-traditional paths.
- Field: The main area(s) of study or specialisation, allowing for examination of academic disciplines linked to later achievements.
- Institution: The name of the university or school that awarded the degree, providing context on institutional pedigree and geographical distribution.
- Graduation Year: The year the degree was conferred or expected, useful for temporal analyses and cohort comparisons.
- Country: The nation where the educational institution is located, supporting geographic segmentation of academic backgrounds.
- University Global Ranking: An approximate world ranking of the institution (e.g., QS/The Times), enabling assessment of the link between institutional prestige and individual achievement.
- GPA (or Equivalent): The grade point average or a comparable academic performance metric, offering quantitative insight into academic achievement where available.
- Scholarship/Award: Any significant scholarships, fellowships, or academic honours received, highlighting exceptional recognition during their education.
Distribution
The dataset is provided in a CSV-friendly structure. It contains 125 records and includes 10 distinct columns. The file size is 13.13 kB.
Usage
This dataset is ideal for:
- Educational Research: Comparing different academic paths to understand their correlation with subsequent accomplishments.
- Policy & Guidance: Informing educators and career counsellors about typical institutional and field-of-study patterns among high achievers.
- Visualisation & Storytelling: Creating engaging charts, for instance, on the frequency of attendance at highly-ranked universities or average academic performance metrics among successful individuals.
- Machine Learning & Clustering: Grouping profiles based on educational attributes to uncover underlying patterns or segments, such as those with STEM versus business-oriented academic backgrounds.
Coverage
The dataset profiles distinguished figures from various professional domains, including entrepreneurship, science, politics, entertainment, sports, and activism. The institutions featured span multiple countries globally, with a notable presence from the USA and China, among others. The time range of graduation years varies, including studies from different periods, and notes both completed degrees and in-progress or dropped-out statuses. It focuses on the academic journeys of successful people, offering insights into diverse paths to achievement.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Academics and Researchers: To study educational trends and their impact on career success.
- Career Counsellors and Educators: To guide students by showcasing educational patterns of high achievers.
- Data Analysts and Visualisation Experts: To identify and present trends in elite education.
- Machine Learning Engineers: To build models for clustering or predicting success based on academic profiles.
Dataset Name Suggestions
- Eminent Achievers' Academic Paths
- Success Pathways Education
- High Achievers Academic Profiles
- Global Leaders Educational Journeys
- Academic Foundations of Success
Attributes
Original Data Source: Academic Foundations of Success
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