100+ DSA Interview Questions for Cracking FAANG with Animated Examples for Deeper Understanding and Faster Learning

What you will learn

### Learn, implement, and use different Data Structures

### Learn, implement and use different Algorithms

### Become a better developer by mastering computer science fundamentals

### Learn everything you need to ace difficult coding interviews

### Cracking the Coding Interview with 100+ questions with explanations

### Time and Space Complexity of Data Structures and Algorithms

### Recursion

### Big O

Description

Welcome to the Complete Data Structures and Algorithms in Python Bootcamp, the most modern, and the most complete Data Structures and Algorithms in Python course on the internet.

At 40+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Python. You will see **100+ Interview Questions** done at the top technology companies such as Apple,Amazon, Google and Microsoft and how to face Interviews with comprehensive visual explanatory video materials which will bring you closer towards landing the tech job of your dreams!

Learning Python is one of the fastest ways to improve your career prospects as it is one of the most in demand tech skills! This course will help you in better understanding every detail of Data Structures and how algorithms are implemented in high level programming language.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer.

**After finishing this course, you will be able to:**

Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting and dynamic programming to solve programming challenges.

Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications

Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets

Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

**Why this course is so special and different from any other resource available online?**

This course will take you from very beginning to a very complex and advanced topics in understanding Data Structures and Algorithms!

You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course.

You will also see Interview Questions done at the top technology companies such as Apple,Amazon, Google and Microsoft.

I cover everything you need to know about technical interview process!

So whether you are interested in learning the **top programming language** in the world in-depth

**And **interested in learning the fundamental **Algorithms, Data Structures and performance analysis** that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next **technical interview** this is the course for you!

**And this is what you get by signing up today:**

Lifetime access to 40+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want

Friendly and fast support in the course Q&A whenever you have questions or get stuck

FULL money back guarantee for 30 days!

**Who is this course for?**

Self-taught programmers who have a basic knowledge in Python and want to be professional in Data Structures and Algorithms and begin interviewing in tech positions!

As well as students currently studying computer science and want supplementary material on Data Structures and Algorithms and interview preparation for after graduation!

As well as professional programmers who need practice for upcoming coding interviews.

And finally anybody interested in learning more about data structures and algorithms or the technical interview process!

This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms , increase your earning potential or just want a job with more freedom, this is the right course for you!

The topics that are covered in this course.

**Section 1 – Introduction**

- What are Data Structures?
- What is an algorithm?
- Why are Data Structures and Algorithms important?
- Types of Data Structures
- Types of Algorithms

**Section 2 – Recursion**

- What is Recursion?
- Why do we need recursion?
- How Recursion works?
- Recursive vs Iterative Solutions
- When to use/avoid Recursion?
- How to write Recursion in 3 steps?
- How to find Fibonacci numbers using Recursion?

**Section 3 – Cracking Recursion Interview Questions**

- Question 1 – Sum of Digits
- Question 2 – Power
- Question 3 – Greatest Common Divisor
- Question 4 – Decimal To Binary

**Section 4 – Bonus CHALLENGING Recursion Problems (Exercises)**

- power
- factorial
- productofArray
- recursiveRange
- fib
- reverse
- isPalindrome
- someRecursive
- flatten
- captalizeFirst
- nestedEvenSum
- capitalizeWords
- stringifyNumbers
- collectStrings

**Section 5 – Big O Notation**

- Analogy and Time Complexity
- Big O, Big Theta and Big Omega
- Time complexity examples
- Space Complexity
- Drop the Constants and the non dominant terms
- Add vs Multiply
- How to measure the codes using Big O?
- How to find time complexity for Recursive calls?
- How to measure Recursive Algorithms that make multiple calls?

**Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple and Microsoft)**

- Product and Sum
- Print Pairs
- Print Unordered Pairs
- Print Unordered Pairs 2 Arrays
- Print Unordered Pairs 2 Arrays 100000 Units
- Reverse
- O(N)Â Equivalents
- Factorial Complexity
- Fibonacci Complexity
- Powers of 2

**Section 7 – Arrays**

- What is an Array?
- Types of Array
- Arrays in Memory
- Create an Array
- Insertion Operation
- Traversal Operation
- Accessing an element of Array
- Searching for an element in Array
- Deleting an element from Array
- Time and Space complexity of One Dimensional Array
- One Dimensional Array Practice
- Create Two Dimensional Array
- Insertion – Two Dimensional Array
- Accessing an element of Two Dimensional Array
- Traversal – Two Dimensional Array
- Searching for an element in Two Dimensional Array
- Deletion – Two Dimensional Array
- Time and Space complexity of Two Dimensional Array
- When to use/avoid array

**Section 8 – Python Lists**

- What is a List? How to create it?
- Accessing/Traversing a list
- Update/Insert a List
- Slice/ from a List
- Searching for an element in a List
- List Operations/Functions
- Lists and strings
- Common List pitfalls and ways to avoid them
- Lists vs Arrays
- Time and Space Complexity of List
- List Interview Questions

**Section 9 – Cracking Array/List Interview Questions (Amazon, Facebook, Apple and Microsoft)**

- Question 1 – Missing Number
- Question 2 – Pairs
- Question 3 – Finding a number in an Array
- Question 4 – Max product of two int
- Question 5 – Is Unique
- Question 6 – Permutation
- Question 7 – Rotate Matrix

**Section 10 – CHALLENGING Array/List Problems (Exercises)**

- Middle Function
- 2D Lists
- Best Score
- Missing Number
- Duplicate Number
- Pairs

**Section 11 – Dictionaries**

- What is a Dictionary?
- Create a Dictionary
- Dictionaries in memory
- Insert /Update an element in a Dictionary
- Traverse through a Dictionary
- Search for an element in a Dictionary
- Delete / Remove an element from a Dictionary
- Dictionary Methods
- Dictionary operations/ built in functions
- Dictionary vs List
- Time and Space Complexity of a Dictionary
- Dictionary Interview Questions

**Section 12 – Tuples**

- What is a Tuple? How to create it?
- Tuples in Memory / Accessing an element of Tuple
- Traversing a Tuple
- Search for an element in Tuple
- Tuple Operations/Functions
- Tuple vs List
- Time and Space complexity of Tuples
- Tuple Questions

**Section 13 – Linked List**

- What is a Linked List?
- Linked List vs Arrays
- Types of Linked List
- Linked List in the Memory
- Creation of Singly Linked List
- Insertion in Singly Linked List in Memory
- Insertion in Singly Linked List Algorithm
- Insertion Method in Singly Linked List
- Traversal of Singly Linked List
- Search for a value in Single Linked List
- Deletion of node from Singly Linked List
- Deletion Method in Singly Linked List
- Deletion of entire Singly Linked List
- Time and Space Complexity of Singly Linked List

**Section 14 – Circular Singly Linked List**

- Creation of Circular Singly Linked List
- Insertion in Circular Singly Linked List
- Insertion Algorithm in Circular Singly Linked List
- Insertion method in Circular Singly Linked List
- Traversal of Circular Singly Linked List
- Searching a node in Circular Singly Linked List
- Deletion of a node from Circular Singly Linked List
- Deletion Algorithm in Circular Singly Linked List
- Method in Circular Singly Linked List
- Deletion of entire Circular Singly Linked List
- Time and Space Complexity of Circular Singly Linked List

**Section 15 – Doubly Linked List**

- Creation of Doubly Linked List
- Insertion in Doubly Linked List
- Insertion Algorithm in Doubly Linked List
- Insertion Method in Doubly Linked List
- Traversal of Doubly Linked List
- Reverse Traversal of Doubly Linked List
- Searching for a node in Doubly Linked List
- Deletion of a node in Doubly Linked List
- Deletion Algorithm in Doubly Linked List
- Deletion Method in Doubly Linked List
- Deletion of entire Doubly Linked List
- Time and Space Complexity of Doubly Linked List

**Section 16 – Circular Doubly Linked List**

- Creation of Circular Doubly Linked List
- Insertion in Circular Doubly Linked List
- Insertion Algorithm in Circular Doubly Linked List
- Insertion Method in Circular Doubly Linked List
- Traversal of Circular Doubly Linked List
- Reverse Traversal of Circular Doubly Linked List
- Search for a node in Circular Doubly Linked List
- Delete a node from Circular Doubly Linked List
- Deletion Algorithm in Circular Doubly Linked List
- Deletion Method in Circular Doubly Linked List
- Entire Circular Doubly Linked List
- Time and Space Complexity of Circular Doubly Linked List
- Time Complexity of Linked List vs Arrays

**Section 17 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple and Microsoft)**

- Linked List Class
- Question 1 – Remove Dups
- Question 2 – Return Kth to Last
- Question 3 – Partition
- Question 4 – Sum Linked Lists
- Question 5 – Intersection

**Section 18 – Stack**

- What is a Stack?
- Stack Operations
- Create Stack using List without size limit
- Operations on Stack using List (push, pop, peek, isEmpty, )
- Create Stack with limit (pop, push, peek, isFull, isEmpty, )
- Create Stack using Linked List
- Operation on Stack using Linked List (pop, push, peek, isEmpty, )
- Time and Space Complexity of Stack using Linked List
- When to use/avoid Stack
- Stack Quiz

**Section 19 – Queue**

- What is Queue?
- Queue using Python List – no size limit
- Queue using Python List – no size limit , operations (enqueue, dequeue, peek)
- Circular Queue – Python List
- Circular Queue – Python List, Operations (enqueue, dequeue, peek, )
- Queue – Linked List
- Queue – Linked List, Operations (Create, Enqueue)
- Queue – Linked List, Operations (Dequeue(), isEmpty, Peek)
- Time and Space complexity of Queue using Linked List
- List vs Linked List Implementation
- Collections Module
- Queue Module
- Multiprocessing module

**Section 20 – Cracking Stack and Queue Interview Questions (Amazon,Facebook, Apple, Microsoft)**

- Question 1 – Three in One
- Question 2 – Stack Minimum
- Question 3 – Stack of Plates
- Question 4 – Queue via Stacks
- Question 5 – Animal Shelter

**Section 21 – Tree / Binary Tree**

- What is a Tree?
- Why Tree?
- Tree Terminology
- How to create a basic tree in Python?
- Binary Tree
- Types of Binary Tree
- Binary Tree Representation
- Create Binary Tree (Linked List)
- PreOrder Traversal Binary Tree (Linked List)
- InOrder Traversal Binary Tree (Linked List)
- PostOrder Traversal Binary Tree (Linked List)
- LevelOrder Traversal Binary Tree (Linked List)
- Searching for a node in Binary Tree (Linked List)
- Inserting a node in Binary Tree (Linked List)
- Delete a node from Binary Tree (Linked List)
- Delete entire Binary Tree (Linked List)
- Create Binary Tree (Python List)
- Insert a value Binary Tree (Python List)
- Search for a node in Binary Tree (Python List)
- PreOrder Traversal Binary Tree (Python List)
- InOrder Traversal Binary Tree (Python List)
- PostOrder Traversal Binary Tree (Python List)
- Level Order Traversal Binary Tree (Python List)
- Delete a node from Binary Tree (Python List)
- Entire Binary Tree (Python List)
- Linked List vs Python List Binary Tree

**Section 22 – Binary Search Tree**

- What is a Binary Search Tree? Why do we need it?
- Create a Binary Search Tree
- Insert a node to BST
- Traverse BST
- Search in BST
- Delete a node from BST
- Delete entire BST
- Time and Space complexity of BST

**Section 23 – AVL Tree**

- What is an AVL Tree?
- Why AVL Tree?
- Common Operations on AVL Trees
- Insert a node in AVL (Left Left Condition)
- Insert a node in AVL (Left Right Condition)
- Insert a node in AVL (Right Right Condition)
- Insert a node in AVL (Right Left Condition)
- Insert a node in AVL (all together)
- Insert a node in AVL (method)
- Delete a node from AVL (LL, LR, RR, RL)
- Delete a node from AVL (all together)
- Delete a node from AVL (method)
- Delete entire AVL
- Time and Space complexity of AVL Tree

**Section 24 – Binary Heap**

- What is Binary Heap? Why do we need it?
- Common operations (Creation, Peek, sizeofheap) on Binary Heap
- Insert a node in Binary Heap
- Extract a node from Binary Heap
- Delete entire Binary Heap
- Time and space complexity of Binary Heap

**Section 25 – Trie**

- What is a Trie? Why do we need it?
- Common Operations on Trie (Creation)
- Insert a string in Trie
- Search for a string in Trie
- Delete a string from Trie
- Practical use of Trie

**Section 26 – Hashing**

- What is Hashing? Why do we need it?
- Hashing Terminology
- Hash Functions
- Types of Collision Resolution Techniques
- Hash Table is Full
- Pros and Cons of Resolution Techniques
- Practical Use of Hashing
- Hashing vs Other Data structures

**Section 27 – Sort Algorithms**

- What is Sorting?
- Types of Sorting
- Sorting Terminologies
- Bubble Sort
- Selection Sort
- Insertion Sort
- Bucket Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Comparison of Sorting Algorithms

**Section 28 – Searching Algorithms**

- Introduction to Searching Algorithms
- Linear Search
- Linear Search in Python
- Binary Search
- Binary Search in Python
- Time Complexity of Binary Search

**Section 29 – Graph Algorithms**

- What is a Graph? Why Graph?
- Graph Terminology
- Types of Graph
- Graph Representation
- Create a graph using Python
- Graph traversal – BFS
- BFS Traversal in Python
- Graph Traversal – DFS
- DFS Traversal in Python
- BFS Traversal vs DFS Traversal
- Topological Sort
- Topological Sort Algorithm
- Topological Sort in Python
- Single Source Shortest Path Problem (SSSPP)
- BFS for Single Source Shortest Path Problem (SSSPP)
- BFS for Single Source Shortest Path Problem (SSSPP) in Python
- Why does BFS not work with weighted Graphs?
- Why does DFS not work for SSSP?
- Dijkstra’s Algorithm for SSSP
- Dijkstra’s Algorithm in Python
- Dijkstra Algorithm with negative cycle
- Bellman Ford Algorithm
- Bellman Ford Algorithm with negative cycle
- Why does Bellman Ford run V-1 times?
- Bellman Ford in Python
- BFS vs Dijkstra vs Bellman Ford
- All pairs shortest path problem
- Dry run for All pair shortest path
- Floyd Warshall Algorithm
- Why Floyd Warshall?
- Floyd Warshall with negative cycle,
- Floyd Warshall in Python,
- BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,
- Minimum Spanning Tree,
- Disjoint Set,
- Disjoint Set in Python,
- Kruskal Algorithm,
- Kruskal Algorithm in Python,
- Prim’s Algorithm,
- Prim’s Algorithm in Python,
- Prim’s vs Kruskal

**Section 30 – Greedy Algorithms**

- What is Greedy Algorithm?
- Well known Greedy Algorithms
- Activity Selection Problem
- Activity Selection Problem in Python
- Coin Change Problem
- Coin Change Problem in Python
- Fractional Knapsack Problem
- Fractional Knapsack Problem in Python

**Section 31 – Divide and Conquer Algorithms**

- What is a Divide and Conquer Algorithm?
- Common Divide and Conquer algorithms
- How to solve Fibonacci series using Divide and Conquer approach?
- Number Factor
- Number Factor in Python
- House Robber
- House Robber Problem in Python
- Convert one string to another
- Convert One String to another in Python
- Zero One Knapsack problem
- Zero One Knapsack problem in Python
- Longest Common Sequence Problem
- Longest Common Subsequence in Python
- Longest Palindromic Subsequence Problem
- Longest Palindromic Subsequence in Python
- Minimum cost to reach the Last cell problem
- Minimum Cost to reach the Last Cell in 2D array using Python
- Number of Ways to reach the Last Cell with given Cost
- Number of Ways to reach the Last Cell with given Cost in Python

**Section 32 – Dynamic Programming**

- What is Dynamic Programming? (Overlapping property)
- Where does the name of DC come from?
- Top Down with Memoization
- Bottom Up with Tabulation
- Top Down vs Bottom Up
- Is Merge Sort Dynamic Programming?
- Number Factor Problem using Dynamic Programming
- Number Factor : Top Down and Bottom Up
- House Robber Problem using Dynamic Programming
- House Robber : Top Down and Bottom Up
- Convert one string to another using Dynamic Programming
- Convert String using Bottom Up
- Zero One Knapsack using Dynamic Programming
- Zero One Knapsack – Top Down
- Zero One Knapsack – Bottom Up

**Section 33 – CHALLENGING Dynamic Programming Problems**

- Longest repeated Subsequence Length problem
- Longest Common Subsequence Length problem
- Longest Common SubsequenceÂ problem
- Diff Utility
- Shortest Common SubsequenceÂ problem
- Length of Longest Palindromic Subsequence
- Subset Sum Problem
- Egg Dropping Puzzle
- Maximum Length Chain of Pairs

**Section 34 – A Recipe for Problem Solving**

- Introduction
- Step 1 – Understand the problem
- Step 2 – Examples
- Step 3 – Break it Down
- Step 4 – Solve or Simplify
- Step 5 – Look Back and Refactor

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