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

Accessing a random byte from RAM is thousands of times slower than accessing a random byte from a hard disk or solid-state drive.

Accessing an item from external storage is faster than accessing it from random access memory (RAM).

The size of internal memory must be $O(B + log_Bn)$ for block-based data structures where $B$ is the block size.

The `BlockStore` is a complicated object that encapsulates external memory devices with their own collection of system calls.

Which statement is *true* regarding the external memory model of computation?

Which statement is true regarding the external memory model of computation?

A $B$-tree can have any number of children.

 The height of a $B$-tree is proportional to the base-$B$ logarithm of the number of leaves.

All keys in a $B$-tree are stored in the root node.

Which statement is *true* about $B$-trees?

Which statement is true about <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>B</mi></mrow><annotation encoding="application/x-tex">B</annotation></semantics></math>B-trees?

 The root node is removed, and its child becomes the new root.

The node with the underflow borrows keys from or is merged with one of its siblings.

The smallest value in the subtree rooted at the node with the underflow is removed.

The node with the underflow is split into two new nodes.

What is a possible consequence of underflow in a B-tree?

What is a possible consequence of underflow in a B-tree?

What is the running time of `find(x)`, `add(x)`, and `remove(x)` operations in a B-tree in the external memory model?

What is the running time of <code>find(x)</code>, <code>add(x)</code>, and <code>remove(x)</code> operations in a B-tree in the external memory model?

Test yourself on what you've learned about external memory searching.

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 External Memory Searching