You’ll begin by learning what algorithms and data structures are. You will discover two data structures: linked lists and stacks. You will then learn how to calculate the complexity of an algorithm by using Big O Notation.

Welcome!

Implementing a linked list

Inserting a node at the beginning of a linked list

Removing the first node from a linked list

Understanding Big O Notation

Options

True

False

Big O Notation: true or false?

Practicing with Big O Notation

Working with stacks

Implementing a Stack with the push method

Implementing the pop method for a stack

Using Python's LifoQueue

Work with Linked Lists and Stacks and Understand Big O notation

This second chapter will teach you the basics of queues, hash tables, trees, and graphs data structures. You will also discover what recursion is.

Working with queues

Implementing a queue for printer tasks

Using Python's SimpleQueue

Hash tables

Correcting bugs in a dictionary

Iterating  over a nested dictionary

Trees and graphs

Correcting bugs in a tree implementation

Building a weighted graph

Understanding Recursion

Fibonacci sequence

Towers of Hanoi

Queues, Hash Tables, Trees,  Graphs, and Recursion

This chapter will focus on searching algorithms, like linear search, binary search, depth first search, and breadth first search. You will also study binary search trees and how to search within them.

Linear Search and Binary Search

Implementing binary search

Binary search using recursion

Binary Search Tree (BST)

Inserting a node into a binary search tree

Finding the minimum node of a BST

Depth First Search (DFS)

Printing book titles in alphabetical order

Using pre-order traversal with Polish notation

Implementing DFS for graphs

Breadth First Search (BFS)

Using breadth first search in binary trees

Finding a graph vertex using BFS

Searching algorithms

This chapter will teach you some sorting algorithms, like bubble sort, selection sort, insertion sort, merge sort, and quicksort.

Bubble Sort

Sorting numbers using bubble sort

Correcting a bug in the bubble sort algorithm

Selection Sort and Insertion Sort

Coding selection sort

Sorting cards using insertion sort

Merge sort

Merge sort: true or false?

Correcting a bug in the merge sort algorithm

Quicksort

Sorting numbers using quicksort

Implementing the quicksort algorithm

Congratulations!

Sorting algorithms

Course Glossary

Most computer programs are based on a few data structures and algorithms. Learn about what’s behind the hood of most of your computer interactions in this four-hour course! You’ll familiarize yourself with some of the most common data structures: linked lists, stacks, queues, graphs and trees. You’ll also implement popular algorithms, such as Depth First Search, Breadth First Search, Bubble sort, Merge sort, and Quicksort.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits, you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2>Recognize Popular Data Structures and Algorithms</h2>
Most computer programs are based on a few data structures and algorithms. Learn about what’s behind the hood of most of your computer interactions in this four-hour course! You’ll familiarize yourself with some of the most common data structures: linked lists, stacks, queues, and trees. You’ll also implement popular algorithms, such as Depth First Search, Breadth First Search, Bubble sort, Merge sort, and Quicksort.

<h2>Learn to Spot Data Structures and Algorithms in Everyday Life</h2>
You'll practice applying data structures and algorithms to decks of cards, music playlists, international dishes, and stacks of books. You’ll walk away with the ability to recognize common data structures and algorithms, and implement them in day-to-day applications!

<h2>Analyze the Efficiency of Algorithms</h2>
Along the way, you’ll stop to analyze popular algorithms in terms of their efficiency. You’ll come to grips with “Big O Notation”, the industry standard for describing the complexity of an algorithm.

<h2>Sharpen Your Python Programming Knowledge</h2>
Being well-versed with data structures and algorithms means being able to take everyday problems and solve them using efficient code. You’ll be practising this in Python, you’ll take these fundamental and transferable skills with you to any programming language.


Introduction to Object-Oriented Programming in Python

Explore data structures such as linked lists, stacks, queues, hash tables and graphs; and the most common searching and sorting algorithms.

Gain an Introduction to Data Structures and Algorithms - DataCamp

Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!

Data Structures and Algorithms in Python

Python Developer

Python Programming Toolbox

The time is constant in an algorithm of O(1).

Big O notation is used to measure the seconds that it takes for an algorithm to process.

Big O Notation is used to measure the best-case complexity of an algorithm.

Big O Notation: true or false?

Data Structures and Algorithms in Python

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