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Essential Data Structures and Algorithms with Python

Master the essential data structures and algorithms that every programmer needs to know through interactive coding challenges. This course covers the DSA patterns that programmers actually use on a daily basis. Learn to analyze algorithm efficiency with Big-O notation, implement essential data structures like arrays, lists, hash tables, stacks, and queues, and understand core algorithms for sorting and searching. Build problem-solving skills by working with classic algorithms including bubble sort, selection sort, insertion sort, linear search, and binary search. Explore recursion fundamentals and develop the foundation for advanced algorithmic thinking. Perfect for developers who want to strengthen their programming fundamentals and prepare for technical interviews.

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Course Chapters

1

DSA Introduction

Learn the fundamentals of data structures and algorithms and why they are essential for every programmer. Discover problem-solving methodologies, understand algorithm quality and edge cases, and explore common algorithmic patterns. Build a solid foundation for analyzing and implementing efficient solutions to computational problems.

7 lessons
2

Big-O Analysis

Master Big-O notation to analyze and compare algorithm efficiency. Learn to identify constant, linear, and quadratic time complexities. Understand how algorithms scale with input size, compare growth rates, and analyze both time and space complexity. Essential knowledge for writing efficient code and making informed algorithmic choices.

16 lessons
3

Arrays and Lists

Master arrays and lists, the fundamental data structures for organizing sequential data in programming. Learn efficient operations for adding, removing, and accessing elements. Explore advanced techniques including two-pointer algorithms, array rotation, duplicate detection, and prefix sum arrays for optimized problem solving.

15 lessons
4

Sorting Algorithms

Learn essential sorting algorithms including bubble sort, selection sort, and insertion sort. Understand when and why to use each algorithm. Implement these fundamental sorting techniques from scratch, analyze their time complexity, and understand the trade-offs between different approaches to organizing data efficiently.

15 lessons
5

Searching Algorithms

Master fundamental searching algorithms including linear search and binary search. Learn to find data efficiently in both unsorted and sorted collections. Implement search algorithms from scratch, understand their performance characteristics, and explore variants of binary search for different problem scenarios.

12 lessons
6

Hash Tables and Dictionaries

Learn hash tables and dictionaries for fast data lookup and storage. Understand hash functions, collision resolution, and load factor management. Implement hash table operations from scratch and master set operations including union, intersection, and difference. Essential for building efficient lookup systems and caching mechanisms.

13 lessons
7

Stacks

Master the stack data structure and its Last-In-First-Out (LIFO) principle. Learn to implement stack operations including push, pop, and peek. Understand how stacks are used in function calls, expression evaluation, and undo operations. Build a complete stack implementation and analyze its performance characteristics.

7 lessons
8

Queues

Learn the queue data structure and its First-In-First-Out (FIFO) principle. Implement essential queue operations including enqueue and dequeue. Understand how queues are used in scheduling systems, breadth-first search, and resource management. Build efficient queue systems for real-world applications.

6 lessons
9

Recursion Fundamentals

Master recursion, a powerful problem-solving technique where functions call themselves. Learn to identify base cases and recursive relationships. Implement classic recursive algorithms, understand memoization for optimization, and apply recursion to searching problems.

7 lessons