Big O notation is the standard vocabulary computer scientists use to describe how the cost of an algorithm grows as the size of its input grows. Instead of measuring runtime in seconds — which depends on your machine, your compiler, and what else is running — Big O describes the shape of the growth curve: does the work stay flat as data grows (O(1)), grow in step with the data (O(n)), or explode as the data grows (O(n^2))?
In this module, Big O is the tool that lets you compare arrays and lists honestly. Accessing an element by index in an array is a constant-time operation; searching an unsorted list means potentially touching every element; nested loops over the same collection produce quadratic behavior. Once you can label these patterns, you can justify choosing one structure over another instead of guessing.
As you watch, focus on three habits. First, learn to count operations in terms of n rather than in absolute numbers. Second, practice dropping constants and non-dominant terms — why 3n + 50 is simply O(n), and why n^2 + n collapses to O(n^2). Third, build intuition for the relative ordering of complexity classes so that, when you later analyze insertion or deletion at the front versus the back of a structure, the Big O label comes naturally.
A fifteen-minute introduction to Big-O notation that, based on its title and description, walks through both time and space complexity with worked examples. Expect coverage of what Big-O means conceptually and how common complexity classes such as constant, linear, and quadratic time arise from simple code patterns. The longer runtime allows for a slower, example-driven build-up rather than a quick definition.
This is the best starting point for the subtopics "What Is Big O Notation?" and the individual O(1), O(n), and O(n^2) classes, since it is framed explicitly as an introduction with examples. Geekific produces accessible, beginner-oriented programming and data-structures content, which suits students meeting these ideas for the first time.
A concise seven-minute treatment of asymptotic notation that covers Big-O alongside its companions, Theta and Omega. It frames Big-O as one member of a family of notations used to bound an algorithm's growth rate, which clarifies what Big-O is actually asserting (an upper bound) rather than treating it as a vague efficiency score.
Michael Sambol is a well-regarded source for short, precise algorithms explanations, and this video adds the formal grounding behind the informal definition. It directly supports "Comparing Complexity Classes" by showing how growth rates are ordered and bounded.
A focused walkthrough of how to calculate the time complexity of a polynomial algorithm, taking an expression with multiple terms and reducing it to a single Big O class. Expect step-by-step reasoning about which term dominates as n grows large and why lower-order terms and multiplicative constants can be discarded.
This video targets the "Dropping Constants and Non-Dominant Terms" subtopic more directly than a general introduction does, which is exactly the step students most often get wrong. Dinesh Varyani's DSA series is known for methodical, worked-example instruction that you can follow along with on paper.
Together these videos build the analytical vocabulary the module's outcomes depend on. The introductory and asymptotic-notation videos establish what Big O means and how complexity classes are ordered, which is the foundation for MO3 — classifying list and array operations as O(1), O(n), or O(n^2). The polynomial-analysis video supplies the simplification technique you need to reduce a counted operation expression to a clean complexity label, reinforcing the same outcome with hands-on practice. Once that vocabulary is in place, you can apply it to the operation-by-operation discussion in MO2 and use it as evidence when comparing memory layout and sizing behavior (MO1) or defending a data structure choice for a given use case (MO4).
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.