Computer Science Learning Curve Survey 2024 Report
In 2024, JetBrains Academy surveyed 23,991 respondents worldwide, including university students, online learners, self-taught enthusiasts, coding boot camp graduates, professionals, and career switchers.
Based on their inspiring insights, this report explores the current trends in computer science education, from formats and tools to motivations, career goals, and challenges.
Whether you're an educator, researcher, learner, curious professional, or supportive parent, dive in! Share your thoughts and connect with the CS learning community using #JetBrainsAcademySurvey24.
This is a public report; its contents may be used only for non-commercial purposes. Get the full details here.
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Formal Education
Have you studied computer science in any format over the past 12 months?
Yes, self-education
Yes, at a formal educational institution
Just over half of computer science learners study at formal educational institutions, with 54% of formal learners broadening their knowledge through further self-education.
of those who have completed formal education hold a bachelor's degree or higher.
Highest level of formal education completed
Formal educational institution currently attended
Degree currently pursued
Major field of study (current or past)
Computer science
Software engineering
Other engineering
Art / Humanities
Economics
Mathematics
Biology / Chemistry
Social sciences
Physics
Other
Career
of all computer science learners have paid IT work experience, and for 89% of them, this is their primary income source. Most of these respondents work in software engineering roles (76%), with 35% holding mid-level positions.
Current job role100+
This question was only shown to those who responded that they rely on work in computer science as their primary income source.
The tech industry remains predominantly male across most roles, with significantly lower representation for women and non-binary individuals. Core technical roles and leadership positions (team leads and executives) have the least gender diversity, with 88%–94% male representation.
However, some roles show relatively higher female representation compared to the industry average: UX/UI designers (16%), QA/Testers, business analysts (14%), instructors (13%), and product/marketing managers (12%). Non-binary representation remains limited across all roles, with developer advocates seeing the highest rate at 6%.
Employment status
Seniority level
Salary (annual net in USD, excluding any bonuses)
Do you have previous work experience outside of computer science/IT?
Yes, I worked/studied in another field before switching to computer science/IT
No, this is the only field I’ve ever worked in
Younger respondents aged 18–29 are more likely to go directly into a tech career, with only 9% of those aged 18–20 and 24% of those aged 21–29 having prior experience in another field. However, career switching becomes more common with age, with 50% of respondents aged 30–39 and 59% of those aged 60+ reporting previous careers outside of tech.
There are also clear regional differences in career trajectories. In India and China, non-career-switchers form the majority, reflecting a strong trend of direct entry into computer science. In contrast, Argentina and Brazil show more diverse pathways, with career switchers either outnumbering or nearly matching non-switchers. In regions like Europe, Southeast Asia, and North America, career switchers make up about one-third, reflecting a more conventional entry pattern.
Do you have previous work experience outside of computer science/IT? (by region)
India
China
Germany
Türkiye
Middle East, Africa, Central Asia
Other Southeast Asia and Oceania
South Korea
Rest of Europe
France
Canada
Previous professional sphere
Respondents answered this question with open-text answers. ChatGPT was used to automate the analysis and sorting of the responses into thematic clusters.
This question was only shown to respondents who said they had worked or studied in another field before switching to computer science/IT.
Engineering and technical fields take the top spot among those transitioning to computer science, followed by finance and business management. Education, healthcare, and creative arts also rank prominently, showcasing the diverse professional backgrounds entering the field.
Reasons for choosing a career in tech100+
Motivation for learning new computer science topics100+
While a strong passion for computer science drives most career transitions, nearly half of respondents highlight their love for problem-solving and process automation as key motivators. Interestingly, salary and remote work opportunities rank slightly lower than creative ambitions, such as building games or websites, revealing that the field attracts those driven by aspirations as much as by practical benefits.
Motivation for learning new computer science topics (by region)
| I don’t want to learn new computer science topics | Other | To complete a specific task | To migrate to another technology | Out of interest | To find a new job or switch roles | To keep up with the latest trends | To work on personal projects | To grow in my current role | |
|---|---|---|---|---|---|---|---|---|---|
| <1% | 2% | 18% | 16% | 43% | 52% | 49% | 56% | 68% | Eastern Europe, Balkans, and the Caucasus |
| <1% | 1% | 13% | 11% | 49% | 49% | 40% | 49% | 67% | South Korea |
| <1% | 2% | 26% | 21% | 47% | 47% | 51% | 56% | 67% | Other Southeast Asia and Oceania |
| <1% | 2% | 27% | 19% | 79% | 34% | 48% | 60% | 66% | Germany |
| – | 3% | 21% | 17% | 67% | 44% | 47% | 55% | 64% | Benelux and Northern Europe |
| 1% | 2% | 17% | 17% | 45% | 50% | 55% | 59% | 64% | India |
| <1% | 1% | 22% | 26% | 23% | 45% | 55% | 49% | 64% | Nigeria |
| <1% | 2% | 20% | 18% | 51% | 46% | 47% | 58% | 62% | Rest of Europe |
| – | <1% | 23% | 17% | 67% | 43% | 47% | 44% | 62% | China |
| – | 2% | 21% | 14% | 62% | 48% | 44% | 58% | 61% | United Kingdom |
| 1% | 2% | 22% | 16% | 58% | 54% | 45% | 65% | 61% | United States |
| 1% | 2% | 19% | 21% | 38% | 44% | 48% | 54% | 60% | Middle East, Africa, Central Asia |
| – | 3% | 13% | 18% | 58% | 50% | 54% | 51% | 60% | Spain |
| 1% | 1% | 20% | 22% | 45% | 41% | 46% | 51% | 56% | Türkiye |
| <1% | 2% | 25% | 13% | 56% | 59% | 45% | 62% | 56% | Canada |
| 2% | 1% | 15% | 19% | 42% | 41% | 28% | 39% | 55% | Russian Federation and Belarus |
| – | 3% | 16% | 21% | 52% | 64% | 42% | 57% | 54% | Brazil |
| 1% | 1% | 24% | 23% | 73% | 38% | 39% | 58% | 54% | France |
| 9% | 1% | 10% | 18% | 49% | 63% | 46% | 56% | 54% | Mexico |
| <1% | 2% | 11% | 19% | 41% | 60% | 51% | 57% | 52% | Central and South America |
| 4% | <1% | 14% | 19% | 43% | 40% | 31% | 38% | 50% | Ukraine |
| 3% | 1% | 12% | 13% | 58% | 34% | 42% | 31% | 48% | Japan |
| 1% | 2% | 9% | 17% | 52% | 63% | 44% | 47% | 42% | Argentina |
Developed regions, such as Western Europe and North America, show stability, with learners focusing on personal interests and innovative personal projects. In contrast, learners in Latin America are motivated by the opportunity to switch jobs, which reflects fluid job markets but also a lesser emphasis on immediate practical skills. Asia shows a spectrum of motivations. South Korea aligns with career-driven growth, while Japan reports low engagement across learning dimensions, indicating a potential need for policy and cultural shifts. In India and Southeast Asia, learners are motivated to keep up with trends, which reflects the dynamism of their growing tech ecosystems.
Desired job role100+
This question was only shown to respondents who indicated “finding a new job” or “switching roles” as one of their motivations to learn computer science topics.
Developer is the top career choice in IT, most likely a reflection of the role’s versatility, high demand, and broad applicability across industries, making it an optimal choice for career transitions, especially for individuals new to the field. Significant numbers are also branching into data-focused careers or DevOps, showcasing the growing appeal of specialized fields. On the contrary, QA roles, though good for entry, lack popularity and long-term prospects, making them less aspirational for career transitions.
of respondents report they have, at one point, searched for work in computer science/IT.
Important factors when searching for a job in computer science/IT
| Not important | Fairly unimportant | Fairly important | Extremely important | |
|---|---|---|---|---|
| 1% | 6% | 35% | 58% | Work experience |
| 1% | 13% | 51% | 35% | Familiarity with the latest technologies |
| 2% | 16% | 51% | 32% | Soft skills |
| 4% | 17% | 47% | 31% | Internships and co-op programs |
| 6% | 26% | 44% | 25% | Connections and networking |
| 5% | 23% | 48% | 24% | Pet projects |
| 7% | 26% | 49% | 18% | University diplomas |
| 6% | 31% | 47% | 16% | Peer references |
| 9% | 31% | 46% | 14% | Industry certificates |
| 11% | 35% | 42% | 12% | Course completion certificates |
Work experience and up-to-date tech knowledge are reportedly key to landing a job, but soft skills are equally valued, with 83% of learners marking them as important. Networking is another crucial factor – 25% consider it critical, and 44% actively use their connections for career opportunities. This underscores the need for strong interpersonal skills and professional networks in the tech sector.
Learning Topics
Computer science areas studied in the past three years100+
Along with programming languages, algorithms, and data structures, databases are a popular choice for learners. AI and machine learning remain popular fields, with 33% and 34% of learners exploring them, respectively.
Proficiency in computer science areas studied
| Novice / Exploratory | Beginner | Intermediate | Advanced | Expert | |
|---|---|---|---|---|---|
| 4% | 25% | 44% | 23% | 5% | Software engineering |
| 6% | 28% | 41% | 21% | 5% | Web development |
| 8% | 29% | 40% | 17% | 5% | Product management |
| 4% | 23% | 47% | 22% | 4% | Programming languages |
| 10% | 33% | 37% | 16% | 4% | Human-computer interaction (HCI) |
| 9% | 33% | 38% | 16% | 4% | Project management |
| 9% | 37% | 37% | 14% | 3% | Testing |
| 10% | 37% | 35% | 15% | 3% | Data analysis |
| 15% | 42% | 30% | 10% | 3% | Natural language processing (NLP) |
| 16% | 40% | 29% | 11% | 3% | Computer vision |
| 7% | 32% | 41% | 16% | 3% | Databases |
| 9% | 36% | 39% | 13% | 3% | Computer networks |
| 7% | 35% | 38% | 16% | 3% | Operating systems |
| 11% | 40% | 34% | 12% | 3% | Cybersecurity |
| 6% | 31% | 46% | 15% | 2% | Algorithms and data structures |
| 17% | 43% | 27% | 10% | 2% | Artificial intelligence |
| 18% | 43% | 27% | 10% | 2% | Machine learning |
| 16% | 39% | 30% | 13% | 2% | Computer graphics |
Women tend to rate their technical skills lower, yet they demonstrate a strong drive for growth, with 8% more female learners transitioning into computer science from other fields compared to their male counterparts.
Topics learners want to explore in next course
Respondents answered this question with open-text answers. ChatGPT was used to automate the analysis and sorting of the responses into thematic clusters.
Learning Formats, Practices, and Resources
Experience with educational formats100+
The data shows a continued demand for traditional, in-person, hands-on learning environments like university education, workshops, and mentorship programs. However, satisfaction with these formats varies widely across age groups and regions, reflecting inconsistent effectiveness.
Rating of experience with educational formats
| Poor | Needs Improvement | Satisfactory | Very good | Excellent | |
|---|---|---|---|---|---|
| 2% | 5% | 18% | 32% | 43% | Internships |
| 1% | 7% | 22% | 36% | 34% | Mentorship programs and tutoring |
| 2% | 4% | 22% | 42% | 30% | Paid online courses (MOOCs) or code schools |
| 2% | 9% | 23% | 38% | 29% | Professional training provided by an employer |
| 1% | 5% | 25% | 40% | 29% | Self-paced online tutorials |
| 1% | 7% | 28% | 36% | 28% | Codecamps, user groups, meetups |
| 3% | 9% | 29% | 33% | 26% | Vocational programs |
| 4% | 8% | 26% | 37% | 25% | Outsourced professional training, paid for by an employer |
| 2% | 7% | 26% | 40% | 25% | Coding bootcamp sessions |
| 2% | 9% | 31% | 34% | 24% | Exchange programs |
| 1% | 8% | 31% | 38% | 21% | Free online courses (MOOCs) or code schools |
| 3% | 11% | 31% | 36% | 20% | Online university programs |
| 6% | 14% | 31% | 30% | 19% | University, college, school education |
| 2% | 11% | 32% | 36% | 19% | Offline courses, code schools |
| 2% | 10% | 33% | 36% | 19% | Workshops and seminars |
University, college, and school education, as well as self-paced online tutorials, are top answers for all respondents. The rest depends on the specific age group and career needs. Workshops and seminars are most popular among the 50–59 age group, with 17% of such learners having experience with them and about one quarter rating their experience as excellent. Mentorship programs are highly rated by respondents aged 21–29, with 36% of them rating it as excellent, but the satisfaction with this experience declines as age increases. Satisfaction with employer-provided training peaks among respondents aged 18–20, with 41% of learners marking it as excellent. Paid online courses and coding bootcamps appeal most to younger and mid-career individuals.
Familiarity with MOOCs and code schools
| Never heard of it | Aware of it, but never tried it | Tried it, but don’t use it anymore | Currently use it | |
|---|---|---|---|---|
| 18% | 23% | 29% | 29% | Udemy |
| 18% | 27% | 35% | 20% | Coursera |
| 29% | 41% | 15% | 16% | JetBrains Academy |
| 38% | 26% | 25% | 10% | edX |
| 26% | 35% | 29% | 10% | Codecademy |
| 35% | 36% | 20% | 10% | LinkedIn Learning |
| 28% | 33% | 30% | 9% | Khan Academy |
| 52% | 28% | 10% | 9% | Canvas |
| 55% | 28% | 12% | 5% | DataCamp |
| 48% | 32% | 16% | 4% | Udacity |
| 67% | 18% | 11% | 4% | Pluralsight |
| 79% | 13% | 5% | 3% | Stepik |
| 85% | 8% | 4% | 3% | SWAYAM |
| 84% | 11% | 4% | 2% | JavaRush |
| 70% | 22% | 6% | 2% | The Open University |
| 78% | 16% | 5% | 1% | FutureLearn |
| 84% | 12% | 3% | 1% | Egghead |
| 90% | 7% | 2% | 1% | XuetangX |
| 92% | 6% | 2% | 1% | MiríadaX |
| 89% | 8% | 2% | 1% | Cognitive Class |
| 87% | 9% | 3% | 1% | Platzi |
Did you know?
JetBrains Academy users are 24% more likely to rate their experience with paid online courses (MOOCs) or code schools as “Excellent”. Discover your learning options with JetBrains Academy.
Practices for mastering computer science topics100+
Learners exploring computer science prioritize hands-on and visual learning, with coding platforms, video tutorials, and documentation leading the way. However, the recent stats on AI chatbot usage and participation in coding contests imply a shift toward interactive and dynamic approaches to problem-solving and skill-building.
This blend of traditional and modern resources suggests that learners value both structured guidance and opportunities for creative experimentation.
Competitive coding experience
Extensive experience: I regularly compete or have competed a lot in the past
Moderate experience: I’ve participated in a few contests
No interest: I don’t have any experience in it, nor do I want to compete
No experience: I’m new to competitive coding but interested in it
The majority of respondents are new to competitive coding but interested in it, while 30% have some experience and have participated in a few contests or used to compete regularly in the past.
Preferred resources and communities for learning computer science100+
Peer interaction is a key component of CS learning. About one-third of respondents value hackathons and open-source contributions, while a quarter prefer engaging with coding communities for learning. While platforms and tutorials dominate, collaborative and competitive activities inspire deeper engagement.
100+
Learners of all ages rely on various resources for help. Google is the top choice for all ages, while AI assistants like ChatGPT are especially popular among younger users, with two-thirds of those under 29 using them. Younger learners also tend to seek help from friends and educators, while those in their 30s and 40s turn to colleagues. YouTube is widely used across all ages, while older learners prefer textbooks and platforms like Medium. Overall, younger generations balance AI, peer support, and educational media, while older groups favor professional networks, structured articles, and textbooks.
100+
| 18–20 | 21–29 | 30–39 | 40–49 | 50–59 | 60 or older | |
|---|---|---|---|---|---|---|
| 70% | 76% | 77% | 75% | 68% | 68% | |
| 66% | 67% | 55% | 46% | 38% | 35% | An AI-based assistant (ChatGPT or other) |
| 58% | 48% | 31% | 22% | 13% | 9% | Friends and classmates |
| 56% | 65% | 64% | 52% | 37% | 33% | Stack Overflow |
| 53% | 53% | 50% | 50% | 43% | 36% | YouTube |
| 47% | 32% | 21% | 20% | 16% | 8% | An educator / teacher / tutor |
| 23% | 23% | 27% | 32% | 31% | 34% | Textbooks |
| 20% | 25% | 26% | 27% | 18% | 25% | Online tech media (e.g. Medium) |
| 19% | 19% | 18% | 16% | 24% | 14% | People on social media |
| 17% | 31% | 36% | 33% | 29% | 19% | Colleagues |
| 3% | 3% | 3% | 4% | 2% | 9% | Other |