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Data Structures and Algorithms

Data Structures and Algorithms

An exploration of the core data structures and problem-solving techniques that underlie efficient software, from arrays and hash tables to graphs, sorting, and dynamic programming.

60-Second Review

Express review

A condensed, point-by-point summary of the module, for reviewing right before a test or exam.

  1. Measuring Efficiency: Big O and Arrays. Big O notation measures how algorithms scale, and arrays are the simplest structure to analyze.
  2. Linear Structures: Lists, Stacks, Queues, and Hash Tables. Linked lists, stacks, queues, and hash tables organize data for fast insertion, order, or lookup.
  3. Trees and Heaps. Trees, balanced trees, and heaps organize data hierarchically for fast search and priority access.
  4. Graphs and Graph Traversal. Graphs model relationships between entities, explored through BFS, DFS, and Dijkstra's algorithm.
  5. Sorting and Searching. Sorting algorithms and the contrast between linear and binary search underpin efficient data processing.
  6. Algorithm Design Strategies. Recursion, dynamic programming, greedy methods, and backtracking solve problems systematically.
  7. Complexity Theory and Specialized Structures. P, NP, and specialized structures such as tries and union-find address computationally hard problems.
  8. Applying Algorithmic Thinking. Choosing the right paradigm, structure, and interview practice turns theory into working software.

This is the starting point of Data Structures and Algorithms: you don't need any prior knowledge of this subject to begin.

Key points

Visual module map

1Measuring Efficiency: Big O and Arrays
2Trees and Heaps
3Sorting and Searching
4Algorithm Design Strategies
5Applying Algorithmic Thinking

Alternative visual view: a simplified sequence of some of the module's key points, in their order within the syllabus.

Your module mastery

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Approximate breakdown by skill
Understanding of historiographical debate0%

A rough estimate based on the key points reviewed, not an exact measurement of actual mastery.

Final test

You need to review all key points to unlock the final test.

Optional mini-games

Extra practice, with no penalty, to reinforce the module's content.

Review Flashcards

Review each key point with cards that flip to reveal the summary.

Memory

Match each concept with its definition in as few attempts as possible.

Time Attack

Answer true or false statements before time runs out.

Chronological Order

Put the module's key points back in their original order.

Further reading

Bibliography and cited sources used to create this module.

  • Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. (2022). Introduction to Algorithms (4th ed.). Cambridge, MA: MIT Press.
  • Sedgewick, R. and Wayne, K. (2011). Algorithms (4th ed.). Boston: Addison-Wesley.
  • Skiena, S. S. (2020). The Algorithm Design Manual (3rd ed.). Cham: Springer.
  • Knuth, D. E. (1997-1998). The Art of Computer Programming, Volumes 1-3. Boston: Addison-Wesley.
  • Cook, S. A. (1971). "The Complexity of Theorem-Proving Procedures." Proceedings of the Third Annual ACM Symposium on Theory of Computing, pp. 151-158.