Admission into graduate school data. This dataset has a binary response (outcome, dependent) variable called admit. There are three predictor variables: gre, gpa and rank.

With the enactment of the Higher Education Opportunity Act (HEOA) of 2008, five Predominantly Black Institutions are eligible to receive funding to improve graduate education opportunities at the master’s level in mathematics, engineering, physical or natural sciences, computer science, information technology, nursing, allied health or other scientific disciplines where African American students are underrepresented. Types of Projects Institutions may use federal funds for activities that include: Purchase, rental or lease of scientific or laboratory equipment for educational purposes, including instructional and research purposes; Construction, maintenance, renovation and improvement in classroom, library, laboratory and other instructional facilities, including purchase or rental of telecommunications technology equipment or services; Purchase of library books, periodicals, technical and other scientific journals, microfilm, microfiche, and other educational materials, including telecommunications program materials; Scholarships, fellowships, and other financial assistance for needy graduate students to permit the enrollment of students in, and completion of a master’s degree in mathematics, engineering, physical or natural sciences, computer science, information technology, nursing, allied health, or other scientific disciplines in which African Americans are underrepresented; Establishing or improving a development office to strengthen and increase contributions from alumni and the private sector; Assisting in the establishment or maintenance of an institutional endowment to facilitate financial independence pursuant to Section 331; Funds and administrative management, and the acquisition of equipment, including software, for use in strengthening funds management and management information systems; Acquisition of real property that is adjacent to the campus in connection with the construction, renovation, or improvement of, or an addition to, campus facilities; Education or financial information designed to improve the financial literacy and economic literacy of students or the students’ families, especially with regards to student indebtedness and student assistance programs under title IV; Tutoring, counseling, and student service programs designed to improve academic success; Faculty professional development, faculty exchanges, and faculty participation in professional conferences and meetings; and Other activities proposed in the application that are approved by the Secretary as part of the review and acceptance of such application.

Mexico Migration Statistics (MS): Number of Admissions data was reported at 46,286,424.000 Person in 2024. This records an increase from the previous number of 43,923,184.000 Person for 2023. Mexico Migration Statistics (MS): Number of Admissions data is updated yearly, averaging 23,482,081.500 Person from Dec 1989 (Median) to 2024, with 36 observations. The data reached an all-time high of 46,286,424.000 Person in 2024 and a record low of 7,334,632.000 Person in 1992. Mexico Migration Statistics (MS): Number of Admissions data remains active status in CEIC and is reported by Ministry of Interior. The data is categorized under Global Database’s Mexico – Table MX.G012: Migration Statistics: Number of Admissions: Annual.

The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants’ resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.

Dataset

This dataset was created by Noyeem Hossain

Released under Apache 2.0

Contents

Number of Canadian students in a master's degree entry cohort belonging to a visible minority group, by student characteristics.

Central Police University 111 academic year research master's and doctoral classes, as well as screening and recruitment re-examination results statistics table.

The dataset contains ranking-and-academic-year-, state-, and university/institute-wise compiled data on the total number of admitted, graduated and placed students of post and graduate students, along with additional details such as number of first year students intaken, number of admitted students through lateral entry, number of students selected for higher studies and median salary of students selected for placements, etc., as per the National Institutional Ranking Framework (NIRF) data

These data are calculated based on the year of cohort, that is, the academic year in which a group of students began university studies. In each COHORTE COURSE all data (including graduation data) are referenced to the year in which studies were initiated in order to track students who started studies at the same time. Graduate students collect the number of students from a new entry cohort who have completed all curriculum credits, regardless of the year they finished. Time Graduate Students is the number of students in a new entry cohort who graduate on schedule or one more year. Dropout rate is the percentage of students from a new-income cohort who had to obtain the degree in the intended academic year, according to the duration of the plan, and who have not enrolled either in that academic year or in the next (in the degrees with a planned duration of 1 year, the two consecutive years of non-enrollment that is they account for the calculation are two years after the new income). Initial Abandonment Rate is the percentage of students in a new-income cohort who, without obtaining a degree, do not enroll in the study either of the two academic years following that of entry. Graduation rate is the percentage of students who complete teaching in the expected time or in one more year relative to their incoming cohort. NOTE: the fees exclude students who have recognised (or adapted or validated) more than 15 % of the credits of the curriculum and students enrolled in the part-time modality in any of the years studied.

Dataset

This dataset was created by Fatima

Contents

Introduction This STEM advising outreach program was developed for undergraduate students who are contemplating future applications to PhD programs in the life sciences. The audience of ~20 students ranged in academic stage, and was composed mostly of life sciences undergraduates enrolled at Bowdoin College.

We have previously described two similar outreach events (ref. 1,2); this 90-minute combination of seminar and discussion built on that pilot program. This session at Bowdoin College was intended to complement the advising that students receive from their primary research mentors on campus. Although undergraduates at many excellent institutions have access to extensive pre-professional advising for careers in medicine, law and some other directions, the structure of advising for scientific research and the many career options that rely on PhD training is less consistent. Independent study or thesis research mentors are often a student’s primary source of advice. Career advisors have confirmed that reiteration and reinforcement of advising principles by professionals external to the school environment is helpful. Therefore, this outreach program’s content was developed with a goal of demystifying PhD programs and the benefits that they provide. The topics covered included (a) determining the key differences between programs, (b) understanding how PhD admissions works, (c) preparing an effective application, (d) proactive planning to strengthen one’s professional portfolio (internships, independent research, cultivating mentors), (e) key transferable skills that most students learn in graduate school, (f) what career streams are open to life science PhDs, and, (g) some national and institutional data on student career aspirations and outcomes (ref. 3). Methods The approach of bringing a faculty member and an administrative staff member who both have life science PhD training backgrounds was intentional. This allowed the program to portray different perspectives and experience to guide student career development, while offering credible witnesses to the types of experiences, skills and knowledge gained through PhD training. Central to the method of this outreach program is the willingness of graduate educators to meet the students on their own ground. The speakers guided students through a process of identifying national graduate programs that might best serve their individual interests and preferences. In addition to recruiting prospective applicants to Harvard Medical School (HMS) summer internships and PhD programs, the speakers made an explicit appeal to students to hone their professional portfolio proactively by discussing important skills that undergraduates need to be competitive in admissions and the career workplace including acquiring training in statistics and programming, soliciting diverse mentorship, acquiring authentic research experiences/internships, conducting thesis research, and obtaining fellowships). By reinforcing much of the anecdotal and formal advising content that is made available by faculty mentors and career counselors, our host saw the value of external experts to validate guidance.

This event built off our most recent event (ref. 2); we delivered a presentation covering the relevant topics and transitioned into an open discussion featuring a third visitor in our team. In contrast to the aforementioned previous event, the time constraint at lunch time prevented us from doing a formal panel. Our third speaker was a HMS Curriculum Fellow (ref. 4) whose career goals included teaching at a comparable institution (primarily undergraduate institution, PUI).

Students were encouraged to have lunch during the session, as the program was held at midday to avoid conflicts with other academic or extracurricular events. ResultsAs the principal goal of the session was to encourage and engage students, not to evaluate them, and the students ranged widely in stage and long-term career objectives, there were no assessment surveys of learning gains. Informally, student engagement was excellent as judged by the frequency and thoughtful nature of questions asked during the discussion phase of the session. Ad hoc student feedback directly following the event was extremely positive, as was our host’s follow up by email after the event. The success of the program was also evident by an invitation for a repeat of the program or other forms of collaboration in the future, including the possibility of reciprocal visits to HMS.DiscussionThis advising session was a continued refinement of our prototype, and thus served to prepare us for a series of similar events across a larger network of colleges. Our decision to incorporate a HMS Curriculum Fellow served three purposes: (1) to engage speaker who pursued doctoral training at three different institutions (UCLA, Tufts University, Harvard University), (2) to broaden the range of career trajectories presented as outcomes from doctoral programs, and (3) to provide networking and career development opportunities for the Curriculum Fellow.

The dataset contains year-, iit-, programme-of-study-, course-of-study-, gender- and community-wise number of admissions made in different Indian Institutes of Technology (IITs) in India.

Notes:

1. The data covered is of the 14 IITs which have provided data, namely IIT-Bhilai, IIT-Dhanbad, IIT-Dharwad, IIT-Gandhinagar, IIT-Goa, IIT-Hyderabad, IIT-Indore, IIT-Jammu, IIT-Madras, IIT-Palakkad, IIT-Patna, IIT-Ropar, IIT-Tirupati, and IIT-Bhubaneswar.

2. For the sake of brevity and removing duplictes in the datasets, whereever there wer 'zero (0)' admissions for different types of coureses, the pertinent rows were removed.

3. The different types of communities covered in the dataset include:

EWS: Economically Weaker Section, GEN: General Category, GEN - EWS: General Category - Economically Weaker Section, OBC: Other Backward Classes, OBC - NCL: Other Backward Classes - Non-Creamy Layer, SC: Scheduled Caste, ST: Scheduled Tribe, PWD: Persons with Disabilities, GEN - PWD: General Category - Persons with Disabilities, GEN - EWS - PWD: General Category - Economically Weaker Section - Persons with Disabilities, OBC - NCL - PWD: Other Backward Classes - Non-Creamy Layer - Persons with Disabilities, SC - PWD: Scheduled Caste - Persons with Disabilities, ST - PWD: Scheduled Tribe - Persons with Disabilities, EWS - PD: Economically Weaker Section - Persons with Disabilities, Others: Other categories not covered by specific reservations, OBC - PWD: Other Backward Classes - Persons with Disabilities, OBC - CL: Other Backward Classes - Creamy Layer

These data are calculated based on the year of cohort, that is, the academic year in which a group of students began university studies. In each COHORTE COURSE all data (including graduation data) are referenced to the year in which studies were initiated in order to track students who started studies at the same time. Graduate students collect the number of students from a new entry cohort who have completed all curriculum credits, regardless of the year they finished. Time Graduate Students is the number of students in a new entry cohort who graduate on schedule or one more year. Dropout rate is the percentage of students in a new-income cohort who had to earn the degree in the intended academic year, according to the duration of the plan, and who have not enrolled in either that academic year or the next. Initial Abandonment Rate is the percentage of students in a new-income cohort who, without obtaining the degree, do not enroll in the study either of the two academic years following the entry Rate of Graduation Percentage of students who complete teaching in the expected time or in one more year relative to their incoming cohort. The fees exclude students from grade adaptation courses, students who have recognised (or adapted or validated) more than 15 % of the credits of the curriculum and students enrolled in the part-time modality in any of the years studied.

The Premedical SMP Data Collection file contains the responses obtained from the survey that is provided in the supporting information section. The file consists of two spreadsheets: one spreadsheet is for respondents that attended a SMP prior to medical school (SMP Students tab) and one for those that did not (Traditional Students tab).

Mexico MS: No. of Admissions: Foreigners: Resident: Permanent data was reported at 36,267.000 Person in Mar 2025. This records an increase from the previous number of 31,540.000 Person for Feb 2025. Mexico MS: No. of Admissions: Foreigners: Resident: Permanent data is updated monthly, averaging 10,997.000 Person from Jan 2002 (Median) to Mar 2025, with 279 observations. The data reached an all-time high of 50,962.000 Person in Jan 2025 and a record low of 537.000 Person in May 2006. Mexico MS: No. of Admissions: Foreigners: Resident: Permanent data remains active status in CEIC and is reported by Ministry of Interior. The data is categorized under Global Database’s Mexico – Table MX.G011: Migration Statistics: Number of Admissions.

Study population descriptive statistics.

AYUSH: Tamil Nadu: Post Graduate: Admission Capacity: Siddha data was reported at 94.000 Person in 2016. This stayed constant from the previous number of 94.000 Person for 2015. AYUSH: Tamil Nadu: Post Graduate: Admission Capacity: Siddha data is updated yearly, averaging 94.000 Person from Mar 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 126.000 Person in 2010 and a record low of 80.000 Person in 2011. AYUSH: Tamil Nadu: Post Graduate: Admission Capacity: Siddha data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Health Sector – Table IN.HLE006: AYUSH: Health Education: Post Graduate.

Background and Objective: Every year thousands of applications are being submitted by international students for admission in colleges of the USA. It becomes an iterative task for the Education Department to know the total number of applications received and then compare that data with the total number of applications successfully accepted and visas processed. Hence to make the entire process easy, the education department in the US analyze the factors that influence the admission of a student into colleges. The objective of this exercise is to analyse the same.

Domain: Education

Dataset Description:

Attribute Description GRE Graduate Record Exam Scores GPA Grade Point Average Rank It refers to the prestige of the undergraduate institution. The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Admit It is a response variable; admit/don’t admit is a binary variable where 1 indicates that student is admitted and 0 indicates that student is not admitted. SES SES refers to socioeconomic status: 1 - low, 2 - medium, 3 - high. Gender_male Gender_male (0, 1) = 0 -> Female, 1 -> Male Race Race – 1, 2, and 3 represent Hispanic, Asian, and African-Americ

Mexico MS: No. of Admissions: Locals data was reported at 786,081.000 Person in Mar 2025. This records an increase from the previous number of 676,109.000 Person for Feb 2025. Mexico MS: No. of Admissions: Locals data is updated monthly, averaging 441,591.000 Person from Jan 2002 (Median) to Mar 2025, with 279 observations. The data reached an all-time high of 1,149,557.000 Person in Dec 2024 and a record low of 60,788.000 Person in Apr 2020. Mexico MS: No. of Admissions: Locals data remains active status in CEIC and is reported by Ministry of Interior. The data is categorized under Global Database’s Mexico – Table MX.G011: Migration Statistics: Number of Admissions.