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Learn Data Structures and Algorithms in Python

Build data structures from scratch and learn how to think through complex algorithms in Python. Practice your hard problem-solving skills and write faster code to feel confident in interviews.

What will you learn?

If you've had trouble getting past a hard whiteboarding session, this course is for you. Big-O complexity is arguably the most important concept students learn in a formal computer science degree. You'll build data structures from scratch in Python and improve your problem-solving skills. We'll cover binary trees, linked lists, stacks, graphs and more. This Python course will give you the foundation you need to start your career off on the right foot. After completing this course, you'll be comfortable crushing interview questions and writing performant code.

Chapter List

1
Algorithms Intro
Learn about what algorithms are and why they matter
2
Math
Learn the math required to understand Big-O notation, namely exponents, logarithms, and factorials
3
Big-O Analysis
Figure out what Big-O notation and time complexity mean in the context of algorithms and performant code
4
Sorting Algorithms
Learn how data is sorted on a computer, and how to sort it faster
5
Exponential Time
Understand why exponential time complexity is so dangerous
6
Data Structures Intro
Learn about data structures and how they play a critical role in algorithms
7
Stacks
Learn about stacks, the original LIFO data structure and build one from scratch
8
Queues
Learn about the FIFO queue data structure and how to implement a simple one from scratch
9
Linked Lists
Understand how linked lists vary from arrays and how to use one to build a faster queue
10
Binary Trees
Learn about binary trees, what they are used for, and implement one from scratch
11
Red Black Trees
Solve the classic balancing problem of traditional binary trees with a red-black algorithm
12
Hashmaps
Build a hashmap from scratch and learn how to use the Python dictionary type effectively
13
Tries
Build all the methods of a trie class, and efficiently search entire documents of text
14
Graphs
Learn about graph structures and how we can use them to quickly solve a wide array of search problems
15
BFS and DFS
Implement and understand the ever-famous breadth first and depth-first search algorithms
16
P vs NP
Learn about P and NP

Join 3,058 students in the Learn Data Structures and Algorithms in Python course

Read reviews of their learning experiences

This lesson exceeded my expectations. I went in thinking I knew the basics, but the deeper dives into trade-offs and edge cases were exactly what I needed. The instructor didn’t just show ‘happy path’ solutions: they demonstrated how to think about failure modes, performance bottlenecks, and future extensibility. The capstone project, while optional, gave me the perfect sandbox to experiment and get feedback.

(4/5)
Varadharajan Damotharan profile image

Varadharajan Damotharan

Bangalore

I appreciated how the instructor broke down the core concepts into digestible steps. The examples were practical and mirrored what I encounter in my day-to-day work. In particular, the section on error handling and observability tied everything together. If I had a minor nitpick, it would be that the pacing sped up in the last module, but the accompanying notes and code samples made it easy to revisit and cement the ideas.

(5/5)
Chukwuka Akibor profile image

Chukwuka Akibor

Germany

Appreciated the tips on common pitfalls and debugging.

(4/5)
Kanwadeep Kaur profile image

Kanwadeep Kaur

California

This lesson exceeded my expectations. I went in thinking I knew the basics, but the deeper dives into trade-offs and edge cases were exactly what I needed. The instructor didn’t just show ‘happy path’ solutions: they demonstrated how to think about failure modes, performance bottlenecks, and future extensibility. The capstone project, while optional, gave me the perfect sandbox to experiment and get feedback.

(3/5)
Maksym Minenko profile image

Maksym Minenko

Ukraine

Solid overview with just enough depth to be practical.

(3/5)
Joseph Ogbeche profile image

Joseph Ogbeche

Lagos, Nigeria

I appreciated how the instructor broke down the core concepts into digestible steps. The examples were practical and mirrored what I encounter in my day-to-day work. In particular, the section on error handling and observability tied everything together. If I had a minor nitpick, it would be that the pacing sped up in the last module, but the accompanying notes and code samples made it easy to revisit and cement the ideas.

(4/5)
Yonela Johannes profile image

Yonela Johannes

Cape Town

Short but impactful—taught me exactly what I needed.

(4/5)
AkashPattanaik  profile image

AkashPattanaik

India/Odisha

I appreciated how the instructor broke down the core concepts into digestible steps. The examples were practical and mirrored what I encounter in my day-to-day work. In particular, the section on error handling and observability tied everything together. If I had a minor nitpick, it would be that the pacing sped up in the last module, but the accompanying notes and code samples made it easy to revisit and cement the ideas.

(3/5)
Vladimir  profile image

Vladimir

Istanbul

Great lesson, super clear.

(5/5)
John W profile image

John W

Dublin, Ireland

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Mediocrity doesn't cut it anymore

The only way to become a great developer is to write a lot of code

Avoid tutorial hell

by writing a ton of code

Stay motivated with

a game-like curriculum

Build portfolio projects

to prove your skills

Delve deeper

into foundational concepts

Learn flexibly online

without interrupting your life

For 1% the price of college

to minimize your financial risk

Frequently asked Questions

Got questions? We've got answers

Yes! It's free to create an account and start learning. You'll get all the immersive and interactive features for free for a few chapters. After that, if you still haven't paid for a membership, you'll be in read-only (content only) mode.