Grokking Algorithm Complexity and Big-O

What it covers

The course page lists 8 chapters, 44 lessons, 46 playgrounds, 29 quiz questions and about 5 hours of study time (September 2026). The chapters run in this order:

• Basics of algorithm analysis: measuring efficiency and how functions grow.
• Asymptotic notations: Big-O, Big-Omega, Big-Theta, little-o and little-omega, and how they differ. This matters because an interviewer asking for the tightest bound is really asking for Big-Theta.
• Time complexity: analysing loops and other control structures directly, best, worst and average cases, and constant, linear and quadratic time.
• Space complexity: a whole chapter on the half of the answer candidates forget.
• Recursive algorithms: the recursion tree method, recurrence relations and the master theorem, stack space, and where logarithmic, linearithmic and exponential costs come from.
• Data structures: the cost of common operations on arrays, linked lists, hash tables and sets, stacks and queues, and binary search trees.
• Practical applications: complexity in sorting, graph and dynamic programming algorithms, and time versus space trade-offs.

Most chapters end with a quiz.

Price and ratings

The course offers a short, ordered path through the theory with quizzes, which suits someone who has avoided the topic and wants it done in a weekend. As of September 2026 the course page lists a one-time price of $49.98 for lifetime access, and it is included in the provider's all-courses subscription. The page shows a 4.2 rating from about 545 ratings.

If you already have a data structures course or a pattern course from the same provider, note that those also include complexity analysis on every solution, so this course is mainly for people who want the theory in one place.

How long it really takes

Five hours of reading is realistic for the content. Plan a week if you work every quiz properly and redo the recursive analysis examples by hand.

How to make it stick

The real deliverable is a habit, not a certificate. After this course:

• State time and space complexity out loud for every practice problem before looking at the solution.
• For recursive solutions, draw the recursion tree once, count the levels and the work per level.
• Remember the call stack counts as space. Many candidates give O(1) space for a recursive solution that uses O(n).

Common questions

Do I need maths for this? Only comfort with basic algebra and logarithms. The master theorem lessons are the most mathematical part.

Is this enough for interviews? It covers the complexity side fully. For problem solving, follow it with Grokking Data Structures & Algorithms for Coding Interviews or Grokking the Coding Interview.

Best for: self-taught engineers and students who pause too long when asked "and what is the space complexity?"