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Data structures and algorithms separate the juniors from the seniors.
Learn the skills you need to get hired.

Big O Data Structures
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Big-O Data Structures is our first Python course, and its purpose is to teach complex data storage concepts in a simple step-by-step curriculum. Common data structures like linked lists, binary trees, and hashmaps are common topics in coding interviews. After taking this course, you’ll be able to confidently answer those questions in order to land high-paying engineering jobs.

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Code in the browser

We don't expect you to bring anything to class. We teach concepts in bite-sized chunks and all your code is written and edited right in your browser.

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Don't stop! We have plenty of computer science courses on Qvault, and as you unlock achievements by doing assignments, we'll reward you with free content.

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No need to upload zip files or guess at whether your code is performing correctly. All your programs instantly run against our test suites to keep you moving quickly.

Why Learn Data Structures?

Senior developers need to understand algorithmic complexity in order to write fast applications and systems. Data structures are the backbone of most algorithms, as most algorithms operate on potentially large sets of data. Many new developers, especially those without formal training, never had the chance to learn these CS basics, and Big-O Data Structures is a course built for them.

This course begins with an overview of Big-O analysis and the definition and purpose of basic data structures. Next, we code some of the most common structures like linked lists, stacks and queues right in the browser and learn about their pros and cons. Later in the course we dive into more complex structures – trees, hashmaps and graphs.

The majority of Big-O Data Structures consists of multi-choice style questions accompanied by explanatory excerpts, though there are many hands-on coding exercises as well. The coding projects in this course are all written in Python, a language becoming more and more popular due to its simple syntax and wide-range of library support.

Big-O Data Structures is a sequel to Big-O Algorithms so be sure to start there if you haven’t taken that course yet.

Content Overview

1. Intro

  • Intro to data structures and definitions
  • Big-O review
  • Lists and indexing

2. Stacks

  • LIFO and stack theory
  • Pros and cons of stacks
  • Stack class implementation
  • Stack overflow

3. Queues

  • Stacks vs Queues
  • Queue class implementation
  • FIFO
  • Linked Lists
  • Linked List Queue

4. Trees

  • Generic Trees
  • Binary Search Trees
  • Red Black Trees
  • Balanced Trees

5. Hashmaps

  • What are hashmaps
  • How hashmaps work
  • Hashmap class implementation
  • Python dictionaries

6. Graphs

  • Graph definition and purposes
  • Graph class implementation
  • Adjacency lists
  • Depth first search
  • Breadth first search
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