Dynamic Programming, Backtracking, Data Structures, BigO,Question Patterns,In depth Explanations. Get the job you want !
What you will learn
Dynamic Programming, Backtracking Techniques
Common Data Structures such as Arrays, Hash Table,Linked List,Binary trees,Graphs etc.
Time and Space Complexity of Algorithms, Detailed Discussion of Logic to solve questions
Real Coding Interview Questions from Google, Meta,Amazon,Netflix ,Microsoft etc.
Boost your Problem solving skills
Description
About the Course:
Welcome to the Algorithms and Data Structures Coding Interview Bootcamp with Python!
The primary goal of this course is to prepare you for coding interviews at top tech companies. By tackling one problem at a time and understanding its solution, you’ll accumulate a variety of tools and techniques for conquering any coding interview.
Daily Coding Challenges:
The course is structured around daily coding challenges. Consistent practice will equip you with the skills required to ace coding interviews. For the next 40 days commit to yourself to practice atleast 2 coding interview questions everyday. You don’t need any setup for this as the daily coding problem challenges can be solved in the coding environment provided by Udemy. The course will automatically track your progress and you just need to spend your time making actual progress everyday.
Topics Covered:
We start from the basics with Big O analysis, then move on to very important algorithmic techniques such as Recursion, Backtracking and Dynamic Programming Patters. After this we move to cover common data structures, and discuss real problems asked in interviews at tech giants such as Google, Meta, Amazon, Netflix, Apple, and Microsoft.
For each question, we will:
- Discuss the optimal approach
- Explain time and space complexity
- Code the solution in Python (you can follow along in your preferred language)
Additional Resources:
The course includes downloadable resources, motivational trackers, and cheat sheets.
Course Outline:
- Day 1: Arrays, Big O, Sorted Squared Array, Monotonic Array
- Day 2:Recursion,k-th symbol in Grammar,Josephus problem
- Day 3:Recursion, Tower of Hanoi, Power Sum
- Day 4:Backtracking, Permutations, Permutations 2
- Day 5:Backtracking, Subsets, Subsets 2
- Day 6:Backtracking, Combinations, Combinations Sum 1
- Day 7:Backtracking,Combinations Sum 2,Combinations Sum 3
- Day 8:Backtracking,Sudoku Solver, N Queens
- Day 9:Dynamic Programming, Fibonacci, Climbing Stairs
- Day 10:Dynamic Programming, Min Cost Climbing Stairs, Tribonacci
- Day 11:Dynamic Programming, 01 Knapsack, Unbounded Knapsack
- Day 12:Dynamic Programming, Target Sum, Partition Equal Subset Sum
- Day 13:Dynamic Programming, LCS, Edit Distance
- Day 14:Dynamic Programming, LIS, Max Length of Pair Chain, Russian Doll Envelopes
- Day 15:Dynamic Programming, Palindromic Substrings, Longest Palindromic Substring, Longest Palindromic Subsequence
- Day 16:Dynamic Programming, Palindrome Partitioning, Palindrome Partitioning 2
- Day 17:Dynamic Programming, Word Break, Matrix Chain Multiplication
- Day 18:Dynamic Programming, Kadane’s algorithm – Max Subarray, Maximum Product Subarray
- Day 19: Arrays, Rotate Array, Container with Most Water
- Day 20: Hash Tables, Two Sum, Isomorphic Strings
- Day 21: Strings, Non-Repeating Character, Palindrome
- Day 22: Strings, Longest Unique Substring, Group Anagrams
- Day 23: Searching, Binary Search, Search in Rotated Sorted Array
- Day 24: Searching, Find First and Last Position, Search in 2D Array
- Day 25: Sorting, Bubble Sort, Insertion Sort
- Day 26: Sorting, Selection Sort, Merge Sort
- Day 27: Sorting, Quick Sort, Radix Sort
- Day 28: Singly Linked Lists, Construct SLL, Delete Duplicates
- Day 29: Singly Linked Lists, Reverse SLL, Cycle Detection
- Day 30: Singly Linked Lists, Find Duplicate, Add 2 Numbers
- Day 31: Doubly Linked Lists, DLL Remove Insert, DLL Remove All
- Day 32: Stacks, Construct Stack, Reverse Polish Notation
- Day 33: Queues, Construct Queue, Implement Queue with Stack
- Day 34: Binary Trees, Construct BST, Traversal Techniques
- Day 35: Binary Trees, Level Order Traversal, Left/Right View
- Day 36: Binary Trees, Invert Tree, Diameter of Tree
- Day 37: Binary Trees, Convert Sorted Array to BST, Validate BST
- Day 38: Heaps, Max Heap, Min Priority Queue
- Day 39: Graphs, BFS, DFS
- Day 40: Graphs, Number of Connected Components, Topological Sort
My confidence in your satisfaction with this course is so high that we offer a complete money-back guarantee for 30 days! Thus, it’s a totally risk-free opportunity. Register today, facing ZERO risk and standing to gain EVERYTHING.
So what are you waiting for? Join the best Python Data Structures & Algorithms Bootcamp on Udemy.
I’m eager to see you in the course.
Let’s kick things off! 🙂
Jackson
English
language
Content
Day 1: Arrays Data Structures and Algorithms
What you’re going to get from this course
Welcome! How to make best use of this course (Please Watch)
Day 1 Goals
Introduction to Data Structures
Introduction to Big O, Time Complexity
2 Asymptotic Analysis and Big O
Big O Space Complexity
Big O Logarithm
Arrays: Data Structures Crash Course
Quiz: Arrays
CODING EXERCISES
CODING INTERVIEW Q1 (Easy): Sorted Squared Array
Coding Exercise: Sorted Squared Array
Method 1, Big O Analysis
Python Code – Method 1
Method 2
Python Code – Method 2
CODING INTERVIEW Q2 (Easy): Monotonic Array
Coding Exercise: Monotonic Array
Method and Big O analysis
Python Code – Monotonic Array
Day 2: Recursion
Day 2 Goals
Recursion Basics
Recursive Leap of Faith
Visualising Recursion
Recursion vs Iteration
Ways to write Base condition
Recurrence relation
How to Solve Recursion Questions
Recursion Approaches – 0 to N and N to 0
Recursion is everywhere
Complexity Analysis of Recursive Solutions
Quiz: Recursion
CODING INTERVIEW QUESTION (Medium): k-th symbol in Grammar
Coding Exercise (k-th symbol in Grammar)
Approach(k-th symbol in Grammar)
Pseudocode (k-th symbol in Grammar)
Python Code
Complexity Analysis(k-th symbol in Grammar)
Python Solution (k-th symbol in Grammar)
CODING INTERVIEW QUESTION (Medium): Josephus problem
Coding Exercise: Josephus problem
Approach 1
Pseudocode
Complexity Analysis
Python Solution 1: Josephus problem Method 1
Approach 2
Pseudocode
Complexity Analysis
Python Solution 2 : Josephus problem Method 2
Approach 3
Complexity Analysis
Python Solution 3 : Josephus problem Method 3
Day 3: Recursion Continued
Day 3 Goals
CODING INTERVIEW QUESTION (Medium): Tower of Hanoi
Coding Exercise(Tower of Hanoi)
Identifying that wew can use Recursion
Approach
Recursion Tree
Python Solution : Tower of Hanoi
Complexity Analysis : Tower of Hanoi
CODING INTERVIEW QUESTION(Medium): Power Sum
Coding Exercise: Power Sum
Method and Big O Analysis
Python Solution: Power Sum
Day 4: Backtracking
Day 4 Goals
What is Backtracking
How is it different from Recursion ?
How does Backtracking work ?
Pass by reference / change inplace
Blueprint to solve questions using Backtracking
Identify when to use Backtracking
Quiz: Backtracking
CODING INTERVIEW QUESTION (Medium): Permutations
Coding Exercise ( Permutations)
Approach
Pseudocode
Python Solution : Permutations
Complexity Analysis
CODING INTERVIEW QUESTION(Medium): Permutations 2
Permutations 2
Approach
Pseudocode
Python Code: Permutations 2
Complexity Analysis : Permutations 2
Day 5: Backtracking
Day 5 Goals
CODING INTERVIEW QUESTION(Medium): Subsets
Subsets
Method
Subsets – Comparison with Backtracking Blueprint
Subsets – Complexity Analysis
Python Code – Subsets
CODING INTERVIEW QUESTION(Medium): Subsets 2
Coding Exercise: Subsets 2
Approach
Python Code: Subsets 2
Subsets 2: Complexity Analysis
Day 6: Backtracking
Day 6 Goals
CODING INTERVIEW QUESTION(Medium): Combinations
Coding Exercise: Combinations
Approach
Combinations : Complexity Analysis
Python Code : Combinations
Combinations: Optimisation
Python Code: Combinations with Optimisation
CODING INTERVIEW QUESTION ( Medium) : Combinations Sum 1
Combinations Sum 1
Day 7: Backtracking
Day 7 Goals
CODING INTERVIEW QUESTION (Medium): Combinations Sum 2
Coding Exercise: Combinations Sum 2
CODING INTERVIEW QUESTION ( Medium) : Combinations Sum 3
Coding Exercise: Combinations Sum 3
Day 8: Backtracking
Day 8 Goals
CODING INTERVIEW QUESTION(Hard) : Sudoku Solver
Sudoku Solver
Approach
Pseudocode
isValid check for Sudoku Solver
Python Code : Sudoku Solver
Complexity Analysis
Another approach – Sudoku Solver ( Python Code)
CODING INTERVIEW QUESTION(Hard): N Queen
Coding Exercise: N Queen
Approach
Pseudocode
Python Code: N Queen
Complexity Analysis
Day 9: Dynamic Programming
Day 9 Goals
Introduction to Dynamic Programming (DP)
Dynamic Programming – Patterns
Approach to solve DP(Dynamic Programming) Questions
Why writing the Recursive solution helps to write the Bottom up approach
Identifying Dynamic Programming Questions
Quiz: Dynamic Programming
CODING INTERVIEW QUESTION(Easy): Fibonacci
Coding Exercise: Fibonacci
Approaches
Approach 1: Recursion
Complexity Analysis: Approach 1 – Recursion
Python Code – Recursion
Approach 2: Memoization
Complexity Analysis : Approach 2 – Memoization
Python Code: Approach 2 – Memoization
Approach 3: Tabulation
Complexity Analysis: Approach 3 – Tabulation
Python Code: Approach 3 – Tabulation
Approach 4: Space Optimised Tabulation + Complexity Analysis
Python Code: Approach 4 -Space Optimised Tabulation + Complexity Analysis
CODING INTERVIEW QUESTION(Easy): Climbing Stairs
Coding Exercise: Climbing Stairs
Day 10: Dynamic Programming Type – Fibonacci
Day 10 Goals
CODING INTERVIEW QUESTION(Easy): Min Cost Climbing Stairs
Coding Exercise: Minimum Cost Climbing Stairs
CODING INTERVIEW QUESTION(Easy): Tribonacci
Coding Exercise: Tribonacci
Day 11: Dynamic Programming Type – Knapsack
Day 11 Goals
CODING INTERVIEW QUESTION(Medium): 0/1 Knapsack
Coding Exercise: 01 Knapsack
Approach 1: Recursion
Recursive Approach: Pseudocode
Recursive Approach: Complexity Analysis
Python Code : Recursive Approach
Approach 2: Memoization
Memoization: Pseudocode
Python Code: Memoization
Memoization: Complexity Analysis
Approach 3: Tabulation
Python Code: Tabulation
Tabulation: Complexity Analysis
Approach 4: Space Optimised Tabulation Approach
Python Code: Space Optimised Tabulation
Space Optimised Tabulation Approach: Complexity Analysis
CODING INTERVIEW QUESTION(Medium): Unbounded Knapsack
Coding Exercise: Unbounded Knapsack
Day 12: Dynamic Programming Type – Knapsack
Day 12 Goals
CODING INTERVIEW QUESTION(Medium): Target Sum
Coding Exercise: Target Sum
CODING INTERVIEW QUESTION(Medium): Partition Equal Subset Sum
Coding Exercise: Partition Equal Subset Sum
Day 13: Dynamic Programming Type – LCS ( Longest Common Subsequence)
Day 13 Goals
CODING INTERVIEW QUESTION(Medium): LCS
Coding Exercise: LCS ( Longest Common Subsequence)
Approach 1: Recursion
Pseudocode
Recursion Tree and Complexity Analysis
Python Code: LCS
Approach 2: Memoization
Python Code: Memoization – LCS
Approach 3: Tabulation
Tabulation: Complexity Analysis
Python Code : Tabulation – LCS
Approach 4: Space Optimised Tabulation – LCS
Python Code : Space Optimised Tabulation – LCS
CODING INTERVIEW QUESTION(Medium): Edit Distance
Coding Exercise: Edit Distance
Identifying this as an LCS Type Question
Approach 1: Recursion
Pseudocode
Recursion: Complexity Analysis
Python Code: Recursive Approach(Edit Distance)
Approach 2: Memoization
Python Code: Memoization(Edit Distance)
Approach 3: Tabulation
Tabulation: Complexity Analysis
Python Code: Tabulation (Edit Distance)
Approach 4: Space Optimised Tabulation
Python Code: Space Optimised Tabulation ( Edit Distance)
Day 14: Dynamic Programming Type – LIS ( Longest Increasing Subsequence)
Day 14 Goals
CODING INTERVIEW QUESTION(Medium): Longest Increasing Subsequence (LIS)
Coding Exercise: LIS
Approach 1: Recursion – LIS
Recursion Tree
Complexity Analysis – Recursion – LIS
Python Code – Recursion – LIS
Approach 2: Memoization
Complexity Analysis – Memoization
Python Code – Memoization – LIS
Approach 3: Tabulation – using a 2D dp array
Dry run
Complexity Analysis – Tabulation – using a 2D dp array
Python Code – Tabulation using a 2D dp array – LIS
Approach 4: Tabulation – using a 1D dp array
Dry run
Complexity Analysis- Tabulation – using a 1D dp array
Python Code-Tabulation – using a 1D dp array
Approach 5: using Binary Search – LIS
Part 1 – Approach 5: using Binary Search – LIS
Part 2 – Approach 5: using Binary Search – LIS
Binary Search for this question ( refer Binary Search section for more details)
Complexity Analysis – Approach 5: using Binary Search – LIS
Python Code – Approach 5: using Binary Search – LIS
CODING INTERVIEW QUESTION(Medium): Max Length of Pair Chain
Coding Exercise: Max Length of Pair Chain
CODING INTERVIEW QUESTION(Hard): Russian Doll Envelopes
Coding Exercise: Russian Doll Envelopes
Day 15: Dynamic Programming Type – Gap Strategy / Length wise Iteration
Day 15 Goals
Introduction to Gap Strategy or Length wise Iteration
CODING INTERVIEW QUESTION(Medium): Palindromic Substrings
Palindromic Substrings
Intuition for Approach
Indetifying this as a DP question
Approach: Recursion with memoization
pseudocode
Filling the Memoization table
iterate length wise
Recursion with memoization: Complexity analysis
Python Code: Recursion with memoization
Tabulation approach
Tabulation approach: Complexity Analysis
Python Code: Tabulation
CODING INTERVIEW QUESTION(Medium): Longest Palindromic Substring
Coding Exercise: Longest Palindromic Substring
CODING INTERVIEW QUESTION(Medium): Longest Palindromic Subsequence
Coding Exercise: Longest Palindromic Subsequence
Day 16: Dynamic Programming Type – Partition Method
Day 16 Goals
Introduction to the Partition method
CODING INTERVIEW QUESTION(Medium): Palindrome Partitioning
Coding Exercise: Palindrome Partitioning
Approach
Pseudocode
Side note: Computing n C r
Complexity Analysis
Python Code: Palindrome Partitioning
CODING INTERVIEW QUESTION(Hard):Palindrome Partitioning 2 ( Minimum Cuts) – Hard
Coding Exercise: Palindrom Partitioning 2 ( Min Cuts)
Approach 1: Recursion
Python Code: Recursion – Palindrome Partitioning 2
Approach 2: Memoization
Python Code: Memoization – Palindrome Partitioning 2
Tabulation – Approach A : Palindrome Partitioning 2
Dry Run
Pseudocode
Python Code : Tabulation – Approach A : Palindrome Partitioning 2
Complexity Analysis
Tabulation – Approach B : Palindrome Partitioning 2
Dry run
Pseudocode
Python Code: Tabulation – Approach A : Palindrome Partitioning 2
Complexity Analysis
Day 17: Dynamic Programming Type – Partition Method
Day 17 Goals
CODING INTERVIEW QUESTION(Medium): Word Break
Coding Exercise: Word Break
CODING INTERVIEW QUESTION(Hard): Matrix Chain Multiplication
Coding Exercise: Matrix Chain Multiplication
Day 18: Dynamic Programming Type – Kadane’s Algorithm
Day 18 Goals
CODING INTERVIEW QUESTION (Medium): Max Subarray
Coding Exercise: Max Subarray
CODING INTERVIEW QUESTION (Medium): Maximum Product Subarray
Coding Exercise: Maximum Product Subarray
Day 19: Arrays Data Structures and Algorithms
Day 19 Goals
Coding Interview Q1(Medium): Rotate Array
Coding Exercise: Rotate Array
Method and Big O analysis
PYTHON Code Solution
Python Code Method 2
Coding Interview Q2(Medium): Container with most water
Coding Exercise: Container with most water
Method 1 and Big O analysis
PYTHON Code Method 1
Method 2 and Big O analysis
PYTHON Code Method 2
Day 20: Dictionaries / Hash Tables Data Structures and Algorithms
Day 20 Goals
Hash Table: Data Structures Crash Course
Coding Interview Q1(Easy): Two Sum
Coding Exercise: Two Sum
Method 1, Big O analysis
PYTHON Code
Method 2, Big O analysis
PYTHON Code
Coding Interview Q2(Easy): Isomorphic Strings
Coding Exercise: Isomorphic Strings
Method and Big O analysis
PYTHON Code
Day 21 : Strings Data Structures and Algorithms
Day 21 Goals
Data Structures Crash Course: Strings
Coding Interview Q1(Easy): First Non Repeating Character
Coding Exercise: First Non Repeating Character
Method 1 and Big O analysis
PYTHON code
Method 2 and Big O analysis
PYTHON code
Coding Interview Q2(Easy): Is Palindrome ?
Coding Exercise: Is Palindrome ?
Method 1 and Big O analysis
PYTHON code
Method 2 and Big O analysis
PYTHON code
Method 3 and Big O analysis
PYTHON code
Day 22: Strings Data Structures and Algorithms
Day 22 Goals
Coding Interview Q1(Medium): Longest Sub string with Unique characters
Coding Exercise: Longest Sub string with Unique characters
Method and Big O analysis
PYTHON code
Coding Interview Q2(Medium): Group Anagrams
Coding Exercise: Group Anagrams
method and Big O analysis
PYTHON code
Day 23: Searching Algorithms
Day 23 Goals
Coding Interview Q1 (Easy): Binary Search Algorithm
Coding Exercise: Binary Search Algorithm
Method and Big O analysis
PYTHON Code Iterative
PYTHON Code Recursive
Coding Interview Q2(Medium): Search in rotated sorted array
Coding Exercise: Search in rotated sorted array
Method and Big O analysis
PYTHON Code
Day 24: Searching Algorithms
Day 24 Goals
Coding Interview Q1(Medium): Search for range
Coding Exercise: Search for range
Method and Big O analysis
PYTHON Code – Recursive
PYTHON Code – Iterative
Coding Interview Q2(Medium): Search in Matrix
Coding Exercise: Search in Matrix
method and Big O analysis
PYTHON code
Day 25: Sorting Algorithms
Day 25 Goals
Coding Interview Q1: Bubble Sort Algorithm
Coding Exercise: Bubble Sort Algorithm
Method and Big O analysis
Python Code
Coding Interview Q2: Insertion Sort Algorithm, Big O analysis
Coding Exercise : Insertion Sort Algorithm
Python code
Insertion sort is a stable sorting Algorithm
Day 26: Sorting Algorithms
Day 26 Goals
Coding Interview Q1: Selection Sort Algorithm, Big O analysis
Coding Exercise: Selection Sort Algorithm
Python Code
Coding Interview Q2: Merge Sort Algorithm
Coding Exercise: Merge Sort
Method and Big O analysis
Python Code
Day 27: Sorting Algorithms
Day 27 Goals
Coding Interview Q1: Quick Sort Algorithm
Coding Exercise: Quick Sort
Optimise Time Complexity
Optimise Space Complexity
Python Code
Coding Interview Q2: Radix Sort Algorithm, Big O analysis
Coding Exercise: Radix Sort Algorithm
Python Code
Day 28 Singly Linked List Data Structures and Algorithms
Day 28 Goals
Data Structures Crash Course: Linked Lists
Coding Interview Q1(Medium): Design a Singly Linked List
Coding Exercise: Design a Singly Linked List
Method and Big O analysis
Python Code
Coding Interview Q2: Remove Duplicates
Coding Exercise: Remove Duplicates
Method and Big O analysis
Python Code
Day 29 Singly Linked List Data Structures and Algorithms
Day 29 Goals
Coding Interview Q1(Easy): Reverse
Coding Exercise: Reverse SLL
Method and Big O analysis
Python Code
Coding Interview Q2(Medium) : Cycle Detection
Coding Exercise: Cycle Detection
Method and Big O analysis
Python Code
proof
Day 30 : Singly Linked List Data Structures and Algorithms
Day 30 Goals
Coding Interview Q1(Medium): Find duplicate number
Coding Exercise: Find duplicate number
method and Big O analysis
Python code
Coding Interview Q2(Medium): Add 2 numbers
Coding Exercise: Add 2 numbers
method and Big O analysis
Python code
Day 31 Doubly Linked List Data Structures and Algorithms
Day 31 Goals
Coding Interview Q1: Remove Node, Insert Node
Coding Exercise: Remove Node
Method remove
Python code: Remove
Insert Intro
Method Insert
Coding Exercise: Insert Node
Python code: Insert
Coding Interview Q2: Remove Value, Insert at Position in Doubly Linked List
Coding Exercise :Remove Value in Doubly Linked List
Remove Val Method
Python Code
Insert at Position
method
Coding Exercise: Insert at Position in DLL
Python Code
Day 32: Stacks Data Structures and Algorithms
Day 32 Goals
Data Structures Crash Course: Stacks and Queues
Coding Interview Q1: Design a Stack
Coding Exercise: Design a Stack (with Linked List)
Python Code
Coding Interview Q2(Medium): Reverse Polish Notation
Coding Exercise: Reverse Polish Notation
method and Big O analysis
Python Code
Day 33: Queue Data Structures and Algorithms
Day 33 Goals
Coding Interview Q1: Design a Queue
Coding Exercise: Design a Queue using a Linked List
Python Code
Coding Interview Q2(Easy) : Queue with Stack
Coding Exercise : Queue with Stack
method and Big O analysis
Python Code
Day 34: Binary Tree / Binary Search Tree Data Structures and Algorithms
Day 34 Goals
Data Structures Crash Course: Trees Introduction
Theory: Binary Trees 1
Proof : height of Balanced Binary tree is floor(log N)
Theory: Binary Tree Terminaologies
What is a BST – Binary Search Tree
Coding Interview Q1: Construct Binary Search Tree,Big O analysis
Coding Exercise: Construct Binary Search Tree
Python Code
Coding Interview Q2 : Traverse – BFS and DFS,Big O analysis
Coding Exercise : Traverse – BFS and DFS
Python Code
Day 35: Binary Tree / Binary Search Tree Data Structures and Algorithms
Day 35 Goals
Coding Interview Q1(Medium): Level Order traversal
Coding Exercise: Instance method, Level Order traversal
Insert method
Python code
Level Order Traversal Method and Big O analysis
Python code – Level order traversal
Coding Interview Q2(Medium): Left / Right view
Coding Exercise: Left / Right view
Method and Big O analysis
Python code
Day 36: Binary Tree Data Structures and Algorithms
Day 36 Goals
Coding Interview Q1 (Easy): Invert Binary Tree
Iterative method and Big O analysis
Python Code: Iterative
Recursive method and Big O analysis
Python Code: Recursive
Coding Interview Q2 (Easy): Diameter of Binary Tree
Method and Big O analysis
Python Code
Day 37: Binary Search Trees Data Structures and Algorithms
Day 37 Goals
Coding Interview Q1(Easy): sorted array to BST
method and Big O analysis
Python code
Coding Interview Q2(Medium) : Valid BST
Method and Big O analysis
Python Code
Day 38: Heaps and Priority Queue Data Structures and Algorithms
Day 38 Goals
Binary Heap: Data Structure Crash Course
Coding Interview Q1: Construct Max Binary Heap, Big O analysis
Proof of Build Binary Heap Time Complexity
Python Code
Introduction to Priority Queue
Coding Interview Q2: Construct Priority Queue,Big O analysis
Python Code
Day 39: Graphs Data Structures and Algorithms
Day 39 Goals
Coding Interview Q1: BFS, Adjacency List,Big O analysis
Python Code
BFS, Adjacency Matrix
Python Code
Coding Interview Q2: DFS, Recursive, Big O analysis
Python Code
DFS Iterative
Python Code
Day 40: Graphs Data Structures and Algorithms
Day 40 Goals
Coding Interview Q1: Number of Components, Big O analysis
Python Code
Coding Interview Q2(Medium): Course Scheduler
Brute Force Method and Big O analysis
Python Code – Brute Force Method
Topological Sort based method and Big O analysis
Python Code