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.

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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
Most of the data structures described in this chapter are folklore. They can be found in implementations dating back over 30 years. For example, implementations of stacks, queues, and deques, which generalize easily to the `ArrayStack`, `ArrayQueue` and `ArrayDeque` structures described here, are discussed by #key# Knuth: D. Knuth. Fundamental Algorithms, volume 1 of The Art of Computer Programming. Addison-Wesley, third edition, 1997. #key#.

#key# Brodnik et al.: A. Brodnik, S. Carlsson, E. D. Demaine, J. I. Munro, and R. Sedgewick. Resizable arrays in optimal time and space. In Dehne et al. [18], pages 37–48. #key# seem to have been the first to describe the `RootishArrayStack` and prove a $\sqrt n$ lower-bound. They also present a different structure that uses a more sophisticated choice of block sizes in order to avoid computing square roots in the `i2b(i)` method. Within their scheme, the block containing $i$ is block $\lfloor \log (i+1) \rfloor$, which is simply the index of the leading $1$ bit in the binary representation of $i + 1$. Some computer architectures provide an instruction for computing the index of the leading $1$-bit in an integer. In Java, the `Integer` class provides a method `numberOfLeadingZeros(i)` from which one can easily compute $\lfloor \log(i+1) \rfloor.$

A structure related to the `RootishArrayStack` is the two-level tiered vector of #key# Goodrich and Kloss: M. T. Goodrich and J. G. Kloss. Tiered vectors: Efficient dynamic arrays for rank-based sequences. In Dehne et al. [18], pages 205– 216. #key#. This structure supports the `get(i, x)` and `set(i, x)` operations in constant time and `add(i, x)` and `remove(i)` in $O(\sqrt n)$ time.

Discover more aspects regarding array-based lists.

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 Array-Based Lists

Additional notes