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University Employability Dataset
Education & Learning Analytics
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About
This dataset provides detailed placement data for students from an XYZ campus. It serves as a valuable resource for understanding the various academic and employability factors that influence a student's placement status and salary [1]. Originally provided by faculty at Jain University Bangalore for practical sessions, it allows for the exploration of key questions such as: which factors most impact a candidate's placement, the significance of academic percentages in securing employment, and which degree specialisations are most sought after by corporates [1, 2].
Columns
The dataset contains 15 columns, each detailing specific attributes of the students:
- sl_no: A serial number for each student record [3].
- gender: Indicates the student's gender, either Male ('M') or Female ('F') [3].
- ssc_p: The percentage obtained in Secondary School Certificate (10th Grade) [4].
- ssc_b: The Board of Education for Secondary School (Central or Others) [4].
- hsc_p: The percentage obtained in Higher Secondary Certificate (12th Grade) [5].
- hsc_b: The Board of Education for Higher Secondary (Central or Others) [6].
- hsc_s: The specialisation chosen in Higher Secondary Education (Commerce, Science, or Other) [6].
- degree_p: The percentage obtained in their Undergraduate Degree [6].
- degree_t: The type or field of their undergraduate degree (Comm&Mgmt, Sci&Tech, or Other) [7].
- workex: A boolean indicator for whether the student has prior work experience [7].
- etest_p: The percentage obtained in the employability test conducted by the college [8].
- specialisation: The specialisation chosen during their Post Graduation (MBA), either Marketing & Finance (Mkt&Fin) or Marketing & Human Resources (Mkt&HR) [8].
- mba_p: The percentage obtained in their MBA programme [9].
- status: The placement status of the student, indicating if they were 'Placed' or 'Not Placed' [9].
- salary: The salary offered by the corporate to the placed candidates [10].
Distribution
The dataset is provided in CSV format (
Placement_Data_Full_Class.csv) and is 19.71 kB in size [3, 11]. It contains 215 unique records across its 15 columns [3]. While most columns have data for all 215 records, the 'salary' column has 148 valid records, with 67 instances where salary information is missing (representing 31% of the total) [10].Usage
This dataset is ideal for:
- Training and practical sessions in programming languages like Python and R, particularly for data analysis and machine learning tasks [1, 2].
- Conducting various statistical tests to identify significant correlations and predictive factors for student placement [2].
- Analysing the influence of academic performance (e.g., secondary, higher secondary, degree, and MBA percentages) on placement success [1, 2].
- Determining the demand for specific degree specialisations by corporate recruiters [2].
- Investigating the impact of employability test scores and work experience on placement outcomes [1].
Coverage
The data covers students from a single campus, identified as an "XYZ campus" [1]. Demographically, it includes male (65%) and female (35%) students [3, 4]. The dataset captures academic performance from secondary school (10th grade) through higher secondary (12th grade), undergraduate degrees, and postgraduate (MBA) programmes [1]. It also includes details on board of education, specialisations, work experience, and employability test scores [4-8]. Placement status and salary offers for placed students are included [1].
License
CC0: Public Domain
Who Can Use It
This dataset is highly beneficial for:
- Students: For hands-on experience with real-world data in academic projects and programming exercises [1].
- Educators and Professors: To provide practical case studies for teaching data analytics, statistics, and business intelligence [1].
- Data Analysts and Scientists: To build predictive models for student placement, identify key influencing factors, and perform in-depth statistical analysis [2].
- Career Counsellors: To gain insights into factors that contribute to successful placements and advise students accordingly [2].
- Educational Institutions: To assess the effectiveness of their placement drives, understand market demands, and refine their curriculum to better prepare students for employment [2].
Dataset Name Suggestions
- Student Campus Placement Dynamics
- Graduate Recruitment Success Predictors
- University Employability Dataset
- Academic Background and Job Placement Data
- Indian Student Placement Analytics
Attributes
Original Data Source: University Employability Dataset
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