Computer science tutoring

Algorithms and systems explained in plain language

From big-O analysis to data structures and system design, I help you connect theory to real coding so assignments and exams make sense.

AI can produce a working balanced tree in seconds. It cannot sit in your midterm and justify why the complexity is logarithmic. Theory is where AI dependence shows up first, because these courses grade your reasoning, not your output.

Topics we tackle

  • Data structures: arrays, lists, trees, graphs, traced by hand before you code them.
  • Algorithms and complexity analysis you can derive, not just quote back.
  • Object-oriented design and clean architecture, plus the reasoning behind each choice.
  • Systems concepts: memory, pointers, and performance, where AI answers are often confidently wrong.

How we improve results

  • Structured practice with guided walkthroughs, hints before answers.
  • Assignment breakdowns with success criteria you can explain back in your own words.
  • Exam-focused revision and mock questions answered with everything closed.
  • Interview style questions where the reasoning matters more than the code.

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Why theory is where AI dependence hurts most

You can get through a coding assignment with AI and never notice a gap. Algorithms, data structures, and complexity are different. Those courses grade the thinking, and the thinking is the one thing you cannot paste in.

Exams grade reasoning, not output

An assignment asks for working code. A midterm asks why one approach is O(n log n) and the other is not. AI answers the first question well and is not in the room for the second.

Theory has nothing to paste

You can paste a sorting implementation. You cannot paste a recurrence relation you solved, a loop invariant you argued, or a call stack you traced on paper.

Confidently wrong answers

Ask about amortized analysis, pointer arithmetic, or an unusual edge case and you will sometimes get a fluent answer that is simply incorrect. If you cannot check it, you cannot safely use it.

These gaps compound

Data structures sits under algorithms, which sits under systems, upper year courses, and every technical interview you will ever do. A gap here does not stay in one course.

So we work the other way around. You trace it, draw it, and explain it first, and AI is used for practice questions and feedback rather than finished answers.

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Be honest about the AI part. None of this makes you a bad student, it makes you a student in 2026, and it changes what I recommend.

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