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

Which is a common implementation of hash tables?

Which is a common implementation of hash tables?

Which data structure uses an array of lists where the $i^{th}$ list stores all elements $x$ such that $\text{hash(x)} = i$?

Which data structure uses an array of lists where the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>i</mi><mrow><mi>t</mi><mi>h</mi></mrow></msup></mrow><annotation encoding="application/x-tex">i^{th}</annotation></semantics></math>ith list stores all elements <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math>x such that <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>hash(x)</mtext><mo>=</mo><mi>i</mi></mrow><annotation encoding="application/x-tex">\text{hash(x)} = i</annotation></semantics></math>hash(x)=i?

If `x` and `y` are not equal, then `x.hashCode()` = `y.hashCode()`.

If `x` and `y` are equal, then `x.hashCode()` and `y.hashCode()` are not equal.

If `x` and `y` are not equal, then the probability that `x.hashCode()` = `y.hashCode()` should be small.

Which statement is true?

What is the strength of collision resolution techniques in open data structures?

What is the strength of collision resolution techniques in open data structures?

Test yourself on what you've learned about hash tables.

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

Quiz on Hash Tables