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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

COMP2123: Data Structures and Algorithms

This unit will teach some powerful ideas that are central to solving algorithmic problems in ways that are more efficient than naive approaches. In particular, students will learn how data collections can support efficient access, for example, how a dictionary or map can allow key-based lookup that does not slow down linearly as the collection grows in size. The data structures covered in this unit include lists, stacks, queues, priority queues, search trees, hash tables, and graphs. Students will also learn efficient techniques for classic tasks such as sorting a collection. The concept of asymptotic notation will be introduced, and used to describe the costs of various data access operations and algorithms.

Code COMP2123
Academic unit Computer Science
Credit points 6
INFO1110 OR INFO1910 OR INFO1113 OR DATA1002 OR DATA1902 OR INFO1103 OR INFO1903
INFO1105 OR INFO1905 OR COMP2823

At the completion of this unit, you should be able to:

  • LO1. demonstrate proficiency in organising, presenting and discussing professional ideas and issues in oral, written and graphic formats. Thorough descriptive reporting. With thorough consideration of format and audience requirements. Fluent presentation of engineering/IT concepts and issues to professional and non-professional audiences, using a varied range of professional communication tools and formats
  • LO2. design an algorithmic solution to a problem, coding it, analysing its complexity, and evaluating its suitability to a context
  • LO3. write code that recursively performs an operation on a data structure
  • LO4. apply basic algorithmic techniques (e.g. divide-and-conquer, greedy) to given design tasks
  • LO5. use notation of big-Oh to represent asymptotic growth of cost functions
  • LO6. understand commonly used data structures, including lists, stacks, queues, priority queues, search trees, hash tables, and graphs. This covers the way information is represented in each structure, algorithms for manipulating the structure, and analysis of asymptotic complexity of the operations
  • LO7. understand basic algorithms related to data structures, such as algorithms for sorting, tree traversals, and graph traversals
  • LO8. use mathematical methods to evaluate the performance of an algorithm.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.