
Jump into Python-based data structures and algorithms with LeetCode top 150 questions, learn problem understanding, brute force, optimized solutions, and complexity analysis to master arrays and interview readiness.
Explore a course timeline covering arrays and strings, two pointers, sliding window, data structures, graphs, trees, and dynamic programming, including one- and multi-dimensional dynamic programming.
Develop mastery in coding interviews by understanding core patterns and concepts in dynamic programming, graphs, trees, and recursion, then apply time-bound practice and LeetCode contests.
Merge two sorted arrays num1 and num2 by taking the first m and n elements, append them to num1, then sort to return the merged array.
Remove the occurrences of a given value from an array in-place, overwriting nonmatching values and returning the count of elements not equal to val in linear time.
Remove duplicates from a sorted array in place, preserving order and returning the count of unique elements. Use a dictionary to track seen values and overwrite the array with uniques.
Learn to remove duplicates from a sorted array allowing at most twice, using a dictionary to count occurrences, replace values in place and return the length.
Identify the majority element in an array by counting unique values and returning the one whose count exceeds n/2. Implement a Python solution using set(nums) and counts.
Rotate the array to the right by k steps, using k modulo length to handle large values and modify nums in place with a slicing approach.
Identify the buy and sell days by scanning the price array in a single loop to find the lowest price before the highest price later, then compute the maximum profit.
Learn to maximize profit with multiple stock trades by buying on days when prices rise and selling the next day, summing all positive price differences in a single pass.
Employ a greedy back-to-front method for the jump game, using a moving goal to decide reachability in O(N) time with a Python implementation.
Learn Jump Game II in a zero-indexed array and compute the minimum jumps to reach the last element using a greedy approach with L, R, and furthermost in Python.
Compute h-index from a citations array by sorting in descending order and using enumerate to return the index where index >= citation value in Python, or list length if none.
Discover how to compute the product of an array except self without division by using prefix and postfix multiplication in O(n) time with two lists.
this lecture demonstrates solving gas station problem on a circular route using python, showing that total gas must meet or exceed total cost and applying a pass to locate start.
Apply a greedy two-pass approach to assign candies by ratings, using left-to-right and right-to-left passes in Python, and sum the minimum candies needed.
Explore how to compute trapped rainwater on an elevation map using Python by applying min(max left, max right) minus height, and compare array-based and two-pointer approaches for efficiency.
Map roman numerals to values and loop the string, applying the subtractive rule when a smaller value precedes a larger one. Implement in Python with a dictionary using O(n) time.
The lecture shows converting integers to roman numerals using a hashmap, iterating in descending order, subtracting values, and appending symbols; it covers subtractive forms like XL and IX.
Learn to compute the length of the last word by stripping leading spaces, splitting on whitespace, and returning the length of the last element.
Find the longest common prefix among an array of strings using a character-by-character check in Python, updating a prefix index until a mismatch ends the search.
Learn to reverse words in a string with Python by stripping leading and trailing spaces, splitting by spaces, filtering out empty tokens, reversing, and joining back into a string.
Discover how to map a string into a zigzag pattern across a set of rows and print the result using a direction-driven row-wise Python approach.
Find the first occurrence of a needle in a haystack by iterating indices and comparing slices, returning the index or -1 if not found or if the needle is empty.
Explore text justification by greedily packing words into lines of fixed width, distributing spaces for full left and right justification, including last line left-justified behavior in Python.
Explore validating a palindrome in python by converting to lowercase, ignoring non-alphanumeric characters with isalnum, and using a two-pointer approach to compare characters in linear time.
Use a two-pointer technique in Python to check if s is a subsequence of t, advancing the second pointer until matching characters, and returning true when s is fully matched.
Use a two-pointer approach on a sorted array to find two numbers that sum to the target, adjusting pointers based on the sum and returning one-based indices in Python.
Use a two-pointer approach on a height array to solve the container with most water, computing min(height[i], height[j])*(j-i) and moving the smaller height pointer in Python in linear time.
Sort the array and use a three-pointer approach to find all triplets that sum to zero, with duplicates removed and a Python implementation.
Use the two-pointer sliding window to compute the minimum-length subarray with sum at least the target in a positive-integer array, returning zero if impossible.
Explore the longest substring without repeating characters using a two-pointer approach and a dictionary to track seen characters, updating the left pointer and maximum length, with a Python solution.
Identify substrings that are concatenations of all given words by using a dictionary of same length words, sliding through s with a Python solution and attention to complexity.
Learn to solve the minimum window substring problem by using two dictionaries to track frequencies and a two-pointer sliding window to find the smallest s-window containing all t characters.
Ensure sudoku board validity by checking rows, columns, and 3x3 subboxes for digits 1–9 without repetition, ignoring empty dots, using Python with sets and a dictionary.
Learn to extract all elements of an n x n matrix in spiral order using a Python function, tracking boundaries k and l, with an overall time complexity of O(N).
Identify zeros in the m by n matrix and mark their rows and columns with trackers, then zero those rows and columns in place.
Learn the game of life on an m by n grid, applying eight-neighbor rules to update cells in place using a dictionary to track neighbor counts.
Determine if a ransom note can be formed from a magazine by counting magazine letters with a dictionary, decrementing counts for each ransom note character, and returning true or false.
Learn how to determine isomorphic strings in python by mapping s to t with two dictionaries, preserving order, and returning false on mapping conflicts.
Map each letter in the pattern to a unique word and vice versa using two dictionaries in Python, validating word pattern isomorphism with length checks and linear time complexity.
Determine whether two strings form a valid anagram by counting characters in a dictionary and subtracting counts as you scan the second string, yielding true when all counts cancel.
Group the strings into anagrams by sorting each string to form a key, then gather groups in a dictionary and return the results.
Explore solving the two sum problem with a dictionary to achieve O(n) time, returning indices of the two numbers that add up to the target in Python.
learn how to determine if a number is happy by repeatedly summing the squares of its digits, using a dictionary to detect cycles and return true or false.
Use a dictionary to map values to their latest indices in a Python solution, returning true when a duplicate appears within k in an integer array.
Implement an O(n) solution to the longest consecutive sequence problem by using a set to find starting points and extend each sequence in Python.
Summarizes ranges in a sorted unique integer array by grouping consecutive numbers into inclusive start-end ranges, ensuring every element is covered exactly once, with a linear time solution in Python.
Merge intervals teaches how to sort intervals by start values and merge overlapping ones into non-overlapping ranges using Python, illustrating with examples and analyzing an O(n) solution.
insert a new interval into a sorted list of non-overlapping intervals and merge overlaps to form the final merged result using a Python approach.
Learn how to determine the minimum number of arrows to burst balloons by treating balloons as intervals on the x-axis, merging overlapping ranges, and solving with a sorted Python approach.
Implement a Python solution to validate a string of brackets using a stack and a dictionary mapping closing to opening brackets.
Explore simplifying an absolute path to a canonical path in Python by using a stack to handle current and parent directory markers and multiple slashes.
Design a min stack in Python that supports push, pop, top, and get minimum in constant time using a list as the stack; includes an example.
Learn to evaluate reverse Polish notation using a stack to process tokens and apply plus, minus, multiply, and division. Ensure division truncates toward zero and results fit in 32-bit integers.
Learn to build a basic calculator in Python that evaluates expressions with digits, plus and minus signs, and parentheses using a stack, number, result, and sign handling, achieving O(n) time.
Detect whether a linked list has a cycle using Floyd’s tortoise and hare with slow and fast pointers; if they meet, there is a cycle, otherwise null means no cycle.
Add two numbers represented by reversed linked lists and manage carries to produce the sum as a linked list, with a Python solution using a dummy head.
Merge two sorted linked lists by comparing node values and splicing nodes with a dummy head and current pointer in a Python solution, and handle nodes after one list ends.
Copy a linked list with a random pointer using a two-pass approach and a dictionary to map nodes, then set next and random pointers in Python.
Reverse a sublist of a singly linked list from left to right using a dummy node and a stack, then rebuild the list in reverse for O(n) time.
Learn how to reverse a linked list in k-sized groups using a stack, with a Python solution that uses a dummy head and only reverses complete k blocks.
remove the nth node from the end of a linked list using traversal with a dummy node and a stack, then rebuild the list in Python with O(N) time.
Remove duplicates from a sorted linked list, leaving only distinct values and returning a new sorted list, using a dictionary to count frequencies and a dummy head.
Learn to rotate a linked list to the right by k places using an array approach, with mod optimization, and implement the Python solution to rebuild the rotated list.
Partition a linked list around x, placing nodes less than x before those greater or equal while preserving relative order, with two lists lower and higher and a Python implementation.
Explore the design of an lru cache using a hash map and a stack to support get and put operations, evicting the least recently used item when capacity is exceeded.
Solve maximum depth of a binary tree using recursion, applying one plus the max of left and right subtrees, with an optional iterative stack approach in Python.
Determine if two binary trees p and q are the same by verifying structure and node values, using a recursive dfs in Python.
Invert a binary tree by swapping left and right children using a depth-first, recursive approach in Python. It covers handling root, traversal, and linear time complexity.
Explore how to determine a symmetric binary tree by comparing mirror images of left and right subtrees using a Python recursive function with O(N) time.
Construct a binary tree from preorder and inorder traversals by selecting the root from preorder and recursively building left and right subtrees via inorder splits in Python.
Construct a binary tree from inorder and postorder traversals by popping the last postorder value as root, finding its inorder index, and building right before left subtrees with O(n^2) time.
Learn how to populate next pointers in each node II using breadth-first search. The Python deque-based solution connects nodes at each level with a previous pointer and runs in time.
Flatten a binary tree to a linked list in place using preorder traversal, adjusting left and right pointers so the right chain follows the preorder order.
Solve the path sum problem by pre-order dfs from root to leaf, accumulating node values to match a target sum. The lecture presents a Python solution.
Why Purchase "Data Structures and Algorithms: Using Python"?
Master Key Data Structures and Algorithms: Learners will gain a deep understanding of essential data structures (like arrays, linked lists, stacks, queues, trees, graphs) and algorithms (such as sorting, searching, dynamic programming, and backtracking) through hands-on practice with Leetcode’s top 150 questions.
Enhance Problem-Solving Skills: By tackling a variety of coding challenges, learners will develop strong problem-solving skills, learning how to approach and break down complex problems into manageable parts, and implement efficient solutions in Python.
Prepare for Technical Interviews: Learners will be well-prepared for technical interviews at top tech companies. They will become familiar with common interview questions and scenarios, and learn how to articulate their thought process and solutions effectively during interviews.
Improve Code Efficiency and Optimization: Learners will learn to write clean, efficient, and optimized code. They will understand the importance of time and space complexity, and how to improve the performance of their solutions by analyzing and optimizing their code.
Develop Debugging and Testing Skills: Gain proficiency in debugging and testing your code, learning to identify and fix errors, and ensure your solutions are robust and reliable.
Build Confidence in Coding Competitions: Increase your confidence in participating in coding competitions and hackathons by practicing with real-world problems and learning strategies to approach competitive programming.
Foster a Growth Mindset: Cultivate a growth mindset by embracing challenges, learning from mistakes, and continuously improving your coding skills through persistent practice and feedback.
These objectives will help learners build a solid foundation in coding and algorithmic thinking, making them more confident and competent in their technical skills