In the academic year of 2022, it is expected that 551,460 female and 331,530 male students will earn a Master’s degree in the United States. These figures are a significant increase from the academic year of 1950, when 16,980 female students and 41,220 male students earned a Master’s degree.

What is a Master’s degree?

A Master’s degree is an academic degree granted by universities after finishing a Bachelor’s degree. Master’s degrees focus in on a specific field and are more specialized than a Bachelor’s. A typical Master’s program is about two years long, with the final semester focusing on the thesis. Master’s degree programs are usually harder to get into than Bachelor’s degree programs, due to the rigor of the program. Because these programs are so competitive, those with a Master’s degree are typically paid more than those with a Bachelor’s degree.

Master’s degrees in the United States

The number of master’s degrees granted in the United States has steadily increased since the 1970s and is expected to continue to increase. In 2021, the Master’s degree program with the worst job prospects in the United States by mid-career median pay was counseling, while the program with the best job prospects was a physician's assistant.

In the academic year of 2020/21, about 202,334 Master's degrees were earned in business across the United States, making it the most common master's degree earned. In that same year, another 47,257 Master's degrees were earned in engineering.

Graph and download economic data for Unemployment Rate: College Graduates: Advanced Degree: Master's Degree and Higher, 25 years and over (CGAD25O) from Dec 2014 to May 2025 about master's degree, 25 years +, tertiary schooling, education, household survey, unemployment, rate, and USA.

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.

The statistic shows the distribution of applications for business graduate programs in the Asia Pacific region in 2018, broken down by region of origin. In that year, 44 percent of the applications in the Asia Pacific region came from East and Southeast Asian countries, followed by 38 percent of the applications that came from Central and South Asian countries. With regards to applications from abroad, three percent of the applications came from European countries, followed by two percent of the applications from African countries.

The dataset contains Academic-year-wise historically compiled data on the total number of Indian students who enrolled for Undergraduate, Graduate, Non-Degree and Optional Practical Training (OPT) courses in the United States of America (USA).

The dataset contains Academic-year-, Programme- and Course-wise historically compiled data on the Number of International Students who have enrolled in the United States of America (U.S.A) for pursuing various types of Graduate, Under-graduate and OPT courses, along with percentage number of students pursing on each specific type of education

Graph and download economic data for Consumer Unit Characteristics: Percent Black or African American by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXU980270LB1409M) from 2012 to 2023 about doctoral degree, consumer unit, professional, tertiary schooling, African-American, education, percent, and USA.

Graph and download economic data for Civilian Labor Force: College Graduates: Advanced Degree: Master's Degree and Higher, 16 to 17 years (CGADL1617) from May 2015 to Jun 2025 about 16 to 17 years, master's degree, tertiary schooling, civilian, education, labor force, labor, household survey, and USA.

Korea Number of School: Graduate School: Sejong data was reported at 10.000 Unit in 2017. This records an increase from the previous number of 9.000 Unit for 2016. Korea Number of School: Graduate School: Sejong data is updated yearly, averaging 7.000 Unit from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 10.000 Unit in 2017 and a record low of 5.000 Unit in 2014. Korea Number of School: Graduate School: Sejong data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.G069: Education Statistics: Number of Schools, Teachers, Classroom and Students (Annual).

Education and further studies: refers to various learning, education, and related information collections

Korea Number of School: Graduate School: Ulsan data was reported at 11.000 Unit in 2017. This stayed constant from the previous number of 11.000 Unit for 2016. Korea Number of School: Graduate School: Ulsan data is updated yearly, averaging 7.000 Unit from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 11.000 Unit in 2017 and a record low of 6.000 Unit in 2008. Korea Number of School: Graduate School: Ulsan data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.G069: Education Statistics: Number of Schools, Teachers, Classroom and Students (Annual).

Korea Number of Enrolled Student: ow: Graduate School data was reported at 326,315.000 Person in 2017. This records a decrease from the previous number of 332,768.000 Person for 2016. Korea Number of Enrolled Student: ow: Graduate School data is updated yearly, averaging 91,304.000 Person from Dec 1965 (Median) to 2017, with 53 observations. The data reached an all-time high of 333,478.000 Person in 2015 and a record low of 3,842.000 Person in 1965. Korea Number of Enrolled Student: ow: Graduate School data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s South Korea – Table KR.G068: Education Statistics: Number of Schools, Teachers, Classroom and Students (Annual).

Graph and download economic data for Unemployment Rate: College Graduates: Advanced Degree: Master's Degree and Higher, 35 to 44 years (CGAD3544) from Dec 2014 to May 2025 about 35 to 44 years, master's degree, tertiary schooling, education, household survey, unemployment, rate, and USA.

In order to encourage academic and related research on gender equality education and improve the academic standards of the above-mentioned topics, the Ministry of Education has formulated the "Key Points for the Ministry of Education to Award Master's and Doctoral Thesis and Journal Papers on Gender Equality Education" for awards.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for bachelor degree in statistics with a concentration in mathematics in the U.S.

Graph and download economic data for Consumer Unit Characteristics: Percent Homeowner with Mortgage by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXU980230LB1409M) from 2012 to 2023 about doctoral degree, consumer unit, professional, homeownership, mortgage, tertiary schooling, education, percent, and USA.

In 2021, the District of Columbia had the most highly educated population in the United States, with 35.9 percent of the population over the age of 25 having an advanced degree. Massachusetts had the next most educated population, with 20.4 percent of residents having an advanced degree. For the United States as a whole, this figure stood at 13.1 percent of the population.

An advanced degree includes graduate and professional degrees.

Graph and download economic data for Unemployment Rate: College Graduates: Advanced Degree: Master's Degree and Higher, 25 to 34 years (CGAD2534) from Dec 2014 to Jun 2025 about master's degree, 25 to 34 years, tertiary schooling, education, household survey, unemployment, rate, and USA.

Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of "Computing in the Statistics Curriculum,'' the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this paper, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote the continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom.

Methods Surveys from Carpentries style workshops the results of which are presented in the accompanying manuscript.

Pre- and post-workshop surveys for each workshop (Introduction to R, Intermediate R, Data Wrangling in R, Data Visualization in R) were collected via Google Form.

The surveys administered for the fall 2018, spring 2019 academic year are included as pre_workshop_survey and post_workshop_assessment PDF files. 
The raw versions of these data are included in the Excel files ending in survey_raw or assessment_raw.

  The data files whose name includes survey contain raw data from pre-workshop surveys and the data files whose name includes assessment contain raw data from the post-workshop assessment survey.


The annotated RMarkdown files used to clean the pre-workshop surveys and post-workshop assessments are included as workshop_survey_cleaning and workshop_assessment_cleaning, respectively. 
The cleaned pre- and post-workshop survey data are included in the Excel files ending in clean. 
The summaries and visualizations presented in the manuscript are included in the analysis annotated RMarkdown file.