Catalogue of Modules, University of Nottingham

G64ADS Advanced Data Structures
(Last Updated:03 May 2017)

Year  11/12

Total Credits: 20

Level: Level 4

Target Students:  Students on the MSc Advanced Computing Science and MSci Computer Science Part III. Also available to students on other courses and from other Schools with the agreement of the module convenor.  Available to JYA/Erasmus students.

Taught Semesters:

Autumn Assessed by end of Autumn Semester 

Prerequisites: Significant prior knowledge of Mathematics and Programming, including standard data structures and algorithms.

Corequisites:  None.

Summary of Content:  

This module is part of the Foundations of Computer Science theme in the School of Computer Science.

Efficiency of algorithms. Worst-case, best-case and average-case analyses. Big-Oh notation. Recurrence relations and other supporting mathematics. Review of standard data structures (lists, ordered binary trees, etc.) Advanced data structures (selection from red-black trees, 2-3 trees etc.) Pointer manipulation. Amortized computations. Use of software libraries.

Method and Frequency of Class:

ActivityNumber Of WeeksNumber of sessionsDuration of a session
Lecture 11 weeks1 per week2 hours
Tutorial 7 weeks1 per week2 hours

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

Method of Assessment: 

Assessment TypeWeightRequirements
Exam 1 60 2 hr written examination 
Coursework 1 40 Coursework (including report and presentation) 

Dr V Capretta

Education Aims:  Obtain advanced knowledge and practical skills in the efficient implementation of algorithms on modern computers.

Learning Outcomes:  Knowledge and Understanding: Mathematical modelling and analysis of the efficiency of algorithms. Advanced implementation techniques. Intellectual Skills: The application of mathematical techniques to algorithms and data structures. The identification and evaluation of appropriate models of efficiency. Professional Skills: The use and selection of appropriate software libraries. Transferable Skills: The ability to formulate and construct effective solutions to algorithmic problems.

Offering School:  Computer Science

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