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

## Additional notes
Sorting is the fundamental algorithmic problem in computer science, and it has a long history. #key# Knuth: D. Knuth. Sorting and Searching, volume 3 of The Art of Computer Programming. Addison-Wesley, second edition, 1997. #key# attributes the merge-sort algorithm to von Neumann (1945). Quicksort is due to #key# Hoare: C. A. R. Hoare. Algorithm 64: Quicksort. Communications of the ACM, 4(7):321, 1961. #key#. The original heapsort algorithm is due to #key# Williams: J. Williams. Algorithm 232: Heapsort. Communications of the ACM, 7(6):347–348, 1964. #key#, but the version presented here (in which the heap is constructed bottom-up in $O(n)$ time) is due to #key# Floyd: R. W. Floyd. Algorithm 245: Treesort 3. Communications of the ACM, 7(12):701, 1964. #key#. Lower-bounds for comparison-based sorting appear to be folklore. The following table summarizes the performance of these comparisonbased algorithms:

# Additional notes
Sorting is the fundamental algorithmic problem in computer science, and it has a long history. #key# Knuth: D. Knuth. Sorting and Searching, volume 3 of The Art of Computer Programming. Addison-Wesley, second edition, 1997. #key# attributes the merge-sort algorithm to von Neumann (1945). Quicksort is due to #key# Hoare: C. A. R. Hoare. Algorithm 64: Quicksort. Communications of the ACM, 4(7):321, 1961. #key#. The original heapsort algorithm is due to #key# Williams: J. Williams. Algorithm 232: Heapsort. Communications of the ACM, 7(6):347–348, 1964. #key#, but the version presented here (in which the heap is constructed bottom-up in $O(n)$ time) is due to #key# Floyd: R. W. Floyd. Algorithm 245: Treesort 3. Communications of the ACM, 7(12):701, 1964. #key#. Lower-bounds for comparison-based sorting appear to be folklore. The following table summarizes the performance of these comparisonbased algorithms:

Discover more aspects regarding 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

Discussion on Sorting Algorithms

Additional notes