This course introduces data structures and algorithms, focusing on practical implementation in Python.

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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

A random binary search tree is always perfectly balanced.

A random binary search tree is more likely to be unbalanced than balanced.

The probability of obtaining any particular permutation of $0$ to $n-1$ is $1/n!$.

The harmonic number $H_{k}$ is closely related to the natural logarithm of $k$.

Which statement is *true* about random binary search trees of size `n`?

Which statement is true about random binary search trees of size <code>n</code>?

A binary search tree that supports `add(x)` and `remove(x)` operations.

A set of nodes containing unique numerical priorities assigned at random that obeys the heap and binary search tree properties.

A data structure that uses Lemma 1 to implement the `find(x)` operation.

A type of heap that is implemented as a `BinarySearchTree` with increasing order of priority.

What is a <code>Treap</code> data structure?

`Treap`s do not support the `add(x)` and `remove(x)` operations needed to implement the `SSet` interface.

The heap property of `Treap` requires that each node has a priority larger than its two children's.

`Treap`s are data structures that can be simultaneously a binary search tree and a heap.

The expected length of the search path for an element `x` in a `Treap` is independent of the rank of `x` in the set.

Which statement is true about <code>Treap</code>?

Test yourself on what you've learned about random binary search trees.

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 Random Binary Search Trees