Gain insights into array-based and linked list-based data structures, explore advanced structures like skiplists and hashing, and discover template-based collections for efficient data storage and retrieval.

java.tar.gz

Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in Python

The problem with random binary search trees is, of course, that they
are not dynamic. They don’t support the `add(x)` or `remove(x)` operations
needed to implement the `SSet` interface. In this section we describe a
data structure called a `Treap` that uses Lemma 1 (from the previous lesson) to implement the `SSet`
interface.

> **Note:** The name Treap comes from the fact that this data structure is simultaneously a binary search tree and a heap.


## Treap overview
A node in a `Treap` is like a node in a `BinarySearchTree` in that it has
a data value, `x`, but it also contains a unique numerical priority, `p`, that is
assigned at random:

The problem with random binary search trees is, of course, that they
are not dynamic. They don’t support the `add(x)` or `remove(x)` operations
needed to implement the `SSet` interface. In this section we describe a
data structure called a `Treap` that uses Lemma 1 (from the previous lesson) to implement the `SSet`
interface.

> **Note:** The name Treap comes from the fact that this data structure is simultaneously a binary search tree and a heap.


# Treap overview
A node in a `Treap` is like a node in a `BinarySearchTree` in that it has
a data value, `x`, but it also contains a unique numerical priority, `p`, that is
assigned at random:

Learn the implementation of a randomized binary search tree.

Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

Treap: A Randomized Binary Search Tree

Treap overview