Learn More About How We Rank Our Computer Science Programs

| ComputerScience.org Staff Modified on March 17, 2022

Learn More About How We Rank Our Computer Science Programs

Driven by the latest industry data, our school rankings underscore our commitment to computer science students seeking excellence in higher education. We adhere to a stringent selection process to choose only the highest-performing schools, offering clear, comprehensive rankings for aspiring students.

We understand the stress that comes with comparing computer science degrees. In an effort to simplify the process of ranking computer science programs, we analyze program data using four key criteria: academics, affordability, reputation, and program availability.

Our rankings help students eliminate the guesswork in finding the nation's most affordable and reputable programs. Our methodology is bias-free; schools cannot pay for a spot in our rankings. While our site does include advertising partners, we do not consider those relationships when compiling rankings.

We cite the National Center for Education Statistics (NCES) as our primary data source. NCES is a federal agency that collects, analyzes, and publishes research about educational institutions.

Ultimately, students must decide on their own which school to attend. Our computer science rankings can help applicants identify high-quality programs and make sound choices based on reliable data.

About the Data We Use

ComputerScience.org compiles its rankings based on data derived from NCES: more specifically, NCES' Integrated Postsecondary Education Data System (IPEDS).

IPEDS gathers survey responses from postsecondary institutions. ComputerScience.org considers many of the same criteria as IPEDS survey components, including graduation rates, outcome measures, and student financial aid. We exclude schools that do not provide enough IPEDS data.

The U.S. Department of Education's Institute of Education Sciences (IES) operates NCES. IES is an independent, nonpartisan data source that offers accurate, accessible education statistics.

ComputerScience.org typically updates rankings annually. With each update, our rankings undergo a new reranking process. For example, our 2022 lists have been recalibrated using the most recent data available.

As of January 19, 2022, IPEDS has released only a portion of its updated school data for 2021. Our rankings use the most current data available at the time of publication.

A Breakdown of Our Rankings Methodology

We begin ranking computer science programs by first carefully selecting our methodology factors. We choose factors directly related to return-on-investment (ROI) and assess their impact on different levels and types of degrees.

Our rankings evaluate a program's academic performance, affordability, reputation, and availability through documented NCES and IPEDS statistics. Online program rankings reflect both full-time and part-time enrollees.

The following charts illustrate our primary online and on-campus program ranking methodology.

About Our Ranking Factors

In addition to the weighted ranking factors above, we also consider several subfactors during the ranking process. We determine a school's affordability by considering financial aid rates, alumni loan default rates, and comparisons of aid received to average enrollment cost.

We also account for academic performance subfactors like class size, retention and graduation rates, and the number of programs available.

Subfactors for Academics

Retention Rate: A school's retention rate indicates the percentage of students who continue their enrollment in a particular program from one year to the next. Generally, a high retention rate demonstrates a high-quality, high-performing program that supports student success. IPEDS measures the percentage of students who remain enrolled from one fall semester to the next to determine a school's retention rate. We use IPEDS's full-time retention rates for 2018-19 in our methodology. Graduation Rate: Graduation rate includes the percentage of students who complete their degree within a set timeframe. IPEDS measures the graduation rates of first-time students, full-time students, and degree- and certificate-seeking students. A high graduation rate, like a high retention rate, can indicate that students' academic success is supported by faculty and other institutional resources. For our rankings, we use IPEDS's 2016 150% graduation rate data, which measures the percentage of students who graduate within 1.5 times the normal time allotted for degree completion. Robust Faculty: IPEDS uses the Standard Occupational Classification (SOC) system to organize staffing data collected from postsecondary institutions. Data points include full- and part-time employees, full-time instructional staff, and full-time faculty. Within the full-time faculty category, SOC data examines gender, race/ethnicity, length of employment, salary, and rank. A diverse faculty can contribute to a school's positive reputation. We use IPEDS's 2019 data for full-time faculty and student-to-faculty ratios in ranking computer science programs.

Subfactors for Affordability

Price for Students With Grants or Scholarships: The cost of higher education is a primary concern for most college-bound students. IPEDS collects data on the overall cost of tuition and fees at public, private, two-year, and four-year institutions for undergraduate and graduate students. To further help students select an affordable program, our rankings use IPEDS's 2018-19 data about the average net tuition cost for students with grant or scholarship aid. Students Getting Financial Aid: IPEDS data also examines merit-based financial aid, including grants and scholarships, schools award. For our 2022 computer science program rankings, we incorporated the 2015-16 IPEDS financial aid data on several key metrics: the percentage of full-time and first-time undergraduate students awarded aid, how much financial aid these students received, and the average amount of grant and scholarship aid awarded across all schools. Students Getting Federal Aid: When determining program affordability, we also take federal financial aid into account. IPEDS collects data on grant aid and Title IV federal aid awarded to undergraduate students. We use 2015-16 IPEDS data —the percentage of undergraduate students awarded federal student loans as well as the average amount of federal student loans awarded to undergraduates — to rank the most affordable computer science programs. Post-Graduation Student Debt: Though most students pursue several forms of financial aid, they still often accrue debt upon graduation. However, some programs strive toward debt-free learning or develop initiatives to minimize post-graduation student debt. ComputerScience.org uses IPEDS's average loan default rate for 2017 and its 2015-16 data for the median debt for students who have completed their degree (six years after enrollment) to compile our 2022 rankings.

Subfactors for Reputation

Percentage of Applicants Admitted: IPEDS collects data to determine a program's acceptance rate by comparing the number of students who apply to the number of applicants granted admission. IPEDS does not survey open admission institutions. Based on IPEDS's admissions rate data for 2019-20, we ranked schools with a high number of first-time degree- or certificate-seeking undergraduate students. Admissions Yield: Admissions yield is the number of students admitted to the program who enrolled in classes. A high admissions yield may signify that an institution's programs and resources can attract new students and help foster student success. We use IPEDS's enrollment rate data from 2019-20 to inform our 2022 computer science program rankings. Return-on-Investment: ROI is a major factor when choosing a college or university, especially for aspiring computer scientists looking to land a lucrative career post-graduation. Programs with a high ROI may advertise accomplishment as a selling point for students. ComputerScience.org's rankings use IPEDS's 2019 data for the average earnings of students who began working six years after enrollment to determine programs with the highest ROI.

Subfactors for Program Availability and Online Flexibility

Percentage of Online Students Enrolled: For our online program rankings, we take into account the availability of online programs at online-only and traditional institutions. We use IPEDS data about the percentage of students enrolled in online computer science programs plus the percentage of students enrolled online overall. This subfactor only affects online-specific rankings. Percentage of Relevant Degree Level Offered: IPEDS also compiles data for the number and availability of degrees an institution offers. ComputerScience.org uses IPEDS data for a school's total number of programs at different degree levels, which determines whether we include the institution in our rankings. Offering a variety of programs can appeal to more students. We use this IPEDS data to inform our rankings of certificate/diploma, associate, bachelor's, master's, and doctoral programs.

Bootcamps Methodology

All bootcamps featured on our website must fit within certain criteria:

Be based in the United States Offer at least one bootcamp a minimum of 4 weeks in length If self-paced, require at least 10 hours of work a week If part-time, require at least 15 hours of work a week

Once vetted, we identify popular bootcamps using search volume. The top 10 bootcamps are featured alphabetically on our page. The rest are separated by bootcamp provider and listed alphabetically below featured bootcamps.

Subject-Specific Bootcamps code

Criteria:

  • Meet all previous criteria
  • Offer at least one bootcamp focused on that subject
Location-Specific Bootcamps location-marker

Criteria:

  • Meet all previous criteria
  • Offer at least one bootcamp located in that area
Payment-Specific Bootcamps currency-dollar

Criteria:

  • Meet all previous criteria
  • Offer at least one bootcamp that can be paid for using that payment option

Featured Image: Westend61 / Getty Images

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