Summary of source data for Code.org infographics and stats
Table of Contents
Source: Bureau of Labor Statistics Employment Projections (Table 1.2)
Classification of occupations:
Computer Occupation Codes
Computer and Information Systems Managers
Computer Hardware Engineers
Computer science teachers, postsecondary
Computer and information research scientists
Computer systems analysts
Information security analysts
Software developers, applications
Software developers, systems software
Network and computer systems administrators
Computer network architects
Computer user support specialists
Computer network support specialists
Computer occupations, all other
STEM Codes (Science, Engineering, Mathematics, and Information Technology Domain)
Architectural and Engineering Managers
Natural Sciences Managers
Mathematical Science occupations
Surveyors, Cartographers, and Photogrammetrists
17-3000 (except 17-3011)
Drafters, Engineering Technicians, and mapping Technicians
19-3000 (except 19-3093)
Social Scientists and Related Workers
Life, Physical and Social Science Technicians
Math and Computer Teachers, Postsecondary
Engineering Teachers, Postsecondary
Life Sciences Teachers, Postsecondary
Physical Sciences Teachers, Postsecondary
Social Sciences Teachers, Postsecondary
Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products
Other Life Sciences
Mathematics & Statistics
Other Physical Sciences
Other STEM Technologies
Interdisciplinary or Other Sciences
In the past (2015 and before), we used the National Science Foundation WebCASPAR tool to analyze the NCES IPEDS Completions Survey
Source: Access Report data reported in 2018 State of Computer Science Education: Policy and Implementation
Results: The full methodology is included on p. 69-71. Data sources include the National Center for Education Statistics, state education agencies, national organizations, school course catalogs, and survey responses. These are schools where students learn computer science during the school day (not in after school clubs) and spend a minimum amount of time per semester applying learned concepts through programming (at least 20 hours of programming for grades 9-12 high schools). Although computer science is broader than programming, some direct programming experience is integral to learning the fundamental concepts and is used as a defining characteristic to differentiate a foundational computer science course from non-computer science courses.
Prior data: Previous data from the study Searching for Computer Science: Access and Barriers in K-12 Education (released in 2015) found that 91% of parents wanted their students to learn CS and 90% of parents wanted their child’s school to teach CS.
Source: A study by Change the Equation and C+R Research, with analysis completed by Code.org. More information about the study can be found from Change the Equation.
Method: High school students were asked, for each course they have taken or plan to take, about whether they “like it a lot,” “like it a little,” “dislike it a little,” or “dislike it a lot.”
Result: When comparing computer science courses to other courses, more students like graphic arts, performing arts, and computer science courses. Code.org’s analysis is described in more detail here.
Source: The Hamilton Project (Brookings)
Included: The net present value for lifetime earnings for high school graduates, college graduates, and computer science majors.
Sources: The number of current open computing jobs comes from the sum of the per-state jobs data from The Conference Board’s Help Wanted OnLine®service (see below for more details on the data from The Conference Board).
The projected rate of this job growth comes from the Bureau of Labor Statistics Employment Projections data for 2016-2026, released in 2017. These data predict an employment change of 12.65% for computing occupations and an employment change of 7.38% for all occupations.
Definitions: For computing occupations, we use SOC codes 11-3021, 15-1100, 17-2061, and 25-1021 (see more details on these codes in the table above).
Definition: The number of job openings in each category were multiplied by the average salary (from the Bureau of Labor Statistics 2016 OES data). For computing occupations, we use SOC codes 11-3021, 15-1100, 17-2061, and 25-1021 (see more details on these codes in the table above).
For students who try AP CS in high school:
For ¼ women in high school CS, university CS, and software workforce:
Definitions: We define STEM exams as Biology, Calculus AB, Calculus BC, Chemistry, Computer Science A, Computer Science Principles, Environmental Science, Physics 1, Physics 2, Physics C: Elec. & Magnet., Physics C: Mechanics, and Statistics.
Results: Thanks to the advocacy efforts by the Code.org Advocacy Coalition, the list of states that allow computer science to count towards graduation credit has increased monthly, and at this point, Code.org is the definitive source of the data. The latest list of states is reflected here.
Source: Horizon Media’s WHY group survey
Results: The group reported that “three in four Americans agree that ‘today, science is cool in a way that it wasn’t ten years ago.’ And computer science is a major driver of this new perceived cool factor -- with 73% agreeing that ‘in the future, all the best jobs will require knowledge of computer coding languages.” And “when asked which two subjects other than reading and writing are critical to ensure the next generation is prepared for the future, 70% chose math, and 50% said computer science. More than two thirds (65%) went as far to agree that ‘most students would benefit more from learning a computer coding language than a foreign language.’ ”
Result: Graduates with computer science degrees earn the second highest starting salaries (just after mechatronic engineering graduates).
Source: Forbes in 2013
Result: Best-paying degree in the USA was a computer science degree from Carnegie Mellon.
According to this IDC study in 2014, or this easier-to-read summary, there are 11M software professionals in the world. 19.2% are in the U.S., which means 2.1M software professionals in the U.S. According to NCWIT, 26% of these professionals are female, which is about 550,000.
Source: MSFT National Talent Strategy document and taken from a Georgetown University Center for Education and the Workforce Report on STEM (October 2011) by Anthony Carnevale, Nicole Smith, and Michelle Melton
Included Quote: "Computer occupations are the most widely represented across industries. For example, 9 percent are in Information Services, 12 percent are in Financial Services, 36 percent are in Professional and Business Services, 7 percent are in Government and Public Education Services, and 12 percent are in Manufacturing." Therefore: 12 + 36 + 7 + 12 = 67%.
Definitions: This is how we classified the job types:
computer systems analyst
software applications developer
computer occupations other
software developers - system software
computer information system manager
network/computer system administrator
Method: For average state salary (versus average salary for a computing occupation as above), we use “Annual mean wage” for all occupations. For average salary in computing occupations, we calculate the weighted arithmetic mean of all computing occupations (BLS codes 11-3021, 15-1100, 17-2061, and 25-1021) using the “annual mean wage” and total employment for each occupation code. That is, instead of simply finding the mean of the four occupation codes, we multiply the average salary for a given code by the number of people employed in that occupation and divide the sum of these salaries by the total number of people employed in computing occupations.
Methods: The number of open computing jobs in each state represents the number of open jobs in the previous month (seasonally adjusted) for Bureau of Labor Statistics’ (BLS) Category SOC “15-0000 Computer and Mathematical Occupations”). This is a conservative estimate of the number of computing occupations as it excludes three BLS categories that include computing occupations: Computer and Information Systems Managers 11-3021, Computer Hardware Engineers 17-2061, and Computer Science Teachers, Postsecondary 25-1021. However, the 15-0000 SOC also includes some mathematical occupations that are not considered computing occupations. (This is due to limitations with our agreement with the Conference Board.) This data is cross-sector.
The comparison to the state average demand rate is comparing the job demand (% open jobs / # of existing jobs determined in the May 2016 BLS’s OES survey) in computing occupations vs the state average.
The national jobs data is the sum of the 50 states plus D.C.
In the past (2015 and before), we used the National Science Foundation WebCASPAR tool to analyze the NCES IPEDS Completions Survey
Source: National Teacher Preparation Data in the Title II Reports. This is data reported by states and institutions with teacher preparation programs. To download the spreadsheet of data for all states from https://title2.ed.gov/Public/Home.aspx, click on any state, then the “Data Files” tab, then download the “All States Report Data File.”
Methods: There are three ways of looking at the data: Teachers Prepared by Area of Certification, Teachers Prepared by Subject Area, and Teachers Prepared by Academic Major. The following table describes the difference between each count and shows the data for all three over the past three years. The state fact sheets report the Teachers Prepared by Subject.
Prepared by Subject
Program completers that were prepared to teach in the subject Teacher Education - Computer Science
Prepared by Area (i.e., certification)
Received an initial teaching certification in Computer Science
Prepared by Major
Graduated with a major in Teacher Education - Computer Science
Number of Hours of Code completed in a state comes from our Numbers of Hour of Code data.
The reported number of Code Studio accounts (by teacher and by student) includes all accounts that have been created and that have logged in at least once. IP addresses are used to determine the state.
At Code.org, we do not count unique student IDs perfectly when tracking participation in the Hour of Code. Why? Partly because we don’t want the friction of prompting to “login / register” before a student or classroom tries learning for the first time, and partly because there are many activities we cannot track online. We do take certain steps to reduce double-counting, but without a login prompt, this can’t work perfectly. At the same time, there are MANY student activities in the Hour of Code that aren’t tracked at all. For example: (1) students who use a mobile/tablet app to try the Hour of Code are typically not counted, (2) students who share a screen for pair-programming or group-programming may be counted as one, (3) students trying an unplugged classroom activity cannot be counted online, and (4) teachers who create their own Hour of Code activities aren’t tracked. As a result, there is some under-counting and some double-counting, and so we do not view the Hour of Code tracker to be an exact measure of usage. It is certainly directionally correct, and shows that many tens of millions of students have participated. And our “lines of code” counter tracks very real usage in our learning platforms.
48% of our students come from traditionally underrepresented minority populations. This includes Black, Latinx, Hispanic, Native Hawaiian or Other Pacific Islander, American Indian or Alaskan Native. 49% of students are on free and reduced meal programs (FARM). To protect the privacy of our youngest students, we measure the diversity for students under age 13 for the entire classroom by surveying teachers. This means these numbers are based on teachers’ estimates of the actual student ethnicities. For older students, the students self identify their race.
Similarly, for student privacy, we do not ask individual students if they are on free or reduced meals. Instead, we have an optional survey for teachers. This means these numbers are based on their knowledge of which students have subsidized meals. To protect privacy, our surveys are optional and do not represent all Code.org teachers.
Furthermore, this survey method doesn’t reflect the ethnicity of students under age 13 who are using Code Studio at home, without a classroom teacher. Our organizational focus is on bringing computer science into K-12 schools, and that is also what we are measuring.
Lastly, our ethnicity surveys do not measure international diversity because our focus is the U.S. and ethnic questions would be different outside the U.S. Similarly, the free and reduced meal program is specific to the United States. We do not measure similar programs internationally.
Our “45% female” measure of gender diversity in CS Fundamentals courses on Code Studio is based on student accounts, and thus represents all active Code Studio students worldwide. This number is updated annually and reflects active student accounts for the previous year.
The previous version of this document (prior to early 2015) can be found here.