Gain insights into implementing array-based and linked list data structures, explore advanced structures like skiplists, hashing, and graphs, and learn reusable, template-based collections for efficient data analysis.

java.tar.gz

Java

Java-1

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 Java

## Binary trees overview
This chapter introduces one of the most fundamental structures in computer science: binary trees. The use of the word _tree_ here comes from the fact that, when we draw them, the resultant drawing often resembles the trees found in a forest. There are many ways of defining binary trees. Mathematically, a **binary tree** is a connected, undirected, finite graph with no cycles, and no vertex of degree greater than three.

For most computer science applications, binary trees are rooted: A special node, `r`, of degree at most two is called the **root of the tree**. For every node, $u \neq r$, the second node on the path from `u` to `r` is called the **parent** of `u`. Each of the other nodes adjacent to `u` is called a **child** of `u`. Most of the binary trees we are interested in are ordered, so we distinguish between the _left child_ and _right child_ of `u`.

## Structure of binary trees
In illustrations, binary trees are usually drawn from the root downward, with the root at the top of the drawing and the left and right children, respectively given by left and right positions in the drawing below. 


# Binary trees overview
This chapter introduces one of the most fundamental structures in computer science: binary trees. The use of the word _tree_ here comes from the fact that, when we draw them, the resultant drawing often resembles the trees found in a forest. There are many ways of defining binary trees. Mathematically, a **binary tree** is a connected, undirected, finite graph with no cycles, and no vertex of degree greater than three.

For most computer science applications, binary trees are rooted: A special node, `r`, of degree at most two is called the **root of the tree**. For every node, $u \neq r$, the second node on the path from `u` to `r` is called the **parent** of `u`. Each of the other nodes adjacent to `u` is called a **child** of `u`. Most of the binary trees we are interested in are ordered, so we distinguish between the _left child_ and _right child_ of `u`.

# Structure of binary trees
In illustrations, binary trees are usually drawn from the root downward, with the root at the top of the drawing and the left and right children, respectively given by left and right positions in the drawing below. 


Check your understanding of binary trees.

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

Introduction to Binary Trees

Binary trees overview