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 sorting algorithm is *not* comparison-based?

Which sorting algorithm is not comparison-based?

What is the running time of the merge-sort algorithm?

What is the running time of the merge-sort algorithm?

This algorithm uses an extra array to store the sorted subarrays.

This algorithm sorts subarrays recursively after solving them.

This algorithm partitions an array into sets of elements equal to the pivot element.

This algorithm guarantees that the expected number of comparisons performed is exactly $2n\ln n + O(n)$.

What is *true* about the quicksort algorithm?

What is true about the quicksort algorithm?

Which data structure is used by the heap-sort algorithm to store elements?

Which data structure is used by the heap-sort algorithm to store elements?

No algorithm can avoid doing roughly $n\log n$ comparisons.

 There are faster algorithms than comparison-based sorting algorithms.

The number of comparisons made by a comparison-based sorting algorithm is always less than $n\log n$.

Randomized algorithms like quicksort can beat the $\log(n!)$ lower bound on the number of comparisons.

Which statement is *true* regarding the comparison-based sorting algorithms?

Which statement is true regarding the comparison-based sorting algorithms?

Counting-sort is a comparison-based sorting algorithm.

Counting-sort is efficient for sorting large arrays of integers.

Counting-sort is not stable and does not preserve the relative order of equal elements.

Counting-sort sorts an array of integers using an auxiliary array of counters.

Which statement is *true* about the counting-sort algorithm?

Which statement is true about the counting-sort algorithm?

Test yourself on what you've learned about the sorting algorithms.

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 Sorting Algorithms