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Engineering Student Career Trajectory Data
Education & Learning Analytics
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About
This resource provides detailed insights into the educational journey and career outcomes of future engineers. It tracks academic achievements, including semester-wise Grade Point Averages (GPA) across eight terms, skills developed, engagement in extracurricular clubs, internship experiences, and final job placement status. The data is structured to allow analysis of key factors contributing to student success, including salary expectations reported as Cost to Company (CTC) in Lakhs Per Annum (LPA).
Columns
- Student ID: A unique identifier assigned to each student record (UUID format).
- Name: The student’s name (anonymised for privacy).
- Age: The age of the student in years (mean age is 20.5).
- Gender: Categorical data indicating gender (Male or Female, approximately 50% split).
- Branch: The academic field of study (e.g., MECH, CSE, ECE, CIVIL).
- Average GPA: The calculated mean GPA across all semesters (on a 0-10 scale).
- Backlogs: The total number of courses the student failed.
- Attendance (%): The percentage of classes the student attended (mean is 78.9%).
- Clubs: Extracurricular activities or clubs the student participated in (e.g., Cultural Club, Entrepreneurship Cell).
- Skills: Technical and domain skills acquired (e.g., Python, Machine Learning).
- Internship Done: A Boolean indicating whether the student completed an internship.
- Internship Domain: The field of the internship (if completed).
- Placement Status: Categorical result showing if the student was placed or not placed.
- Placement Domain: The industry or field of the final placement (if applicable).
- CTC (LPA): The annual salary package offered (Lakhs Per Annum), recorded as 0 if the student was not placed.
- Sem1 GPA - Sem8 GPA: The specific GPA score achieved in each of the eight semesters.
- Alumni Path: The student’s plan after graduation (e.g., Job, Higher Studies, Research).
Distribution
The information is contained within the
students.csv file, which is approximately 442.51 kB. The structure includes 24 fields, profiling approximately 2000 distinct student records. The data is optimally structured for analysis in spreadsheet or database environments.Usage
This data is highly valuable for building advanced machine learning models designed to forecast student placement success or predict the likely CTC based on academic and skill inputs. It can also be employed for detailed academic analysis, helping researchers understand longitudinal performance trends across various engineering specialisations. Furthermore, analysts can quantify the relationship between extracurricular activities, specific skills, or internship completion and subsequent career outcomes.
Coverage
The scope is centred on the trajectory of engineering students across various academic branches. Demographically, the data includes age, gender, and branch affiliation. The information is specific to the educational period spanning eight academic semesters leading up to post-graduation and placement. The geographical location or time frame of the data collection is not specified in the current sample.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For training predictive models related to career placement and salary forecasting.
- Academic Researchers: To study educational effectiveness, student engagement, and the correlation between academic performance metrics and professional success.
- University Administrators: For benchmarking student achievement, evaluating curriculum impact, and refining career guidance strategies.
Dataset Name Suggestions
- Engineering Student Career Trajectory Data
- University Placement Prediction Factors
- Student Academic Performance and Job Outcomes
- Future Engineer Success Metrics
Attributes
Original Data Source: Engineering Student Career Trajectory Data
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