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GeeksforGeeks brings you everything you need to crack GATE 2026 โ 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
Whatโs inside?
โ Live & recorded classes with Indiaโs top educators
โ 200+ mock tests to track your progress
โ Study materials - PYQs, workbooks, formula book & more
โ 1:1 mentorship & AI doubt resolution for instant support
โ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav โ Trained 20K+ students
Dr. Khaleel โ Ph.D. in CS, 29+ years of experience
Chandan Jha โ Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal โ M.Tech (NIT), 13+ years of experience
Sakshi Singhal โ IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh โ GATE 99.24 percentile
Devasane Mallesham โ IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
๐ Enroll for a free counseling session now: https://gfgcdn.com/tu/UI2/
๐2
๐กUse ZIP function to iterate over multiple lists simultaneously ๐ก
#pythontips #codingtips #python #pythonprogramming #codesmarter #coding
#pythontips #codingtips #python #pythonprogramming #codesmarter #coding
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Tech interviews ask candidates to invert binary trees while their real job is 90% figuring out why a 3rd-party API returns null sometimes.
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๐ป ๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ ๐๐ถ๐ด ๐ข ๐ป๐ผ๐๐ฎ๐๐ถ๐ผ๐ป!
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.
O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.
O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.
O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.
O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.
O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.
O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
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