Analytics is the broadest-degree early-career pool in India, and most enterprise teams still source it as though it were an engineering role.
That leaves a large, capable and less-contested segment of the market untouched.
Live from our candidate database
Counted at render time and refreshed every six hours. This is a supply-side view of one platform, not a national labour-market statistic.
Skills present in this pool
The degree tells you less than you think
Entry-level analytics work is SQL, spreadsheet fluency, a visualisation tool and the ability to explain what a number means to someone who did not ask a technical question. None of these is the exclusive property of an engineering degree.
Commerce and economics graduates frequently arrive stronger on the business-reasoning half and weaker on the tooling half — which is the easier half to teach.
What to screen instead
Test SQL live, at a working level: joins, aggregation, a window function, and a question that requires them to decide what to compute rather than just how. This single exercise separates the pool more effectively than any credential.
Then ask them to interpret a chart badly drawn on purpose. Candidates with genuine analytical instinct notice the problem; candidates who have only learned tools describe what the chart says.
Excel is still a real differentiator
In BFSI, operations and much of consulting, the working medium remains the spreadsheet. Candidates with genuine Excel depth — lookups, pivots, cleanup at speed — are more immediately productive than candidates who can only work in Python.
Screening this is easy and rarely done: give them a messy sheet and fifteen minutes.
Where to source
Widening beyond engineering campuses is the highest-return sourcing change available in this category. Commerce and statistics departments have placement infrastructure that is typically far less contested by technology employers.
The pool page for data analytics shows which degrees are actually present, which is the fastest way to see how much of this segment you have been ignoring.
Key takeaways
- Entry-level analytics skills are not exclusive to engineering degrees; commerce and economics graduates are under-sourced.
- A live SQL exercise that requires deciding what to compute separates the pool better than any credential.
- Excel depth remains a genuine differentiator in BFSI, operations and consulting.
- Commerce and statistics departments are far less contested by technology employers.
Questions
Do non-engineering candidates need Python?+
For most entry-level analytics roles, no — SQL and a visualisation tool cover the work. Python becomes necessary as the role moves towards data science, which is a different pool.
How long to make a commerce graduate productive in analytics?+
Tooling gaps close in weeks with structured onboarding. Business-reasoning gaps, which engineering graduates more often have, take considerably longer to close.
Should we run separate funnels by degree?+
No — run one funnel with a degree-blind screen. Splitting by degree reintroduces exactly the filter that costs you the under-sourced segment.