What actually stops people from retraining for a tech career?

Two things, in a near-exact dead heat. Asked what is holding them back, 42% of 325 applicants name money or cost and 42% name fear, missing knowledge or self-doubt about their own ability (13 people name both, and are counted in each). Time is a distant third at 7%. This matters because the two barriers need completely different responses: the financial one is addressed with pricing, instalments and free tiers, and the psychological one is addressed with evidence, low-stakes trials and honest teaching. Programmes that only solve one of them are, on this data, solving half the problem.

Can most people afford to retrain?

On this evidence, most cannot pay upfront. Of 262 applicants who answered a question about investment readiness, 47% said they were only looking for free resources, 28% said they needed instalment or payment options, 18% said they were ready to invest if it was the right fit, and 7% needed to discuss it with family or an employer. Read plainly: roughly three-quarters of people who apply to retrain cannot or will not pay a lump sum. As a school this is an uncomfortable number to publish, and we think it is the single most useful figure here for anyone designing reskilling programmes or policy.

How much time do people realistically have to retrain?

Far less than full-time study assumes. 42% say they can give 10 to 25 hours a week, 33% say under 10 hours, and only 25% say 25 or more. That means 75% of applicants are working with under 25 hours a week - and a third are effectively exploring in the margins of their life. Any programme, timeline or piece of advice that assumes a full-time commitment is addressing a quarter of the people who want to do this.

Do people applying to retrain already have technical backgrounds?

Mostly not. 64% did not study computer science or IT: the largest non-CS groups are business, finance and management (21%) and a broad "other fields" category (25%), with art and design, natural sciences, social sciences, and medicine and health making up the rest. On formal qualifications, 54% hold a bachelor's degree and 10% a master's, while 35% apply with a diploma, a high-school certificate or less. The median applicant age is 27, the mean is 29, the range runs from 15 to 67, and 60% are 26 or older.

Is everyone retraining in order to get a job?

No, and the split is closer than expected. 34% give getting hired or getting a stable job as their main reason - but 30% say they want to build their own startup or freelance, and 19% want to apply AI or tech within the role they already have rather than leave it. 11% describe themselves as just exploring, and 5% name working remotely. Put together, nearly half are not primarily seeking employment from someone else. Framing reskilling purely as a placement pipeline misses a substantial share of the people actually doing it.

Once enrolled, do people study full-time or around a job?

Around a job, overwhelmingly. In a separate table of 553 enrolled students, the programme format chosen is recorded for 520: part-time (30%) and self-paced (25%) together account for 55%, against 26% choosing full-time study online or in person. Payment behaviour points the same way - of 456 records with at least one payment logged, 23% paid across more than one instalment rather than in a single sum. Stated constraint and revealed behaviour agree: this is predominantly a part-time, paid-over-time activity, not a full-time one.

How much can enrolled students already do when they start?

Very little, by their own assessment. Of 113 enrolled students who rated their prior programming and data experience, 56% chose Level 0 - no coding or data experience at all - and a further 31% chose Level 1, meaning some beginner tutorials. That is 87% starting at or near absolute zero, with only 13% able to build anything. On goals, from the same group: 62% want a stable full-time job, but 50% explicitly name working remotely, 34% want to freelance, 25% want to build their own startup, and 15% want to improve their current non-tech role rather than leave it.

Can I cite or reuse this data?

Yes. The aggregate figures on this page are published under a Creative Commons Attribution 4.0 licence: quote, chart or republish them, including commercially, with attribution to Sigmaschool and a link to this page. Every figure states the number of records behind it. For category definitions, the coding rules used on free-text fields, or a correction, contact support@sigmaschool.co.

← Sigmaschool Research

Original research · Edition 1 · August 2026

What stops people retraining for tech? Money and fear, in a dead heat.

Reskilling is usually discussed as a supply problem: build the courses and people will come. We analysed 325 complete applications from people trying to make exactly that move, and asked what was actually stopping them. 42% said money. 42% said fear or not knowing enough. The two barriers are the same size.

Snapshot taken 25 August 2026. 325 records with complete responses on every core field. Composition and barriers only - no placement, salary or employment-outcome claims. Free to read, cite and republish under CC BY 4.0.

42%

name money or cost as what is holding them back (n=325)

42%

name fear, knowledge or self-doubt - an exact dead heat with money (n=325)

75%

have under 25 hours a week to give it (n=325)

64%

did not study computer science (n=325)

Finding 01

Money and self-doubt block people equally.

Self-reported answer to 'what is your biggest blocker?', coded into categories (n=325, complete).

Money or cost125 · 38%
Fear, knowledge or skills123 · 38%
Other or unstated40 · 12%
Time24 · 7%
Both money and fear together13 · 4%

Counting the 13 people who named both, 138 applicants (42%) mention money and 136 (42%) mention fear, missing knowledge or doubt about their own ability. Time is a distant third at 7%. We did not expect the two to land within two records of each other.

The significance is not the tie itself but what it implies: these two barriers need completely different answers. The financial one responds to pricing, instalments, free tiers and subsidies. The psychological one responds to none of those - it responds to evidence, low-stakes ways to try before committing, and teaching that treats confusion as expected rather than disqualifying. A programme, an employer scheme or a national policy that solves only one of them is, on this data, addressing about half the problem.

It is also worth noting how ordinary the second barrier sounds when people describe it. Nothing in these answers is exotic; it is overwhelmingly some version of I am not sure I can do this. That is the same conclusion we reached from a different dataset in am I smart enough for tech, and it appears to be close to universal among people who go ahead and apply anyway.

Finding 02

Nearly half can only take free.

Self-reported investment readiness at the point of applying (n=262 of 325 answering).

Only looking for free resources123 · 47%
Needs instalment or payment options74 · 28%
Ready to invest if it is the right fit47 · 18%
Needs to discuss with family or employer18 · 7%

47% say they are only looking for free resources. A further 28% need instalments or payment options. 18% are ready to invest if the fit is right, and 7% need to discuss it with family or an employer.

Roughly three-quarters of people applying to retrain therefore cannot or will not pay a lump sum. This is an uncomfortable statistic for a school to publish about its own applicants, and we are publishing it because it is the most useful number on this page for anyone outside our commercial interest: if you are designing a reskilling programme, an employer-funded scheme or a public policy, the modal participant has no budget. Free and instalment routes are not a marketing tactic in this market. They are the access condition for most of the people who want in.

Finding 03

Three-quarters have under 25 hours a week.

Realistic weekly time commitment, self-reported (n=325, complete).

10-25 hours a week - part-time pace137 · 42%
Under 10 hours a week - exploring108 · 33%
25+ hours a week - full-on80 · 25%

42% can give 10 to 25 hours a week, 33% under 10, and only 25% can commit 25 hours or more. Three in four are therefore fitting this around a job, a family or both - and a third are working in the genuine margins of their life.

This is the finding least tied to any one country. Education systems and job markets differ; the fact that adults with rent and dependants cannot simply stop earning does not. Any programme, timeline or piece of career advice built around full-time study is speaking to a quarter of the people who want to make this move - which is the practical case we set out in learning to code while working full-time.

Finding 04

Median age 27. 64% never studied computer science.

Age, field of study and highest qualification at application (n=325, complete).

Age: median 27, mean 29, range 15 to 67. 60% are aged 26 or over; 36% are over 30.

Field of study

Computer science / IT117 · 36%
Other fields82 · 25%
Business, finance or management67 · 21%
Art or design21 · 6%
Natural sciences16 · 5%
Social sciences14 · 4%
Medicine or health8 · 2%

Highest qualification

University or bachelor's degree177 · 54%
High school or earlier65 · 20%
Diploma or technical school50 · 15%
Master's degree or higher33 · 10%

64% did not study computer science or IT. The largest non-CS groups are business, finance and management (21%) and a broad other-fields category (25%), with art and design, the sciences and medicine making up the rest. On credentials, 54% hold a bachelor’s and 10% a master’s, while 35% apply with a diploma, a high-school certificate or less.

On language, 47% describe their English as professional and 22% as native - but 28% describe it as basic and 3% as none, and they are applying to an English-language technical programme anyway. That is a barrier worth naming in a region where most technical material is English-only.

84% of applications give a Malaysian location. The remaining 16% span Hong Kong, the Philippines, India, Pakistan, Ghana, Nigeria and the United Kingdom among others, so this is predominantly but not exclusively a Malaysian picture.

Finding 05

Nearly half are not primarily looking for a job.

Main stated reason for applying (n=325, complete).

Get hired, or get a stable job112 · 34%
Build their own startup or freelance96 · 30%
Apply AI or tech in their current role63 · 19%
Just exploring35 · 11%
Work remotely17 · 5%

Getting hired leads at 34% - but only just. 30% want to build their own startup or freelance, and 19% want to apply AI or technology inside the role they already hold rather than leave it. Add the 11% exploring and the 5% whose primary goal is remote work, and nearly half are not principally seeking employment from someone else.

Reskilling is routinely measured as a placement pipeline, with success defined as a job at the end. On this data that definition would misclassify a third of participants as failures for achieving precisely what they came for. It is also why we treat the one-person-product route as a real destination rather than a consolation - the map is in what can I build with AI.

Finding 06

What they do once they commit - and it matches what they said.

A second, separate table of 553 enrolled students. Coverage varies by field and each figure states its own n.

Programme format chosen (n=520)

Part-time158 · 30%
Self-paced129 · 25%
Full-time, online88 · 17%
Full-time, in person49 · 9%
Other programmes96 · 18%

55% choose part-time or self-paced, against 26% choosing full-time study. That is the same picture the application data gave from a different angle: three-quarters said they had under 25 hours a week, and three-quarters behave accordingly once enrolled.

Payment behaviour agrees as well. Of 456 records with at least one payment logged, 23% paid across more than one instalment rather than in a single sum. Set that against the stated constraint from the application stage - 28% said they needed instalment options - and stated need and revealed behaviour land close together. People are, on this evidence, telling the truth about what they can afford.

Prior programming experience at enrolment (n=113)

Level 0 - no coding or data experience at all63 · 56%
Level 1 - completed basic beginner tutorials35 · 31%
Level 2 - can build simple applications10 · 9%
Level 3 - has built and deployed applications4 · 4%
Level 5 - working professional improving skills1 · 1%

56% report no coding or data experience whatsoever, and a further 31% only beginner tutorials - 87% at or near absolute zero, with just 13% able to build anything at all. Read next to the median age of 27, this describes someone several years into a working life who has never written a line of code.

What they want out of it (n=113, multiple answers allowed)

Get a stable, full-time job70 · 62%
Work remotely56 · 50%
Freelance and build for clients38 · 34%
Build my own tech startup28 · 25%
Improve my current non-tech role17 · 15%
Manage developers effectively9 · 8%
I don't know yet9 · 8%

A stable full-time job leads at 62%, but the striking number is second: 50% explicitly name working remotely - half the sample treating location independence as an objective in its own right rather than a perk. 15% want to improve the non-tech role they already have rather than leave it, which again suggests measuring reskilling purely by placement misses part of the point.

Limitations

What this report does not claim.

No outcome, placement or salary claims.

This report describes people at the moment they apply - who they are, what they want and what is stopping them. It says nothing about what happened to them afterwards. We hold outcome records, but they were built to run a school rather than to be published, and we do not consider them structured well enough to support a defensible placement figure. Rather than publish a number we could not stand behind, we publish none.

Who is in this sample. These are 325 people who completed an application to one school. They are already motivated enough to apply, which makes them unrepresentative in an important way: the barriers reported here are the barriers of people who got as far as applying despite them. Anyone stopped earlier is invisible to this data, and their barriers may well be larger.

Self-report. Age, qualification, time available, ability to pay and blockers are all self-declared on an application form and are not verified. Investment readiness in particular may be understated by applicants who reasonably expect that saying so improves their position.

Single school, mostly one country. 84% of records give a Malaysian location. A programme with different pricing, marketing or admissions would draw a different population. Treat this as a well-documented case study rather than a national or global statistic - and note that we have an obvious commercial interest in the subject, which is why every denominator sits next to its numerator.

Methodology

How this was produced.

Source.The complete applications table from Sigmaschool’s internal CRM, exported on 25 August 2026: 325 records, each a submitted application form. Every core field analysed here - age, qualification, field of study, English proficiency, weekly hours, main reason and blocker - is present on all 325 records. Investment readiness is present on 262.

Second source. Finding 06 draws on a separate internal table of 553 enrolled students, exported the same day. Programme format is recorded for 520 of them and payment records for 456; the onboarding survey fields (prior experience, goals) were completed by 113. Those coverage rates are much lower than the application table's, which is why the enrolled-stage figures are reported second and each carries its own n.

Processing. Free-text and select answers were normalised before counting: differing dash characters, casing and spelling variants of the same option were merged (for example, three spellings of the same hours band). The blocker field was coded into five categories by keyword; answers naming both money and knowledge are shown as their own row and are also counted inside each of the two headline percentages, which is why those two figures (42% and 42%) exceed their individual rows. Percentages are rounded and may not sum to exactly 100.

An analysis we ran and did not publish. A separate enrolment table records cohort join dates, and pooling them by calendar month appears to show a dramatic December peak. It is an artefact: only 84 distinct dates exist across 507 records, 55% fall on ten dates, and one date alone carries 119 - the signature of fixed cohort start dates and a bulk data migration, not of individual decisions. We mention it because the chart was tempting and the finding would have been false.

Privacy. No names, contact details, employers, addresses or individual records are published. Every figure is an aggregate over the stated n, and no figure is derived from fewer records than it declares.

Corrections and licence. If you spot an error, write to support@sigmaschool.co and a dated correction will be published here. Figures are released under CC BY 4.0 . Quote them, chart them, republish them - including commercially - with attribution and a link. We ask only that the caveats travel with the numbers.

Questions

Frequently asked.

What actually stops people from retraining for a tech career?
Two things, in a near-exact dead heat. Asked what is holding them back, 42% of 325 applicants name money or cost and 42% name fear, missing knowledge or self-doubt about their own ability (13 people name both, and are counted in each). Time is a distant third at 7%. This matters because the two barriers need completely different responses: the financial one is addressed with pricing, instalments and free tiers, and the psychological one is addressed with evidence, low-stakes trials and honest teaching. Programmes that only solve one of them are, on this data, solving half the problem.
Can most people afford to retrain?
On this evidence, most cannot pay upfront. Of 262 applicants who answered a question about investment readiness, 47% said they were only looking for free resources, 28% said they needed instalment or payment options, 18% said they were ready to invest if it was the right fit, and 7% needed to discuss it with family or an employer. Read plainly: roughly three-quarters of people who apply to retrain cannot or will not pay a lump sum. As a school this is an uncomfortable number to publish, and we think it is the single most useful figure here for anyone designing reskilling programmes or policy.
How much time do people realistically have to retrain?
Far less than full-time study assumes. 42% say they can give 10 to 25 hours a week, 33% say under 10 hours, and only 25% say 25 or more. That means 75% of applicants are working with under 25 hours a week - and a third are effectively exploring in the margins of their life. Any programme, timeline or piece of advice that assumes a full-time commitment is addressing a quarter of the people who want to do this.
Do people applying to retrain already have technical backgrounds?
Mostly not. 64% did not study computer science or IT: the largest non-CS groups are business, finance and management (21%) and a broad "other fields" category (25%), with art and design, natural sciences, social sciences, and medicine and health making up the rest. On formal qualifications, 54% hold a bachelor's degree and 10% a master's, while 35% apply with a diploma, a high-school certificate or less. The median applicant age is 27, the mean is 29, the range runs from 15 to 67, and 60% are 26 or older.
Is everyone retraining in order to get a job?
No, and the split is closer than expected. 34% give getting hired or getting a stable job as their main reason - but 30% say they want to build their own startup or freelance, and 19% want to apply AI or tech within the role they already have rather than leave it. 11% describe themselves as just exploring, and 5% name working remotely. Put together, nearly half are not primarily seeking employment from someone else. Framing reskilling purely as a placement pipeline misses a substantial share of the people actually doing it.
Once enrolled, do people study full-time or around a job?
Around a job, overwhelmingly. In a separate table of 553 enrolled students, the programme format chosen is recorded for 520: part-time (30%) and self-paced (25%) together account for 55%, against 26% choosing full-time study online or in person. Payment behaviour points the same way - of 456 records with at least one payment logged, 23% paid across more than one instalment rather than in a single sum. Stated constraint and revealed behaviour agree: this is predominantly a part-time, paid-over-time activity, not a full-time one.
How much can enrolled students already do when they start?
Very little, by their own assessment. Of 113 enrolled students who rated their prior programming and data experience, 56% chose Level 0 - no coding or data experience at all - and a further 31% chose Level 1, meaning some beginner tutorials. That is 87% starting at or near absolute zero, with only 13% able to build anything. On goals, from the same group: 62% want a stable full-time job, but 50% explicitly name working remotely, 34% want to freelance, 25% want to build their own startup, and 15% want to improve their current non-tech role rather than leave it.
Can I cite or reuse this data?
Yes. The aggregate figures on this page are published under a Creative Commons Attribution 4.0 licence: quote, chart or republish them, including commercially, with attribution to Sigmaschool and a link to this page. Every figure states the number of records behind it. For category definitions, the coding rules used on free-text fields, or a correction, contact support@sigmaschool.co.

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