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Provides an overview of key topics including Big-O notation, arrays and strings, linked lists, stacks and queues, plus recursion, dynamic programming, trees and graphs, sorting, and mock interview question practice. 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Solid knowledge of the theory (data structures and algorithms)  \n2. Knowing how to approach and behave in the interview  \n3. Practice!  \nOverview of Today  \n Big-O notation  \n Arrays and strings  \n Linked lists  \n Stacks and queues  \nBreak  \n Recursion and dynamic programming  \n Trees and graphs  \n Sorting  \n Overview of the coding interview  \n Mock interviews and interview question practice  \nBig-O Notation  \nWhich algorithm is fastest?  \nFor a given problem, there might be multiple valid solution approaches.  \nHow are we going to decide which is best for our use case?  \nWe could try each method out and choose the fastest?  \nWe need a way of determining the ef×ciency of an algorithm without needing to implement it  \nBig-O notation  \nBig-O notation is the typical way to measure the complexity of an algorithm  \nWe measure how the algorithm performance scales with respect to the size of the input. It provides a worst-case analysis of the algorithm.  \nWe typically measure two aspects of performance:  \n time complexity (how long does it take to run?)  \n space complexity (how much memory space does it require?)  \nExamples (all for time complexity and an input of size n):  \n O (n) means the time taken can be as much as a linear function of n  \n O (log n) means the time taken can be as much as a linear function of log n  \n O (1) means the time taken is independent of n  \nSome big-O examples  \nFinding the minimum element of an array:  \nIn [ ] :  \nminval = float ( 'inf ' ) for val in array :  \nminval = min(minval , val)  \n Time complexity is O(n) (have to check each value once)  \n Space complexity is O(1)  \nCalculating 1-norm of square matrix:  \nIn [ ] :  \ntotal = 0  \nfor i in xrange (n ) :  \nfor j in xrange (n ) :  \ntotal += abs (X [i][j ] )  \n Time complexity is O(n2 )  \n Space complexity is O(1)  \nSimplifying big-O  \nWhen we use big-O, we always reduce the notation into its simplest form. We only care about measuring how the algorithm scales with the input size, not calculating the exact complexity as a function of input size.  \nCalculating min and max of array:  \nIn [ ] :  \nminval = +float ( 'inf ' )  \nmaxval = -float ( 'inf ' ) for val in array :  \nminval = min(minval , val)  \nmaxval = max (maxval , val)  \nEven though there are 2n operations here, the time complexity is still O(n) -we ignore the constant factors","cbCaiaSjcBDzXqUU","https://ap.wps.com/l/cbCaiaSjcBDzXqUU","pdf",380330,82,"English","# Preparing for Coding Interviews\n## Why Coding Interviews\n## How to Prepare\n## Overview of Today: Key Topics\n# Big-O Notation\n## Measuring Complexity\n## Time vs Space Complexity\n## Big-O Examples\n## Simplifying Big-O","[{\"question\":\"Why do coding interviews differ from normal interviews?\",\"answer\":\"Coding interviews are largely based on testing programming ability through coding a solution during the interview. The main risk is that without preparation, performance is likely to be poor.\"},{\"question\":\"What are the three important aspects of preparing for a coding interview?\",\"answer\":\"Preparation focuses on three areas: solid theory (data structures and algorithms), knowing how to approach and behave in the interview, and practicing through problems.\"},{\"question\":\"What does Big-O notation measure in coding interviews?\",\"answer\":\"Big-O notation describes algorithm complexity by showing how performance scales with input size, typically using worst-case analysis. It is used to reason about time complexity and space complexity without implementing the algorithm.\"}]","MBAn Technical Interview Skills Training Workshop - Part 1 - Coding Interview Preparation | PDF",1790350207,207]