Graduate Aptitude Test in Engineering — Statistics
IIT Roorkee (2027) · Online (CBT) · Annual (February)
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GATE ST Answer Key
Official and expected answer keys for GATE ST 2026. Check set-wise solutions and objection window details.
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Table of Contents
About Graduate Aptitude Test in Engineering — Statistics (GATE ST) 2027
The Graduate Aptitude Test in Engineering — Statistics (GATE ST) is one of the most competitive entrance exams, conducted by the IIT Roorkee (2027). Preparing for the GATE ST requires systematic practice, conceptual clarity, and regular self-evaluation. Our platform offers dynamic mock tests, previous years' resolved papers, and AI-enabled analysis to help you secure a top rank.
Frequently Asked Questions
What careers does GATE ST open?
GATE ST qualifies you for MTech in Statistics and Data Science at IITs, researcher positions at ISI (Indian Statistical Institute), CSIR, RBI, and data science roles at central government organisations and PSUs.
Is GATE ST the same as ISI entrance?
No, they are different exams. GATE ST qualifies you for IIT/NIT MTech admission and PSU statistical positions. ISI entrance exam is for MStat and MTech (CS) programmes at ISI Kolkata, Delhi, and Bangalore — generally harder.
What is GATE ST 2027 expected cutoff?
GATE ST qualifying cutoff for General category is typically 25-35 out of 100. It is a relatively newer GATE paper with fewer aspirants — around 8,000-10,000 per year — making ranks more accessible.
What is the scope of GATE ST for data science?
GATE ST is valuable for data scientist roles in government organisations (NSSO, RBI, NABARD). For private sector data science, IIT MTech through GATE ST provides industry-relevant training through project work and placements.
Which IITs offer MTech in Statistics through GATE ST?
IIT Bombay, IIT Kanpur, IIT Kharagpur, and IIT Delhi offer MTech/MS in Statistics, Applied Statistics, or related programmes. These are excellent programmes for quantitative finance, actuarial science, and research careers.
How to Prepare for GATE ST
Probability Theory (Distributions, Limit Theorems, Generating Functions) is the foundation — start here before other topics
Statistical Inference (Estimation, Hypothesis Testing, Confidence Intervals) carries the highest weightage — solve Casella & Berger exercises
Stochastic Processes (Markov Chains, Poisson Process) is increasingly important in the data science era — master it
Linear Regression and Design of Experiments are relatively formula-based — create a cheat sheet and practice numerical problems