A Disclosure Before the Advice
I should be honest about where I'm writing this from: I have an engineering degree from BITS Pilani, Hyderabad, so I'm not the person this post is nominally for. But most people preparing for CAT today aren't engineers either - commerce, arts, humanities and economics graduates make up a large share of the candidate pool every year, and they regularly land in the 99th percentile and above. What I want to do here is use my own section-level data to make a specific, useful point: an engineering degree is not the advantage it's assumed to be, and I have three months of mock scores that say so.
The Section My Degree Was Supposed to Help With
DILR is the section engineers are assumed to have a natural edge in - structured, logical, puzzle-like. Over three months of deliberate practice, tracked mock by mock, my DILR average moved from 18.4 to 20.6. Two marks. That's not a rounding error hidden inside an improving overall score - it's the flattest line in my entire dataset (the full breakdown is in my mock log), and it happened despite an engineering background that, on paper, should have made this section comfortable from day one.
What actually held DILR back wasn't unfamiliarity with logical structures. It was set selection under time pressure - a completely different skill from "being good at logic puzzles," and one that has nothing to do with what degree is on your transcript. My worst single mock, a 40 with 2 marks in DILR, came from staying too long on the wrong set, not from failing to understand it. See my time-management writeup for exactly what that looked like.
Where the Real Gap Actually Is
The genuine gap for someone without an engineering background is concentrated in Algebra, Geometry, and abstract Number System problems - topics engineers use continuously through a technical degree and everyone else typically hasn't touched since Class 10 or 12. It is not in Arithmetic (Percentages, Profit & Loss, Time-Speed-Distance), which most non-engineers pick up quickly since it's closer to everyday numerical reasoning. And it is not, based on my own numbers above, a guaranteed advantage in DILR either.
It's also not in VARC. My own VARC scores were the most volatile section I tracked all quarter - swinging between the 3rd and 39th raw mark, 12th to 92nd percentile - and an engineering background did nothing to steady that. If anything, a humanities or commerce background that reads dense prose regularly has a real edge here that a technical degree doesn't automatically confer.
A Modified Foundation Phase, If Your Math Is Rusty
If your last formal math exposure was Class 10 or 12, extend Phase 1 of your prep (see my month-by-month study plan) by 3-4 weeks specifically for Quant foundations, while keeping VARC and DILR on the standard timeline since neither depends on rusty math recall.
- Weeks 1-2: NCERT Class 9-10 Algebra and Geometry, worked through slowly with every step written out - don't jump to shortcuts before the underlying method is solid.
- Weeks 3-4: NCERT Class 11 basics for Functions, Logarithms, and Sequences - CAT draws lightly on these, but working knowledge removes an entire category of "I've never seen this before" panic in the exam.
- From week 5: Join standard LOD 1-2 practice alongside everyone else, now with the foundation to actually benefit from it instead of re-deriving basics mid-problem.
Turn Your Background Into a Real VARC and DILR Advantage
Commerce and economics backgrounds often have a genuine edge in DI-heavy sets involving business scenarios, financial ratios, or market data - the context is already familiar in a way it wasn't for me. Humanities backgrounds often read faster and infer more naturally in VARC, since dense academic prose is closer to existing coursework than a typical engineering curriculum is. Lean into this rather than fixating on a perceived Quant deficit - a 95th-percentile VARC score does exactly as much for your overall percentile as a 95th-percentile Quant score, and my own numbers show Quant improving steadily while VARC stayed the noisiest section I track.
What "Engineer" Doesn't Actually Buy You in Practice
I want to push on this a bit more, because the assumption runs deeper than DILR. An engineering degree at BITS Pilani meant four years of calculus and linear algebra, not CAT's actual Quant syllabus - Modern Math (Permutations & Combinations, Probability, Set Theory) and pure Number System puzzles aren't things most engineering coursework revisits after first year. I had to rebuild both from close to scratch, the same way anyone would from an NCERT starting point, just with less anxiety about whether I "should" already know it. The degree bought confidence, not content. If you're a non-engineer worried you're starting from zero, the honest comparison isn't "them at 100%, you at 0%" - it's closer to "them slightly ahead in two topics, roughly even everywhere else."
Realistic Timeline Expectations
A non-engineer starting from a rusty Class 10 baseline in month one, following the extended foundation phase above, can realistically reach LOD 2 comfort by month three and be attempting full mocks at a competitive level by month four - not far off the timeline any of us actually follow, engineer or not, once foundations are accounted for honestly rather than assumed. My own QUANT average took roughly three months of mock-over-mock practice to climb from 26.8 to 35.6; a non-engineer starting that same climb a few weeks later, from a lower base, is running the same curve on a similar clock, not a fundamentally slower one.
The Confidence Trap
The most common failure mode for non-engineers isn't ability - it's avoidance. Quant anxiety leads to under-practicing the section, which is the one thing guaranteed to keep the gap open. Treat early Quant mocks as diagnostic, not evaluative: a low score in the first month of foundation-building is expected progress, not a signal you're unsuited for the exam. Track Quant accuracy trend specifically, not just overall mock score - it's the only way to see real, usually steady, improvement underneath what feels like a discouraging plateau.
Tracking Progress When the Starting Point Is Different
Because the useful comparison is your own trend, not a generic benchmark, tracking topic-level accuracy over time matters more when your starting point is different from the median candidate's - it's the only way to see a foundation phase actually paying off before mock scores catch up. Karma Yogi's topic tags and accuracy trends make that visible from week one, the same way they showed me a flat DILR line I would have otherwise missed for two months. Start tracking your own foundation free.
End of essay
- Anish Guruvelli