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Learn Data Structures and Algorithms
Gain insights into data structures and algorithms using Python. Learn arrays, linked lists, trees, graphs, sorting, and Big-O complexity while improving efficient coding and problem-solving skills.
163 Lessons
22h
Updated yesterday
Join 3 million developers at
Join 3 million developers at
LEARNING OBJECTIVES
- Analyze algorithm efficiency using Big-O, Big-Omega, and Big-Theta notation to evaluate time and space complexity of Python solutions.
- Implement core linear data structures, including arrays, linked lists, stacks, queues, and hash tables, along with their key operations and collision-handling techniques.
- Build and traverse hierarchical data structures such as binary trees, binary search trees, AVL trees, and min/max heaps to solve structured problems.
- Apply graph representations and traversal algorithms across adjacency matrices and lists to solve connectivity, bipartite, and pathfinding problems.
- Solve string, searching, and sorting problems using pattern matching, linear and binary search, and algorithms like merge sort, quick sort, and heap sort.
- Design recursive solutions for problems, including Fibonacci, combination sum, generate parentheses, and N-Queens, using Python.
KEY OUTCOMES
Ace Coding Interviews
Confidently solve data structures and algorithm problems in Python under real interview conditions at top tech companies.
Write Optimized Python Code
Evaluate trade-offs in time and space complexity to ship efficient, scalable solutions in production Python codebases.
Solve Real-World Problems
Tackle 80+ hands-on coding challenges spanning arrays, trees, graphs, and strings, the way working engineers do daily.
Demonstrate DSA Fluency
Reason through unfamiliar problems in technical screens, code reviews, and whiteboard sessions with structured, testable approaches.
Why choose this course?
The Skill That Decides Your Next Offer
Every coding interview eventually comes down to one moment: a problem on the whiteboard, a clock ticking, and an interviewer waiting to see how you think. That moment is decided long before you sit down.
Why Strong Coders Still Stall
Most developers can write working Python. Far fewer can pick the right structure under pressure, justify a Big-O trade-off, or recover when the brute force fails. That gap is what separates rejections from offers and junior pay from senior.
A Practical Path Built Around Problems
This course rebuilds DSA the way engineers actually use it: 162 lessons, 302 in-browser playgrounds, 66 challenges, and 15 quizzes covering arrays, linked lists, trees, graphs, heaps, recursion, and sorting in Python.
Train Where 2.9M Developers Train
You learn by solving, not watching, with instant code feedback and an AI Mentor at every step. Commit the time, and the next interview stops feeling like a gamble.
Learning Roadmap
1.
Introduction to Data Structures and Algorithms
Introduction to Data Structures and Algorithms
Understand data structures and algorithms' significance for efficient programming and problem-solving.
2.
Algorithm Analysis and Complexity
Algorithm Analysis and Complexity
Master algorithm analysis, efficiency measurement, and Big-O notation for optimal performance.
3.
Arrays
Arrays
11 Lessons
11 Lessons
Explore arrays, their types, operations, and solve related algorithmic problems efficiently.
4.
Linked Lists
Linked Lists
17 Lessons
17 Lessons
Explore linked lists, their types, operations, and practical problem-solving techniques.
5.
Stack
Stack
9 Lessons
9 Lessons
Explore stack data structures, their implementations, operations, and problem-solving applications.
6.
Queue
Queue
12 Lessons
12 Lessons
Explore queue data structures, their implementations, operations, and various applications.
7.
Hash Tables
Hash Tables
11 Lessons
11 Lessons
Explore hash tables, their operations, collision handling, and practical problem-solving techniques.
8.
Recursion
Recursion
9 Lessons
9 Lessons
Explore recursion techniques, problem-solving strategies, and applications in programming.
9.
Trees
Trees
12 Lessons
12 Lessons
Explore tree structures, operations, and traversal techniques for efficient data management.
10.
Binary Search Trees
Binary Search Trees
10 Lessons
10 Lessons
Explore binary search trees, their operations, balancing techniques, and problem-solving strategies.
11.
Heaps
Heaps
9 Lessons
9 Lessons
Explore heaps, their types, and applications through various algorithmic problems.
12.
Graphs
Graphs
9 Lessons
9 Lessons
Explore graphs, their types, representations, traversals, applications, and key problems.
13.
String Algorithms
String Algorithms
17 Lessons
17 Lessons
Master string manipulation techniques, including operations, algorithms, and problem-solving strategies.
14.
Searching Algorithms
Searching Algorithms
13 Lessons
13 Lessons
Explore searching algorithms, focusing on linear and binary search techniques.
15.
Sorting Algorithms
Sorting Algorithms
10 Lessons
10 Lessons
Explore various sorting algorithms, their efficiencies, and practical applications.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
Modern applications, including search engines and AI systems, rely on efficient data handling and fast decision-making. As software systems scale, the difference between functional code and performant code becomes critical. Proficiency in data structures and algorithms is essential; it underpins the design of reliable, scalable, and high-performance systems.
I built this course from my background in computer science research and my work in intelligent systems, where efficiency and optimization directly impact real-world outcomes. My experience with adaptive AI, neural networks, and intelligent tutoring systems shows a consistent pattern: developers often struggle more with selecting efficient problem-solving approaches than with syntax. This course focuses on addressing that gap.
You will learn Data Structures and Algorithms in Python through a structured, hands-on approach that prioritizes clarity and practical application. Instead of memorizing concepts, you will understand how to apply arrays, linked lists, trees, graphs, and algorithms like recursion, sorting, and graph traversal to real problems. You will also build intuition for time and space complexity using Big-O analysis.
Thousands of learners use Educative to strengthen their problem-solving skills and prepare for technical interviews. If you want to write efficient code, think like an engineer, and confidently solve complex problems in Python, this course is the right place to start.
ABOUT THE AUTHOR
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
Trusted by 3 million developers working at companies
A
Anthony Walker
@_webarchitect_
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
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Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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