Grokking Algorithm Complexity and Big-O
Cracking Algorithms: Master Algorithm Analysis and Big-O
Course Overview
Are you struggling to understand how algorithms work or why one solution is better than another? This course on algorithm analysis is designed for students, developers, and job seekers who need a clear path to mastering algorithm performance. Learn how to analyze time and space complexity, study recursive patterns, and apply your knowledge to real-world coding challenges. By the end, you'll confidently choose the most efficient solutions, write optimized code, and excel in interviews and technical projects. Start now to build your expertise and set yourself apart in the world of software development and computer science. This course is for you!
What you'll learn in Grokking Algorithm Complexity and Big-O
- All five asymptotic notations, not just the one everybody uses: Big-O, Big-Omega, Big-Theta, little-o, and little-omega, and how they differ.
- How to analyse control structures directly, so complexity comes from reading the code rather than from recognising the problem.
- Space complexity given its own chapter, since it is the half of the answer candidates forget and interviewers ask for.
- Where logarithmic, linearithmic, and exponential running times come from, rather than presented as a list to memorise.
- Complexity applied to real algorithm families, including sorting, graphs, and dynamic programming, plus the trade-offs between time and space.
- Why an interviewer asking for the tightest bound is not the same as asking for Big-O, which is the distinction most candidates have never been taught.
- Best, worst, and average case treated separately, including why average case is the one people quote wrongly.
- The three methods for recursive complexity: the recursion tree, recurrence relations, and the master theorem.
- The complexity of every common data structure operation: arrays, linked lists, hash tables and sets, stacks, queues, and binary search trees.
- 29 quiz questions across the chapters, because you can follow a complexity argument and still be unable to produce one.
Course Content
Introduction
Basics of Algorithm Analysis
Asymptotic Notations
Time Complexity Analysis
Space Complexity Analysis
Analyzing Recursive Algorithms
Data Structures and Complexity Analysis
Practical Applications and Case Studies
What people say about our courses
About the Author
Arslan Ahmad
Industry Expertise & Leadership
Arslan Ahmad is the lead author of Grokking Algorithm Complexity and Big-O. As the founder of Design Gurus and a former FAANG hiring manager, he has worked at industry giants like Facebook (now Meta) and Microsoft.
He has conducted hundreds of system design interviews, giving him unique insight into what top tech companies look for in candidates.
The course also incorporates expertise from senior engineers at Google, Meta, Amazon, Microsoft, and Uber, ensuring you learn system design best practices from professionals who have built and scaled real-world systems.
500+
Interviews Conducted
10k+
Students Taught
Related Courses
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FAQs
What is Grokking Algorithm Complexity and Big-O?
It is a course on algorithm analysis: asymptotic notation, time and space complexity, how to analyse recursive algorithms, and what every common data structure operation actually costs. Forty-four lessons with 29 quiz questions, ending with complexity applied to sorting, graphs, and dynamic programming.
Who is this course for?
Engineers who can solve interview problems and go quiet when asked what their solution costs. That is extremely common among self-taught developers and among anyone who studied this once and has not used it since. It is also useful before a systems or performance-focused interview where the analysis matters more than the code.
Why learn Big-O as a separate topic?
Because it is asked in every coding interview and taught properly in almost none of them. Most courses attach a one-line complexity to each solution, which tells you the answer without teaching you to derive it. When an interviewer changes the problem slightly, a derived answer survives and a memorised one does not.
Which notations does it cover?
Big-O, Big-Omega, Big-Theta, little-o, and little-omega, with a lesson comparing them. Most candidates only know Big-O and use it where Big-Theta is meant. Knowing the difference is a small thing that reads as real depth in an interview.
Does it cover recursive complexity?
Yes, as a full chapter with three methods: the recursion tree, recurrence relations, and the master theorem. It also handles the space complexity of recursion, which is the stack usage candidates most often forget to count and interviewers most reliably ask about.
Do I need to know algorithms already?
Only basically. You should be able to read a loop and a recursive function. The course builds the analysis from growth rates upward, so you do not need to have studied algorithms formally, and the data structure chapter reintroduces each structure before pricing its operations.
Are there quizzes?
Yes, 29 questions across four chapter quizzes, on time complexity, space complexity, recursive analysis, and data structures. That is deliberate for this subject: complexity arguments are easy to follow and hard to reproduce, and a quiz is the quickest way to tell which of those you have.
Which programming languages are used?
Examples come in Python, Java, C++, JavaScript, Go, and C#. The analysis is language independent, but seeing the code you are analysing in a language you actually read makes the derivation much easier to follow.
Is the course text-based or video-based?
Text-based, with diagrams for growth rates and recursion trees, code examples, and quizzes. Complexity work involves a lot of looking back and forth between a piece of code and a derivation, which suits a page.
Do I get a certificate of completion?
Yes. Design Gurus issues a certificate once you have completed the course, and you can share the link directly or add it to your LinkedIn profile.
