In the previous post we learned conditional probability: how the chance of an event changes once we know that another event has happened. Bayes’ theorem takes the very next step. It lets us reverse a conditional probability, so that from an effect we can work backwards to its most likely cause. In this post we […]

Bayes’ Theorem is one of the most powerful and practical results in probability. It tells us how to update the probability of a cause when we observe new evidence. This article explains the definition, necessary conditions, formula, intuition, the Law of Total Probability, and then takes you from easy to advanced problems with complete step-by-step […]

In the previous post we learned conditional probability: how the chance of an event changes once we know that another event has happened. Bayes’ theorem takes the very next step. It lets us reverse a conditional probability, so that from an effect we can work backwards to its most likely cause. In this post we […]

Time complexity is one of the most important concepts in DSA. It tells us how the running time of an algorithm changes when the input size gets bigger (from 10 items → 100 → 10,000 → 1 million). Very simple idea: We want to know — “If the problem becomes 10× or 100× larger, does […]

🔑 Key Takeaway: Every good algorithm must be correct, finite, unambiguous, and efficient. In the previous post we understood what is an algorithm. Now let’s go one step deeper: How does an algorithm actually look? What are its essential characteristics? And how do real algorithms appear in practice? Properties of a Good Algorithm Computer scientists […]

If you’re starting your journey in programming or preparing for technical interviews, Data Structures and Algorithms (DSA) is the most important foundation you will ever build. DSA teaches you how to think efficiently, solve problems systematically, and write code that performs well even with large amounts of data. Quick fact: Almost every FAANG / top-tier […]