This dataset provides comprehensive information about various Data Science and Analytics master's programs offered in the United States. It includes details such as the program name, university name, annual tuition fees, program duration, location of the university, and additional information about the programs.

Column Descriptions:

• Subject Name: The name or field of study of the master's program, such as Data Science, Data Analytics, or Applied Biostatistics.

• University Name: The name of the university offering the master's program.

• Per Year Fees: The tuition fees for the program, usually given in euros per year. For some programs, the fees may be listed as "full" or "full-time," indicating a lump sum for the entire program or for full-time enrollment, respectively.

• About Program: A brief description or overview of the master's program, providing insights into its curriculum, focus areas, and any unique features.

• Program Duration: The duration of the master's program, typically expressed in years or months.

• University Location: The location of the university where the program is offered, including the city and state.

• Program Name: The official name of the master's program, often indicating its degree type (e.g., M.Sc. for Master of Science) and format (e.g., full-time, part-time, online).

In 2023, ** percent of prospective graduate business students in the United States were interested in hybrid programs, an increase from ** percent in 2019. However, the overall preference in 2023 was for in-person business school programs, at ** percent.

Raw data for the manuscript entitled: European Agrifood and Forestry Education for a Sustainable Future - Gap Analysis from an Informatics Approach

Abstract

Purpose: To evaluate how well European agrifood and forestry Masters program websites use vocabulary associated with the NextFood Project ‘categories of skills’.

Methodology: Web-scraping Python scripts were used to collect texts from European Masters programs websites, which were then analysed using statistical tools including Partial Least Squares Regression and contextual relation analysis. A total of fourteen countries, twenty-seven universities, 1303 European Masters programs, 3305 web-pages and almost two million words were studied using this approach.

Findings: While agrifood and forestry Masters programs used vocabulary from the NextFood Project ‘categories of skills’ in most cases equal to or more often than non-agrifood and forestry Masters programs, we found evidence for the relative underuse of words associated with networking skills, with least use among agriculture-related Masters programs.

Practical Implications: The informatic approach provides evidence that European agrifood and forestry Masters programs are for the most part following the educational paths for meeting future challenges as outlined by the NextFood Project, with the possible exception of networking skills.

Theoretical Implications: This text-based, informatic approach complements the more targeted approaches taken by the NextFood Project in studying the skilling-pathways, which involved focus-group interviews, surveys of stakeholders, interviews of individuals with expert-knowledge and literature reviews.

Originality: A text-based, web-scraping informatic approach has thus far been limited in the study of agrifood and forestry higher education, especially relative to recent advances made in the social sciences.

Dataset with information on the number of new students in each Official Master's degree.

According to an online survey conducted in February 2025 in the United States, ********* of LinkedIn users held a bachelor degree or equivalent. Additionally, ** percent of LinkedIn users in the U.S. held a masters degree or equivalent.

ABSTRACT The health care model based on the Family Health Strategy, created in the early 1990s, encouraged changes in health education, highlighting the need to create lato and stricto sensu postgraduate courses aimed at empowering professionals that foster comprehensive health care. Periodic evaluations are carried out and encouraged by Capes/MEC in order to maintain the quality of postgraduate courses, but evaluations of recently-introduced professional master’s degree courses in family health remain scarce. Objectives To describe the academic profile, contribution, motivations and expectations of graduates of a Professional Master’s in Family Health. Method Cross-sectional and quantitative study to analyze the results of 102 questionnaires answered by graduates of the Professional Master’s Degree in Family Health of the Estácio de Sá University (RJ), who had concluded the course between 2007 and 2012. The instrument consisted of open-ended and closed-ended questions, sent by e-mail and made available online through the electronic platform Survey Monkey. The study evaluated age, gender, regional origin, academic background, as well as the contributions, expectations and motivations related to the course. Results The survey sample was formed predominantly by female graduates, aged over 30, from 13 Brazilian states and, mainly from Medicine and Nursing courses. The contribution of the master’s degree to the graduate’s professional life was evaluated as excellent by 77% of the interviewees. The expectations regarding the course were positively evaluated and the main reasons for seeking the qualification were scientific-technical improvement and personal satisfaction, rather than better salaries or job stability. Conclusion The course was evaluated positively by the graduates, having exceeded their expectations and satisfied the interests that led them to it, thus producing changes to their personal and professional life. A longitudinal analysis of the impact of the professional master’s degree in the career of graduates will require a sequence of similar studies, as has been stimulated by Capes/MEC in recent years.

The COVID-19 data sets and associated Jupyter Hub notebooks are support for a manuscript describing how data science was shown to be effective in developing a transdisciplinary team and the production of novel outputs in part due to the common learning process of all team members being part of an online professional data science and analytics master’s degree program. This online curriculum helped the team members to find a common process that allowed them learn in common (Kläy, Zimmermann, & Schneider, 2015), transdisciplinary learning a key component of transdisciplinary teamwork (Yeung, 2015). Our team's Jupyter Hub files with complete coding and data set explanations are uploaded to document this teamwork and the outputs of the team.

This file set is the basis of a project in which Stephanie Pywell from The Open University Law School created and evaluated some online teaching materials – Fundamentals of Law (FoLs) – to fill a gap in the knowledge of graduate entrants to the Bachelor of Laws (LLB) programme. These students are granted exemption from the Level 1 law modules, from which they would normally acquire the basic knowledge of legal principles and methods that is essential to success in higher-level study. The materials consisted of 12 sessions of learning, each covering one key topic from a Level 1 law module.The dataset includes a Word document that consists of the text of a five-question, multiple-choice Moodle poll, together with the coding for each response option.The rest of the dataset consists of spreadsheets and outputs from SPSS and Excel showing the analyses that were conducted on the cleaned and anonymised data to ascertain students' use of, and views on, the teaching materials, and to explore any statistical association between students' studying of the materials and their academic success on Level 2 law modules, W202 and W203.Students were asked to complete the Moodle poll at the end of every session of study, of which there were 1,013. Only one answer from each of the 240 respondents was retained for Questions 3, 4 and 5, to avoid skewing the data. Some data are presented as percentages of the number of sessions studied; some are presented as percentages of the number of respondents, and some are presented as percentage of the number of respondents who meet specific criteria.Student identifiers, which have been removed to ensure anonymity, are as follows: Open University Computer User code (OUCU) and Personal Identifier (PI). These were used to collate the output from the Moodle poll with students' Level 2 module results.

IntroductionThe assessment of student outcomes is essential for monitoring the quality of graduate programs in healthcare sciences. As such, this study focused on developing a self-employed questionnaire that allowed for the evaluation of elements focused on career impact and levels of satisfaction regarding graduate program education. Following, this instrument was utilized in a cross-sectional study design with alumni that had obtained their degree (MSc or PhD) over a 25-year span (1995–2020) from a graduate program in dentistry located in Brazil.MethodsThe employed instrument comprised a total of 43 questions presenting a mix of both close and open-ended questions coupled with 5-point Likert scales. The questionnaire was hosted online and a total of 528 alumni were invited to participate through e-mail and social media outreach.Results376 alumni answered the questionnaire (71.2% response rate). The majority were female (69.9%), and with a MSc (58.5%). Levels of satisfaction towards the program as well the impact in career and life were higher in alumni that had obtained a PhD degree compared to MSc. After obtaining the degree, an increase in involvement in teaching/research positions (3.4% vs 21.5%, p < 001) and a decrease in unemployment (21.9% vs 2.1%, p < 001) were observed. The highest levels of impact were observed regarding the achievement of the professional goals as nearly 90% of the population agreed with this statement.ConclusionsThis study highlighted the creation and employment of an assessment tool that can be utilized to monitor the perceptions of student outcomes. Among the findings, a decrease in unemployment and a high degree of career impact and satisfaction were observed in the population of this study. Moving forward, it is essential that monitoring educational outcomes remains a priority worldwide.

This data collection contains information on degrees earned at a sample of postsecondary institutions in the United States. The survey collected data on the number of completions of academic, vocational, and continuing professional educational programs by award category. There are three files in the collection. Part 1, Response Status Information, contains response status information to the completions survey for active institutions in the sample. Part 2, Postsecondary Completions: Awards/Degrees Conferred, contains the number of degrees and other awards granted by the institution in each field of study (CIP code), by level of award/degree, and sex of recipient. Part 3, Postsecondary Completions by Major Discipline (Two-Digit CIP Codes), contains the number of degrees and other awards conferred by major discipline (two-digit CIP code), award level, race/ethnicity, and sex of recipient.

The global massive open online course (MOOC) market size is calculated to advance at a CAGR of 32% through 2034, which is set to increase its market value from US$ 13.2 billion in 2024 to US$ 212.7 billion by the end of 2034.

Report Attribute	Detail
MOOC Market Size (2024E)	US$ 13.2 Billion
Projected Market Value (2034F)	US$ 212.7 Billion
Global Market Growth Rate (2024 to 2034)	32% CAGR
China Market Value (2034F)	US$ 23.3 Billion
Japan Market Growth Rate (2024 to 2034)	32.6% CAGR
North America Market Share (2024E)	23.9%
East Asia Market Value (2034F)	US$ 49.1 Billion
Key Companies Profiled	Alison; Coursera Inc; edX Inc; Federica.EU; FutureLearn; Instructure; Intellipaat; iverity; Jigsaw Academy; Kadenze.

Country Wise Insights

Attribute	United States
Market Value (2024E)	US$ 1.4 Billion
Growth Rate (2024 to 2034)	32.5% CAGR
Projected Value (2034F)	US$ 23.6 Billion

Attribute	China
Market Value (2024E)	US$ 1.5 Billion
Growth Rate (2024 to 2034)	32% CAGR
Projected Value (2034F)	US$ 23.3 Billion

Category-wise Insights

Attribute	xMOOC
Segment Value (2024E)	US$ 9.3 Billion
Growth Rate (2024 to 2034)	30.8% CAGR
Projected Value (2034F)	US$ 136.1 Billion

Attribute	Degree & Master Programs
Segment Value (2024E)	US$ 6.4 Billion
Growth Rate (2024 to 2034)	30.2% CAGR
Projected Value (2034F)	US$ 89.3 Billion

The Higher Education General Information Survey (HEGIS) was designed to provide comprehensive information on various aspects of postsecondary education in the United States and its territories (American Samoa, Guam, Puerto Rico, the Virgin Islands, and the Marshall Islands) and Department of Defense schools outside the United States. The HEGIS Fall Enrollment Component for 1974 sought enrollment data for all public and nonpublic 2- and 4-year institutions and their branches for postbaccalaureate students enrolled for advanced degrees, both graduate and professional. The data cover information on enrollments by class level, number of full-time and part-time male and female students enrolled at various levels (graduate, undergraduate, etc.), sex, race, calendar system, type of accreditation, and enrollments of first-time students. All of these data were acquired in terms of head counts and full-time equivalents, by state.

Dataset with information on the total number of students in each Official Master's degree.

Integrated computing uses computing tools and concepts to support learning in other disciplines while giving all students opportunities to experience computer science. Integrated computing is often motivated as a way to introduce computing to students in a low-stakes environment, reducing barriers to learning computer science, often especially for underrepresented groups. This dataset examined integrated computing activities implemented in US schools to examine which programming and CT concepts they teach and whether those concepts differed across contexts. We gathered data on 262 integrated computing activities from in-service K-12 teachers and 20 contextual factors related to the classroom (i.e., primary discipline, grade level, programming paradigm, programming language, minimum amount of time the lesson takes, source of the lesson plan), the teacher (i.e., years teaching, current role (classroom teacher, tech specialist, STEM specialist, etc.), grade levels taught, disciplines taught, degrees and certifications, institutional support received for integrated computing, gender, race, self-efficacy), and the school (e.g., socioeconomic status of students, racial composition, number of CS courses offered, number of CS teachers, years CS courses have been taught, number of students, school location (urban, suburban, rural)). Methods Procedure Data about integrated computing lessons in non-CS classrooms were collected from in-service K-12 teachers in the United States via an online survey, and 262 surveys were completed. Participants were recruited first through teacher networks and districts to include diverse populations and then through LinkedIn. Teachers received a $100 gift card upon completion of the survey, which took approximately 30 minutes. Due to the incentive, submissions were screened during data collection to ensure eligibility (i.e., having a valid school district email) and quality (described below).

Instrument The survey asked about the programming and CT concepts taught in the activities and 20 factors related to classroom, teacher, and school context. The programming concepts included were based on a framework developed by Margulieux et al., 2023. A full list of concepts and contextual factors can be found below. Due to the large sample size, the survey was designed to be primarily quantitative but included a few qualitative questions (e.g., "Please describe in 1-2 sentences the computing learning objective of this activity") and requested teachers to submit their lesson plans. The research team used these qualitative elements to verify data quality, such as by ensuring the lesson included computing and comparing elements of the lesson plans to the quantitative data provided by the teachers. Overall, we found, and excluded, very few instances of low-quality data.

Survey Questions and Descriptive Statistics Qualitative Questions: Title of lesson plan One sentence describing the activity topic (e.g., In this activity, students apply their computational thinking skills to explore the life cycle of a butterfly.) One sentence describing the disciplinary learning objective (e.g., The primary learning goal is to model the life cycle of a butterfly.) One sentence describing the computing learning objective (e.g., Students will conditionals to match body features to life stages.) 1-3 sentences describing the instructional paradigm (e.g., Students will discuss butterflies and life cycles with their partners. Then they will modify the program and use conditionals to create the model.)

Quantitative Question Topic: Response Options (descriptive statistics in parentheses)

Programming and CT Concepts Programming paradigm: Select one: No Programming (80), Unplugged (87), Block-based (69), Text-based (26) Programming language: Open-ended Programming concepts: Select all that apply: Operator-arithmetic, Operator-Boolean, Operator-relational, Conditional-if-else, Conditional-if-then, Loop-for loop, Loop-while loop, Loop-loop index variable, Function-define/call, Function-parameter, Variable, Data types (string, integer, etc.), List, Multimedia component (sprite, sound, button, etc.), Multimedia properties (color, location, etc.), Multimedia movement (forward, back, turn), Output-string, Output-variable, User input, Event (M = 3.2, SD = 2.7) CT concepts: Select all that apply: Algorithms–sequences (158), Algorithms–parallelism (10), Pattern recognition (142), Abstraction (84), Decomposition (89), Debugging (40), Automation (40) (M = 2.1, SD = 1.1)

Classroom Context Integrated discipline: Select one: Art (5), Language arts (37), Foreign language (2), Math (67), Music (3), Science (61), Social Studies (13) Grades taught in lesson: Select all that apply: Kindergarten through 12th grade (activities that spanned K-5 = 107, 6-8 = 53, 9-12 = 93, K-12 = 9) Minimum amount of time the lesson takes: Select one: < 1 hour (90), 1-3 hours (126), 3-8 hours (32), 8+ hours (14) Source of the lesson plan: Select all that apply: Colleague (16), Online search (18), Professional development (20), Professional organization (23), Created based on an external source by myself or with colleagues (28), Modified from an external source (33), Created by myself or with colleagues (124)

Teacher Information Number of years teaching: Open-ended, M = 14.11, SD = 7.6 Current role: Select one: Teacher (220), STEM/Tech specialist (24), Librarian (9), Computer lab director (1), Other (8) Grade levels taught: Select all that apply: K-2, 3-5, 6-8, 9-10, 11-12 (grade levels that spanned K-5 = 79, 6-8 = 45, 9-12 = 93, K-12 = 45) Disciplines taught: Select all that apply: Art (13), Language arts (71), Foreign language (5), Math (134), Music (4), Science (100), Social Studies (54), Computer science (80), Technology (78), Other (8) Degrees, Certs, endorsements, etc. attained: Select all that apply: Teaching certificate in primary discipline(s) (164), Teaching certificate in CS (17), Bachelor’s degree in primary discipline education (129), Bachelor’s degree in CS or CS education (4), Master’s degree in primary discipline education (163), Master’s degree in CS or CS education (0), Endorsement in computer science education (47), EdD or PhD in education (17), Other (86) Support for integrated CS/CT development and implementation: Select all that apply: Professional development through my school/district/LEA/RESA (157), Professional development through external organizations (117), Peer/colleague/department collaboration in my school/district/LEA/RESA (130), Peer/colleague collaboration in external organizations (73), Funding for software licensing, hardware, or curricula (69) Self-efficacy: Views of CT and self-efficacy scale from Yadav, Caeli, Ocak, and Macann, 2022 (M = 4.23 out of 5, SD = 0.60) Gender: Select one: Man (60), Woman (198), Non-binary/third gender (2), Prefer not to say (2) Race: Select one: African American or Black (31), American Indian or Indigenous (1), Asian (13), Caucasian or White (193), Latino/a/x or Hispanic (10), Middle Eastern (0), Pacific Islander (0), Other (14)

School Context Number of students: Open-ended (M = 1179, SD = 741) Number of CS teachers: Open-ended (M = 1.6, SD = 1.4) Number of CS courses: Open-ended (M = 2.1, SD = 2.0) Number of years CS courses taught: Open-ended (M = 3.0, SD = 2.1) Racial composition: Give % of each race: American Indian or Native American (M = 1.8%), Asian (M = 4.5%), Black or African American (M = 23.3%), Hispanic or Latino (M = 17.2%), White or Caucasian (M = 47.5%), Other (M = 2.4%) % of students eligible for free or reduced lunch: Open-ended (M = 56%, SD = 34%) Type of area: Select one: Rural (90), Suburban (122), Urban (50)

Abstract The aim of this paper is to understand students’ experience of research methodology teaching, through a survey with students enrolled in the Master’s Degree Program in Information Management, Libraries and Archives at the Complutense University of Madrid. The analyzed themes included the students’ perception of collaborative work, influence of emotions, implications of research competencies at the professional level, and the role of supervisors. Twenty-six master’s students, both online and on-site, were surveyed among those enrolled in the academic years 2014-2015 and 2013-2014. Results show the need to foster collaborative work with individual work in a balanced way. Furthermore, emotions, especially positive ones, appear to intertwine heavily with the learning experience. It is more difficult to appreciate the implications of research competencies for the professional sphere because of differences in the professional context of all students involved. The activities that students perceive as more creative include discussions of one’s own work (especially with the supervisor) as well as discussions of other students’ work (attendance at Master Thesis Defenses). Finally, supervisors stand out as important figure during the learning of research methodology, as their area of expertise is particularly relevant.

The data contain campus recruitment data of China Aerospace Science and Technology Corporation (CASC) and China Aerospace Science and Industry Corporation (CASIC) and 41 Chinese elite universities’ enrollment of bachelor, master, and doctor degree by discipline. The data also contain the numbers of space industry related professional organizations, publication, and patent of these universities. All of these data were openly collected online.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by educational degree, gender and age group, annual.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Data Bases And Internet Application Programming. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Data Bases And Internet Application Programming. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

Weighted average tuition fees for full-time Canadian and international undergraduate and graduate students. Data are collected from all publicly funded Canadian degree-granting institutions.