You can clone the repo or install the code from NPM:

npm install dsa.js

and then you can import it into your programs or CLI

const { LinkedList, Queue, Stack } = require('dsa.js');

For a list of all available data structures and algorithms, see index.js.

Features

Algorithms are an essential toolbox for every programmer.

You will need to mind algorithms runtime when you have to sort data, search for a value in a big dataset, transform data, scale your code to many users, to name a few. Algorithms are just the step you follow to solve a problem, while data structures are where you store the data for later manipulation. Both combined create programs.

Algorithms + Data Structures = Programs.

Most programming languages and libraries indeed provide implementations for basic data structures and algorithms. However, to make use of data structures properly, you have to know the tradeoffs to choose the best tool for the job.

This material is going to teach you to:

• 🛠 Apply strategies to tackle algorithm questions. Never to get stuck again. Ace those interviews!
• ✂️ Construct efficient algorithms. Learn how to break down problems into manageable pieces.
• 🧠 Improve your problem-solving skills and become a well-rounded developer by understanding fundamental computer science concepts.
• 🤓 Cover essential topics, such as big O time, data structures, and must-know algorithms. Implement 10+ data structures from scratch.

What's Inside

All the code and explanations are available on this repo. You can dig through the links and code examples from the (src folder). However, the inline code examples are not expanded (because of Github's asciidoc limitations), but you can follow the path and see the implementation.

Note: If you prefer to consume the information more linearly, then the book format would be more appropriate for you.

The topics are divided into four main categories, as you can see below:

📈 Algorithms Analysis

Computer Science nuggets without all the mumbo-jumbo

Learn to calculate run time from code examples

Learn how to compare algorithms using Big O notation.

Comparing algorithms using Big O notation

Let's say you want to find the duplicates on an array. Using Big O notation, we can compare different solutions that solve the same problem but has a massive difference in how long it takes to do it.

• Optimal solution using a map
• Finding duplicates in an array (naïve approach)

8 examples to explain with code how to calculate time complexity

Most common time complexities

Time complexity graph

🥞 Linear Data Structures

Understand the ins and outs of the most common data structures

• Arrays: Built-in in most languages so not implemented here. Array Time complexity

• Linked List: each data node has a link to the next (and previous). Code | Linked List Time Complexity

• Queue: data flows in a "first-in, first-out" (FIFO) manner. Code | Queue Time Complexity

• Stack: data flows in a "last-in, first-out" (LIFO) manner. Code | Stack Time Complexity

When to use an Array or Linked List. Know the tradeoffs

Use Arrays when…

• You need to access data in random order fast (using an index).
• Your data is multi-dimensional (e.g., matrix, tensor).

Use Linked Lists when:

• You will access your data sequentially.
• You want to save memory and only allocate memory as you need it.
• You want constant time to remove/add from extremes of the list.
• when size requirement is unknown - dynamic size advantage

Build a List, Stack and a Queue from scratch

Build any of these data structures from scratch:

• Linked List
• Stack
• Queue

🌲 Non-Linear Data Structures

HashMaps

Learn how to implement different types of Maps such as:

• HashMap
• TreeMap

Also, learn the difference between the different Maps implementations:

• HashMap is more time-efficient. A TreeMap is more space-efficient.
• TreeMap search complexity is O(log n), while an optimized HashMap is O(1) on average.
• HashMap’s keys are in insertion order (or random depending on the implementation). TreeMap’s keys are always sorted.
• TreeMap offers some statistical data for free such as: get minimum, get maximum, median, find ranges of keys. HashMap doesn’t.
• TreeMap has a guarantee always an O(log n), while HashMaps has an amortized time of O(1) but in the rare case of a rehash, it would take an O(n).

Know the properties of Graphs and Trees

Graphs

Know all the graphs properties with many images and illustrations.

Graphs: data nodes that can have a connection or edge to