Data Structures & Algorithms

How it is taught

1. Step 1

   Start where you are

   A short placement task, graded by the tutor, starts you past the modules you can already do.

2. Step 2

   Learn in conversation

   Each concept is one session: the tutor explains, asks you to write code, and reviews it until you have shown you understand.

3. Step 3

   Keep it

   Every concept comes back on day 3, 7 and 18. A memory model tracks how well each one is holding, and reviews can go straight into your Google Calendar.

What you will learn

Each concept has its own page: what a session covers, an example and the mistake to watch for.

1. Module 1Complexity analysis

   • Big-O notation
   • Time versus space
   • Amortised cost

2. Module 2Arrays and strings

   • Two pointers
   • Sliding window
   • Prefix sums

3. Module 3Hash maps and sets

   • Hashing and collisions
   • Frequency counting
   • Lookup trade-offs

4. Module 4Linked lists

   • Singly and doubly linked lists
   • Fast and slow pointers
   • Reversing a list

5. Module 5Stacks and queues

   • Stack applications
   • Queues and deques
   • Monotonic stacks

6. Module 6Recursion and backtracking

   • Base cases and the call stack
   • Backtracking templates
   • Memoisation

7. Module 7Sorting

   • Merge sort
   • Quicksort
   • Stability and comparators

8. Module 8Binary search

   • Search on sorted arrays
   • Search on the answer
   • Boundary conditions

9. Module 9Trees

   • Tree traversals
   • Binary search trees
   • Lowest common ancestor

10. Module 10Heaps and priority queues

    • Heap operations
    • Building a heap
    • Top-k problems

11. Module 11Graphs

    • Graph representations
    • Breadth-first search
    • Depth-first search

12. Module 12Shortest paths and ordering

    • Dijkstra's algorithm
    • Topological sort
    • Union-find

13. Module 13Dynamic programming

    • Overlapping subproblems
    • Tabulation
    • Knapsack patterns