Catalogue of Modules, University of Nottingham

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

Year  08/09

Total Credits: 20

Level: Level 4

Target Students:  Students on the Masters degree in Advanced Computing Science.

Students on the MSc in IT or the MSc in the Management of IT conversion courses who can demonstrate good programming experience, including knowledge of and experience with the implementation of basic data structures.  Available to JYA/Erasmus students.

Taught Semesters:

Autumn Assessed by end of Autumn Semester 

Prerequisites: Must be registered for a taught postgraduate programme.

Corequisites:  None.

Summary of Content:  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
Practical 11 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

Further Activity Details:
Three 1-hour lectures and one 2-hour lab session per week. In addition to this optional support will be provided by daily "surgery" sessions, and an online discussion forum.

Method of Assessment: 

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

Professor G Qiu

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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