Algorithms

Most algorithms courses eventually converge on the same core idea: two pieces of code that produce identical output can perform wildly differently as the input grows, and Big-O notation is the language for describing that difference without getting lost in exact runtimes on a particular machine. A student typically meets this first through sorting — comparing something like bubble sort's O(n²) behavior against merge sort's O(n log n), and seeing why the gap barely matters on a ten-item list but becomes decisive on a ten-million-item one — before moving into searching (linear versus binary search), then recursion, and eventually into denser territory like dynamic programming, greedy algorithms, and graph traversal (breadth-first and depth-first search).

A common early sticking point is treating Big-O as a precise measurement rather than a growth-rate description; students often want to know the "exact" complexity of a messy real-world function, when the honest answer is closer to "dominated by its slowest part, expressed as input size gets large." Algorithms tutoring on TutorA tends to work from whatever a student is actually stuck on — a specific proof of correctness, an interview-style coding problem, a data-structures course assignment that happens to require Big-O analysis — rather than a fixed syllabus, since the subject shows up embedded inside so many different courses (intro CS, data structures, technical interview prep) rather than as a single standalone class most students take in isolation.

Recursion in particular tends to need repeated, patient explanation before it clicks, since it asks a student to trust a solution before fully tracing it by hand.