50 Days Of Dsa Python Data Structures Algorithms Leetcode
 
Course Overview:
Welcome to the 50 Days of DSA with Python: Data Structures & Algorithms on LeetCode course! This comprehensive boot camp is designed to prepare you for coding interviews at top tech companies by tackling one problem at a time and understanding its solution. By the end of this course, you'll have various tools and techniques to ace any coding interview.
 
What You’ll Learn:
Dynamic Programming and Backtracking Techniques: Master advanced algorithmic concepts.
Common Data Structures: Deep dive into arrays, hash tables, linked lists, binary trees, graphs, and more.
Time and Space Complexity: Detailed discussions on algorithm efficiency.
Real Coding Interview Questions: Solve problems asked by Google, Meta, Amazon, Netflix, Microsoft, and more.
Problem-solving skills: Sharpen your ability to tackle complex coding challenges.
 
Requirements:
Basic Python Knowledge: Understand basic Python syntax, such as loops and functions.
No Prior DSA Experience Required: This course starts from the basics.
 
About the Course:
The primary goal of this course is to prepare you for coding interviews at top tech companies. You'll develop the skills needed to excel in technical interviews through daily coding challenges. For the next 50 days, commit to practicing at least two coding interview questions daily. You don't need to set up the challenges, as they can be solved directly in the provided coding environment. Your progress will be tracked automatically, allowing you to focus solely on learning and improving.
 
Topics Covered:
Starting from the basics with Big O analysis, the course progresses through essential algorithmic techniques like recursion, backtracking, and dynamic programming patterns. You'll also cover common data structures and solve real problems asked in interviews at major tech companies.
 
Daily Breakdown:
Arrays, Big O Analysis: Sorted squared array, monotonic array.
Recursion: k-th symbol in grammar, Josephus problem.
Recursion: Tower of Hanoi, power sum.
Backtracking: Permutations, permutations 2.
Backtracking: Subsets, subsets 2.
Backtracking: Combinations, combination sum 1.
Backtracking: Combination sum 2, combination sum 3.
Backtracking: Sudoku solver, N-Queens.
Dynamic Programming: Fibonacci, climbing stairs.
Dynamic Programming: Min cost climbing stairs, Tribonacci.
Dynamic Programming: 0/1 knapsack, unbounded knapsack.
Dynamic Programming: Target sum, partition equal subset sum.
Dynamic Programming: Longest common subsequence, edit distance.
Dynamic Programming: Longest increasing subsequence, max length of pair chain, Russian doll envelopes.
Dynamic Programming: Palindromic substrings, longest palindromic substring, longest palindromic subsequence.
Dynamic Programming: Palindrome partitioning, palindrome partitioning 2.
Dynamic Programming: Word break, matrix chain multiplication.
Dynamic Programming: Kadane’s algorithm, max subarray, maximum product subarray.
Arrays: Rotate the array container with the most water.
Hash Tables: Two sums, isomorphic strings.
Strings: Non-repeating character, palindrome.
Strings: Longest unique substring, group anagrams.
Searching: Binary search, search in rotated sorted array.
Searching: Find the first and last positions in a 2D array.
Sorting: Bubble sort, insertion sort.
Sorting: Selection sort, merge sort.
Sorting: Quick sort, radix sort.
Singly Linked Lists: Construct SL and delete duplicates.
Singly Linked Lists: Reverse SLL, cycle detection.
Singly Linked Lists: Find duplicate and add two numbers.
Doubly Linked Lists: DLL remove insert, DLL remove all.
Stacks: Construct stack, reverse Polish notation.
Queues: Construct a queue and implement a queue with a stack.
Binary Trees: Construct BST traversal techniques.
Pre-order and In-order Traversal: Iterative methods.
Post-order Traversal: Iterative, path sum 2.
Construct Binary Tree: From pre-order and in-order traversal, and in and post-order traversal.
Binary Trees: Level order traversal, left/right view.
Level Order Traversal 2: Zigzag traversal.
Vertical Order Traversal: Sum root to leaf numbers.
Binary Trees: Invert tree, diameter of tree.
Binary Trees: Convert sorted array to BST, validate BST.
Lowest Common Ancestor of BST: Unique BST 2.
Lowest Common Ancestor of Binary Tree: Unique BST 1.
Serialize and Deserialize Binary Tree: N-ary tree level order traversal.
Heaps: Max heap, min priority queue.
Graphs: BFS, DFS.
Graphs: Number of connected components, topological sort.
Graphs: Number of provinces; find if the path exists in the graph.
Graphs: Number of islands, numbers with the same consecutive differences.
 
Who This Course Is For:
Aspiring Tech Professionals: Those looking to secure software engineering roles at top tech companies.
Data Science Candidates: Individuals aiming to ace the DSA portion of data science interviews.
Self-Taught Programmers: Those seeking their first job in tech.
Experienced Developers: Professionals wanting to transition into MAANG (top tech firms).
 
By the end of this course, you will have significantly boosted your problem-solving skills and be well-prepared to tackle the most challenging coding interviews. Join us and take the next step towards mastering data structures and algorithms with Python!