Big Data & AI is a core part of A-Level Computer Science. Revise the key concepts and common mistakes below, then lock them in with the free games.
Key concepts
Big-O notationAsymptotic upper bound on the growth rate of an algorithm's runtime or space usage.
O(1)Constant time: runtime independent of input size, e.g., array index access.
O(log n)Logarithmic: runtime grows slowly; binary search is the classic example.
O(n log n)Quasi-linear: typical of efficient comparison sorts like merge sort and heapsort.
O(n^2)Quadratic: bubble, insertion and selection sort; nested loops over the input.
O(2^n)Exponential: brute-force solutions to NP-hard problems such as the travelling salesman.
OverfittingWhen a model fits training data too closely and fails on new data.
UnderfittingWhen a model is too simple to capture patterns in the data.
Training SetData used to fit a model.
Test SetHeld-out data used only for final evaluation.
Decision TreeA model that splits data on feature thresholds at each node.
Cross-ValidationSplitting data into folds to estimate generalisation.
PerceptronThe simplest neural unit — a weighted sum passed through a step function.
Neural NetworkLayers of neurons computing increasingly abstract features.
Common mistakes to avoid
Questions where students often pick the tempting wrong answer — make sure you know the right one:
What is the time complexity of binary search?✗ O(n) - because it searches through items. ✓ O(log n).
What does in-order traversal of a binary search tree produce?✗ Nodes in the order they were inserted. ✓ Nodes in ascending sorted order.
What is encapsulation in OOP?✗ Putting code inside other code (nesting). ✓ Bundling data and methods together while restricting direct access to internal state.
Why must a recursive function have a base case?✗ Because the syntax requires it. ✓ Without it, the function calls itself indefinitely, causing a stack overflow.
A decision tree splits at each node on:✗ A random rule ✓ A feature threshold
Practise Big Data & AI — free games
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