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Data Structures for Coding Interviews in Python

Explore proven strategies developed by FAANG engineers to ace coding interviews with a structured approach to data structures in Python. Prep faster with real-world questions.

Beginner

228 Lessons

30h

Certificate of Completion

Explore proven strategies developed by FAANG engineers to ace coding interviews with a structured approach to data structures in Python. Prep faster with real-world questions.

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This course includes

168 Playgrounds

65 Challenges

24 Quizzes

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Course Overview

Course Content

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Course Overview

Data structures are amongst the most fundamental concepts of Computer Science. The data structure chosen can make or break an entire computer program. Consequently, they are also largely categorized as a vital benchmark of computer science knowledge when it comes to industry interviews. This course contains a detailed review of all the common data structures and provides implementation level details in Python to allow readers to become well equipped. Now with more code solutions, lessons, and illustrations ...Show More

Data structures are amongst the most fundamental concepts of Computer Science. The data structure chosen can make or break an en...Show More

Course Content

Introduction to Complexity Measures

22 Lessons

Sharpen your skills in evaluating the time and space complexity of algorithms.

Comparing Algorithms

Example: Measuring Time Complexity of For Loop

Example: Measuring Time Complexity of a Nested For Loop

Introduction to Asymptotic Analysis and Big O

Other Common Asymptotic Notations and Why Big O Trumps Them

Useful Formulas

Common Complexity Scenarios

Challenge: Big O of Nested Loop with Addition

Solution: Big O of Nested Loop with Addition

Challenge: Big O of Nested Loop with Subtraction

Solution: Big O of Nested Loop with Subtraction

Challenge: Big O of Nested Loop with Multiplication

Solution: Big O of Nested Loop with Multiplication

Challenge: Nested Loop with Multiplication (Basic)

Solution: Nested Loop with Multiplication (Basic)

Challenge: Nested Loop with Multiplication (Intermediate)

Solution: Nested Loop with Multiplication (Intermediate)

Challenge: Nested Loop with Multiplication (Advanced)

Solution: Nested Loop with Multiplication (Advanced)

Challenge: Nested Loop with Multiplication (Pro)

Solution: Nested Loop with Multiplication (Pro)

Complexity Quiz: Test your understanding of complexity

Introduction to Lists

26 Lessons

Unpack the core of lists and arrays in Python, and tackle essential list manipulation challenges.

Lists

Arrays

Lists vs Arrays in Python

Challenge: Remove Even Integers From List

Solution: Remove Even Integers From List

Challenge: Merge Two Sorted Lists

Solution: Merge Two Sorted Lists

Challenge: Find Two Numbers That Add Up to K

Solution: Find Two Numbers That Add Up to K

Challenge: Product of Array Except Self

Solution: Product of Array Except Self

Challenge: Find Minimum Value in List

Solution: Find Minimum Value in List

Challenge: First Non-Repeating Integer in a List

Solution: First Non-Repeating Integer in a List

Challenge: Find Second Maximum Value in a List

Solution: Find Second Maximum Value in a List

Challenge: Rotate List

Solution: Rotate List

Challenge: Rearrange Positive & Negative Values

Solution: Rearrange Positive & Negative Values

Challenge: Rearrange Sorted List in Max/Min Form

Solution: Rearrange Sorted List in Max/Min Form

Challenge: Maximum Sublist

Solution: Maximum Sublist

Lists Quiz: Test your understanding of Lists

Introduction to Linked Lists

30 Lessons

Examine the fundamentals, operations, and challenges of singly and doubly linked lists.

Singly Linked Lists (SLL)

Linked Lists vs. Lists

Basic Linked List Operations

Singly Linked List Insertion

Singly Linked List Deletion

Doubly Linked Lists (DLL)

Singly Linked Lists vs. Doubly Linked Lists

Challenge: Insertion at Tail

Solution: Insertion at Tail

Challenge: Search in a Singly Linked List

Solution: Search in a Singly Linked List

Challenge: Deletion by Value

Solution: Deletion by Value

Challenge: Find the Length of a Linked List

Solution: Find the Length of a Linked List

Challenge: Reverse Linked List

Solution: Reverse Linked List

Challenge: Linked List Cycle

Solution: Linked List Cycle

Challenge: Middle of the Linked List

Solution: Middle of the Linked List

Challenge: Remove Duplicates from a Linked List

Solution: Remove Duplicates from a Linked List

Challenge: Union and Intersection of Linked Lists

Solution: Union and Intersection of Linked Lists

Challenge: Return the Nth Node from End

Solution: Return the Nth Node from End

Challenge: Find If a Doubly Linked List Is a Palindrome

Solution: Find If a Doubly Linked List Is a Palindrome

Linked Lists Quiz: Test your understanding of Linked Lists

Introduction to Stacks and Queues

25 Lessons

Grasp the fundamentals of stacks and queues, their implementations, and practical challenges.

What is a Stack?

Stack (Implementation)

What is a Queue?

Queue (Implementation)

Challenge: Generate Binary Numbers From 1 to n Using a Queue

Solution: Generate Binary Numbers From 1 to n Using a Queue

Challenge: Implement Two Stacks Using One List

Solution: Implement Two Stacks Using One List

Challenge: Reverse First k Elements of Queue

Solution: Reverse First k Elements of Queue

Challenge: Implement Queue Using Stacks

Solution: Implement Queue Using Stacks

Challenge: Sort Values in a Stack

Solution: Sort Values in a Stack

Challenge: Evaluate Postfix Expression Using a Stack

Solution: Evaluate Postfix Expression Using a Stack

Challenge: Next Greater Element Using a Stack

Solution: Next Greater Element Using a Stack

Challenge: Find the Celebrity

Solution: Find the Celebrity

Challenge: Valid Parentheses

Solution: Valid Parentheses

Challenge: Min Stack

Solution: Min Stack

Stack/Queue Quiz: Test your understanding of Stack/Queue

Introduction to Graphs

26 Lessons

Dig into graph concepts, representations, algorithms, and their practical applications in coding.

What is a Graph?

Types of Graphs

Representation of Graphs

Graph Implementation

Complexities of Graph Operations

What is a Bipartite Graph?

Graph Traversal Algorithms

Challenge: Implement Breadth-First Search

Solution: Implement Breadth-First Search

Challenge: Implement Depth-First Search

Solution: Implement Depth-First Search

Challenge: Detect Cycle in a Directed Graph

Solution: Detect Cycle in a Directed Graph

Challenge: Find a Mother Vertex in a Directed Graph

Solution: Find a Mother Vertex in a Directed Graph

Challenge: Count the Number of Edges in an Undirected Graph

Solution: Count the Number of Edges in an Undirected Graph

Challenge: Check If a Path Exists between Two Vertices

Solution: Check If a Path Exists between Two Vertices

Challenge: Graph Valid Tree

Solution: Graph Valid Tree

Challenge: Find the Shortest Path between Two Vertices

Solution: Find the Shortest Path between Two Vertices

Challenge: Remove Edge from a Directed Graph

Solution: Remove Edge from a Directed Graph

Graph Quiz: Test your understanding of Graphs

Introduction to Trees

38 Lessons

Focus on trees' structures, properties, and traversal methods, covering binary, AVL, and Red-Black Trees.

Trees and their Basic Properties!

What makes a tree 'balanced'?

What is a Binary Tree?

More on Complete Binary Trees

Skewed Binary Trees

What is a Binary Search Tree (BST)?

Implementing a Binary Search Tree in Python

Binary Search Tree Insertion

Binary Search Tree Insertion (Implementation)

Searching in a Binary Search Tree (Implementation)

Deletion in a Binary Search Tree

Deletion in a Binary Search Tree (Implementation)

What is a Red-Black Tree?

Red-Black Tree Insertion

Red-Black Tree Deletion

What is a 2-3 Tree?

2-3 Insertion

2-3 Deletion (Case #1)

2-3 Deletion (Case #2)

2-3-4 Trees

Overview of Trees

Challenge: Find Minimum Value in Binary Search Tree

Solution: Find Minimum Value in Binary Search Tree

Challenge: Find kth Maximum Value in Binary Search Tree

Solution: Find kth Maximum Value in Binary Search Tree

Challenge: Find Ancestors of a Given Node in a BST

Solution: Find Ancestors of a Given Node in a BST

Challenge: Find the Height of Binary Search Tree

Solution: Find the Height of Binary Search Tree

Challenge: Find Nodes at k Distance from the Root

Solution: Find Nodes at k Distance from the Root

Trees Quiz: Test your understanding of Trees

Trie

14 Lessons

Build on understanding trie structures, their operations, and solving challenges using them.

Challenge: Total Number of Words in a Trie

Solution: Total Number of Words in a Trie

Challenge: Find All Words Stored in Trie

Solution: Find All Words Stored in Trie

Challenge: List Sort Using Trie

Solution: List Sort Using Trie

Challenge: Word Formation From a Dictionary Using Trie

Solution: Word Formation From a Dictionary Using Trie

Trie Quiz: Test Your Understanding of Tries

Introduction to Heap

12 Lessons

Learn how to use heaps for efficient sorting, priority queues, and element retrieval.

What is a Heap?

Max Heap: Introduction

Max Heap (Implementation)

Min Heap: Introduction

Min Heap (Implementation)

Challenge: Convert Max Heap to Min Heap

Solution: Convert Max Heap to Min Heap

Challenge: Find k Smallest Elements in a List

Solution: Find k Smallest Elements in a List

Challenge: Find K Largest Elements in the List

Solution: Find K Largest Elements in the List

Heap Quiz: Test your understanding of Heaps

Introduction to Hashing

33 Lessons

Walk through hash table fundamentals, efficient operations, implementation strategies, and algorithmic challenges.

What is a Hash Table?

The Hash Function

Collisions in Hash Tables

Building a Hash Table from Scratch

Add/Remove & Search in Hash Table (Implementation)

A Quick Overview of Hash Tables

Trees vs Hash Table

Dictionary vs Set

Challenge: A List as a Subset of Another List

Solution: A List as a Subset of Another List

Challenge: Check if Lists are Disjoint

Solution: Check if Lists are Disjoint

Challenge: Find Symmetric Pairs in a List

Solution: Find Symmetric Pairs in a List

Challenge: Trace the Complete Path of a Journey

Solution: Trace the Complete Path of a Journey

Challenge: Find Two Pairs in an Array Such That a+b = c+d

Solution: Find Two Pairs in an Array Such That a+b = c+d

Challenge: A Sublist with a Sum of 0

Solution: A Sublist with a Sum of 0

Challenge: Word Formation Using a Hash Table

Solution: Word Formation Using a Hash Table

Challenge: Find Two Numbers That Add up to k—Hashing

Solution: Find Two Numbers That Add up to k—Hashing

Challenge: First Non-Repeating Integer in a List—Hashing

Solution: First Non-Repeating Integer in a List—Hashing

Challenge: Linked List Cycle—Hashing

Solution: Linked List Cycle—Hashing

Challenge: Remove Duplicates from Linked List - Hashing

Solution: Remove Duplicates from Linked List - Hashing

Challenge: Union and Intersection of Linked Lists — Hashing

Solution: Union and Intersection of Linked Lists — Hashing

Hashing Quiz: Test your understanding of Hashing

10.

Summary of Data Structures

2 Lessons

Examine linear and non-linear data structures, their complexities, and practical applications.

Overview of Linear & Non-Linear Data Structures

Conclusion

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Free Resources

Frequently Asked Questions

What are the data structures for coding interviews in Python?

For coding interviews in Python, focus on these essential data structures:

Lists: Used for dynamic arrays that support fast access, insertion, and deletion.
Dictionaries: Implement hash tables for efficient key-value storage and lookups.
Sets: Store unique elements and provide fast membership checks.
Tuples: Immutable sequences used for fixed-size collections.
Queues and stacks: Use collections.deque for double-ended queues, which can also efficiently implement stacks and queues.
Heaps: Use heapq for priority queues.
Linked lists, trees, and graphs: Implement manually using classes to handle more complex problems.

Mastering these structures and their operations will prepare you well for Python coding interviews.

Can I use Python for DSA in an interview?

Yes, you can use Python for DSA in interviews. Python is highly popular for its simple syntax, readability, and powerful built-in libraries, which make implementing data structures and algorithms easier. It offers built-in support for lists, dictionaries, sets, and other data structures, along with libraries like collections and heapq for more advanced needs. Python’s versatility and ease of use make it a great choice for demonstrating problem-solving skills in interviews.

How to prepare for a coding interview in Python

To prepare for a coding interview in Python, focus on mastering key data structures (like lists, dictionaries, sets, and heaps) and algorithms (such as sorting, searching, and dynamic programming). Practice solving problems on platforms like LeetCode or HackerRank to build familiarity with Python’s syntax and libraries. Understand time and space complexities and review Python-specific features like list comprehensions, generator expressions, and built-in functions. Regularly simulate coding interviews to improve your problem-solving speed and communication skills.

Is Python a good choice for coding interviews?

Yes, Python is an excellent choice for coding interviews. Its clean and concise syntax allows you to write and debug code quickly, making it ideal for solving complex problems under time constraints. Python’s extensive standard library provides built-in data structures (like lists, dictionaries, and sets) and algorithms, which help simplify implementations. It’s widely accepted in interviews across various companies, and many interviewers are familiar with its capabilities, making it a strong option for effectively demonstrating problem-solving skills.

What are the five data types in Python?

The five primary data types in Python are as follows:

Integers (int): Represent whole numbers, such as 1, 42, or -7.
Floating-point numbers (float): Represent decimal numbers, such as 3.14, 0.001, or -2.5.
Strings (str): Represent sequences of characters, such as “hello”, “Python”, or “123”.
Booleans (bool): Represent truth values, either True or False.
NoneType (None): Represents the absence of a value or a null value, expressed as None.

These basic data types form the foundation for handling and manipulating data in Python.