Crack Coding Interview Pattern: Sliding Window 2024

Name: Crack Coding Interview Pattern: Sliding Window 2024
Rating: 4.5 (1 reviews)

Part of Series focused on solving 30 Sliding Window Pattern Problems asked on all Top Tech Coding Interviews

Created bySunil Mohan

Last updated 7/2024

English

What you'll learn

Solve Easy to Hard Difficulty problems using different data structures and algorithms
Breaking down the coding interview problems in a step by step, systematic manner and provided explanation, different approaches, pseudo code and python code
How to solve all possible types of problems asked by major tech companies that can be solved using Sliding window logic
Create logic building capability to solve coding interview problems efficiently

Course content

4 sections • 25 lectures • 3h 57m total length

Brute Force Approach7:16
Explore brute force and sliding window approaches to compute averages of all contiguous subsets of size three in an input array, and implement the Python solution.

String Section0:31
Explore sliding window techniques for string problems, expanding from earlier number-input tasks to string inputs and string-based challenges.
Longest Possible Substring14:04
Explore the longest substring with m unique characters using a sliding window and a hash map to track counts, updating the max length as the pointers move.
Challenge Problem : Fruit Problem0:19
Tackle a challenge section problem labeled fruit problem, read the problem and try to solve it, with solution discussion in the next section and guidance from previous videos.
Solution : Fruit Problem0:42
Apply the sliding window technique to the fruit problem, using the same solution as the longest substring problem to maximize the number of fruit in a list.
Find Unique Characters7:56
Explore how to find the length of the longest substring without repeating characters using a sliding window and a hash map, updating the start pointer and indices.
Find Permutations16:26
Challenge : Find Anagrams in String0:22
Solution : Anagrams in String1:35
Find all anagrams of a given pattern in a string using the permutation approach and return their starting indices, as shown by examples like 0 and 6.
Minimum Window Substring32:41
Concatenation of All Words26:37
Identify all substrings formed by concatenating the given equal-length words and return their starting indices, using a sliding window and hash map to enforce exact word frequencies.

Solve Together Section0:37
Join the solve together section to tackle a mix of problems beyond strings and numbers and practice solving them collaboratively after attempting the question on your own.
Best Time to Buy and Sell Stock7:26
Apply a sliding window approach to the best time to buy and sell stock, maximizing profit by tracking the minimum price before a future maximum, returning zero if no profit.
Binary Subarrays Sum19:51
Subarrays with K different integers20:39
Apply the sliding window approach to count subarrays with exactly k distinct integers by computing at most k and subtracting at most k-1 using two-pointer techniques.
Max Consecutive Ones5:04
Min Swaps to Group All 1s Togeher6:20
Apply a sliding window strategy to minimize swaps by grouping all ones in a binary array, with window size equal to the ones count and zeros counted for swapping.
Diet Plan Performance11:09
Get Equal Substrings Within Budget8:01
Replace the Substring for Balanced6:24
Apply a sliding window approach to replace the shortest substring so that each of the four characters q, w, e, r appears equally n/4 times, balancing the string.
Count Number of Nice Subarrays5:23
Learn to count subarrays with exactly k odd numbers using a sliding window approach, counting at most k and subtracting at most k-1 to obtain precise results.
Number of Substrings Containing all Three Characters13:15
Practice Problems

Requirements

Basic Knowledge of fundamental data structures and algorithms is preferred
Basic Knowledge of python is preferred
If you want to code yourself, you should be familiar with leetcode and or jupyter notebook or any python ide

Description

It's usually difficult to start preparing for coding interviews immediately with limited knowledge of Data Structures and Algorithms. Through grinding on leetcode for FAANG interviews, candidates have started decoding the patterns behind the questions which instantaneously directs them to apply the most efficient logic interviewer is testing us on. Understanding the interview questions based on the patterns and practicing problems having the same patterns helps prepare for interviews faster, with less effort, more confidence, with less chance of forgetting and/or getting confused during the interviews. Interviewers usually test us on multiple patterns of coding interviews. This course covers one type of pattern where we discuss most types of problems that can be solved using sliding window. It’s a very common pattern asked in interview questions. The plan is to create a series of such courses which will cover all the patterns asked in the interview. Completing this course will strong foundation for sliding window. I have tried to make this course brief and have divided the course to 4 hours of video lectures and 4 hours of self-practice assignments, since there are quite a few patterns and candidate may not want to dedicate all time to learning just one pattern.

Who this course is for:

Getting ready for your software engineering coding interview? This is the place for you.
This Sliding Window Series is one part of Series of Solving Coding Interview Questions. This covers all sliding window and discusses some alternative approaches
Python knowledge is not mandatory, Course discusses logic to solve, pseudo code and python code, students can try coding in any language of their choice while referring to this course
Want to get better at coding interviews?
Strengthen your competitive programming muscles

Crack Coding Interview Pattern: Sliding Window 2024

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 7min

What is Sliding window?3 lectures • 25min

String Problems10 lectures • 1hr 41min

Solve Together11 lectures • 1hr 44min

Requirements

Description

Who this course is for: