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Best resources to learnAlgorithm Complexity

Study sheet

This topic covers the mathematical analysis of computer algorithms, focusing on time and space complexity. Learners will understand how to use Big O notation to evaluate algorithm efficiency, compare performance, and optimize code for large-scale data.

Where to start

Start with Algorithms, Part I, a free Coursera course on elementary data structures, sorting and searching that emphasises analysing the performance of Java implementations. If you only use one resource, make it Introduction to Algorithms (MIT 6.006) by Erik Demaine, Jason Ku and Justin Solomon, whose lectures, notes and problem sets with solutions teach asymptotic analysis. Keep bigocheatsheet.com to hand as a quick reference.

9 resources · · How we curate

📹Videos(2)

video

Big O Notations

Derek Banas explains how algorithm running time grows with input size, walking through constant, linear, quadratic, logarithmic and linearithmic examples in Java code. Viewers finish able to read Big O notation and classify simple loops and sorting routines.

beginner🇬🇧
video

Algorithms Course by Abdul Bari

Opening lecture of Abdul Bari's algorithms series, distinguishing an algorithm from a program, listing the properties every algorithm must satisfy, and outlining the time and space criteria used to compare competing solutions to the same problem.

beginner🇬🇧

🎓Courses(4)

course

Introduction to Algorithms (MIT 6.006)

Modeling computational problems and solving them with core algorithms and data structures: sorting, hashing, binary trees, heaps, graph search, shortest paths and dynamic programming. 32 lecture videos, notes, problem sets and exams with solutions teach asymptotic analysis and algorithm design.

intermediate🇬🇧
course

Algorithmic Toolbox

This online course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

beginner🇬🇧
course👁️ 12

Algorithms, Part I

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.

beginner🇬🇧
course

Advanced Graph Algorithms

Strengthen your skills in algorithmics and graph theory, and gain experience in programming in Python along the way.

intermediate🇬🇧

🌐Websites(3)

website

Complexity Zoo

Wiki catalogue of over five hundred computational complexity classes, each with a formal definition, known inclusions and references. Useful for checking what a class such as BPP or PH means and how it relates to others.

beginner🇬🇧
website

Visualgo

Interactive visualizations of data structures and algorithms built by Steven Halim at the National University of Singapore, animating sorting, trees, graphs, and shortest paths step by step so learners can trace how each algorithm changes state.

beginner🇬🇧
website

bigocheatsheet.com

Big O Cheat Sheet is a quick-reference guide that lists Big-O time and space complexities for common data structures and algorithms. It provides concise, table-form lookup for operations across arrays, linked lists, stacks, queues, trees, graphs, and standard algorithms like sorting and searching.

intermediate🇬🇧
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More in Algorithms & Data Structures

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Frequently Asked Questions

Where should I start learning Algorithm Complexity?

Start with Algorithms, Part I, a free Coursera course on elementary data structures, sorting and searching that emphasises analysing the performance of Java implementations. If you only use one resource, make it Introduction to Algorithms (MIT 6.006) by Erik Demaine, Jason Ku and Justin Solomon, whose lectures, notes and problem sets with solutions teach asymptotic analysis. Keep bigocheatsheet.com to hand as a quick reference.

What are the best free resources to learn Algorithm Complexity?

Dantes has curated 9 resources for Algorithm Complexity, including 2 videos, 4 courses, 3 websites. All resources are hand-picked for quality — no algorithmic filler. Browse the full list above to find the format that works best for you.

Is Algorithm Complexity hard to learn?

Algorithm Complexity is approachable at the beginner level — there are resources here specifically for those starting from scratch. As you progress, intermediate and advanced material is also available to take your skills further.

What types of Algorithm Complexity learning resources are available on Dantes?

For Algorithm Complexity, Dantes has curated 2 videos, 4 courses, 3 websites. Each resource type serves a different learning style: videos and YouTube for visual learners, books for depth, courses for structured progression, and websites for quick reference.

How does Dantes select Algorithm Complexity resources?

Dantes is an algorithm-free learning directory. Resources are hand-curated based on quality, accuracy, and usefulness — not engagement metrics or paid placements. The goal is to surface the best learning material for Algorithm Complexity, whether it's a free YouTube series, a classic textbook, or an open courseware from a top university.

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