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This is an archived syllabus from 2019-2020

COMP26120 Algorithms and Imperative Programming syllabus 2019-2020

COMP26120 Algorithms and Imperative Programming

Level 2
Credits: 20
Enrolled students: 257

Course leader: Giles Reger

Additional staff: view all staff


  • Pre-Requisite (Compulsory): COMP16121
  • Pre-Requisite (Compulsory): COMP16212

Additional requirements

  • Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

    Alternative equivalent knowledge of Java accepted.

Assessment methods

  • 50% Written exam
  • 50% Practical skills assessment
Sem 1 Lecture 1.1 Wed 11:00 - 12:00 -
Sem 1 w2+ Lab Tootill (0 + 1) Fri 10:00 - 12:00 F
Sem 1 w2+ Lab G23 Thu 13:00 - 15:00 H
Sem 1 w2+ Lab G23 Tue 14:00 - 16:00 G
Sem 2 Lab G23 Tue 13:00 - 15:00 G
Sem 2 Lab G23 Mon 16:00 - 18:00 F
Sem 2 Lecture Chemistry G.51 Mon 10:00 - 11:00 -
Sem 2 Lab G23 Fri 10:00 - 12:00 H
Sem 2 w34 Lab G23 Mon 12:00 - 14:00 H
Themes to which this unit belongs
  • Programming and Algorithms
  • Computer Languages


This is a two-semester practical introduction to algorithms and data structures, concentrating on devising and using algorithms, including algorithm design and performance issues as well as 'algorithmic literacy' - knowing what algorithms are available and how and when to use them.

To reflect the emphasis on practical issues, there are two practical (laboratory) hours to each lectured hour. Lectures serve to motivate the subject, orient students, reflect on practical exercises and impart some basic information. A range of practical applications of algorithms will also be presented in the lectures. Other information resources will be important, including a set textbook, which will provide essential support.

The course-unit starts with a 5-week primer on the C programming language, enabling students to become competent programmers in this language as well as in Java (and, possibly, in other languages). This teaching is supported by an on-line C course and extensive laboratory exercises.

There is a follow-up course unit on Advanced Algorithms in the Third Year. This presents the foundational areas of the subject, including (1) measures of algorithmic performance and the classification of computational tasks by the performance of algorithms, (2) formulating and presenting correctness arguments, as well as (3) a range of advanced algorithms, their structure and applications.


To make best use of available learning time by encouraging active learning and by transmitting information in the most effective ways.

To give students a genuine experience of C.

To make students aware of the importance of algorithmic concerns in real-life Computer Science situations.

To emphasise practical concerns, rather than mathematical analysis.

To become confident with a range of data structures and algorithms and able to apply them in realistic tasks.


C for Java programmers (up to reading week)

Algorithms - what they are and how to express them (in pseudocode and selected programming languages: Java and C).

Practical experience in devising, assessing and using algorithms:

  • considerable practice in algorithmic problem solving for realistic problems
  • examples from a wide range of application areas
  • finding appropriate algorithms and data-structures
  • inventing appropriate algorithms and data-structures

Practical experience in 'algorithmic literacy' - knowing how to use the extensive literature on the subject, recognising what algorithms to use in applications and assessing their utility.

A range of basic data structures: arrays, lists, trees (including ordered and balanced trees and heaps), and various kinds of graphs. Representations of basic data structures in programming languages.

A range of basic algorithms: searching and sorting algorithms, tree traversal and manipulation algorithms, some basic graph algorithms. Other algorithmic areas will be explored through practical examples.

An introduction to algorithmic performance: space and time requirements, worst-case, average-case and best case estimates. Practical experience and techniques for measuring and predicting performance: Counting operations.

Scaling and some common rates of growth.

Reasoning about algorithms - experience in informally reasoning about algorithms to establish correctness.

Teaching methods


22 in total, 1 per week


44 hours in total, 22 2-hour sessions, 1 per week

Feedback methods

Feedback is via a variety of methods. Immediate feedback is provided in weekly laboratory sessions. There are tutorial sessions based on work in this course unit, with feedback on the exercises. Exam feedback is provided both on a website and on individual scripts.

Study hours

  • Assessment written exam (4 hours)
  • Lectures (24 hours)
  • Practical classes & workshops (48 hours)

Employability skills

  • Analytical skills
  • Innovation/creativity
  • Oral communication
  • Problem solving

Learning outcomes

On successful completion of this unit, a student will be able to:

  • Write C programs making use of standard constructs and memory management operations   
  • Define standard notions of asymptotic complexity and use these to reason about the complexity of algorithms   
  • Use pseudocode to represent algorithms and informally reason about their correctness   
  • Recall the definitions and representations of basic data structures and the complexity of the operations on them   
  • Explain, using examples of real-world applications, standard algorithmic problems coming from sorting and searching on different data structures, operations on graphs, and number theory   
  • Identify from a set of taught algorithms, which algorithm should apply in a given situation, explain how it should be applied, and compare the solution to possible alternatives   
  • Explain the concepts of divide-and-conquer, dynamic programming, and greedy algorithms, discuss when they are appropriate, and apply them to solve problems   
  • Recall and explain the notions of tractability and NP-completeness with a particular focus on classical NP-complete problems

Reading list

Algorithm design and applications Goodrich, Michael T., author.9781118335918John Wiley Inc2015
Introduction to algorithms : fourth edition Cormen, Thomas H.,9780262367509The MIT Press[2022]
Introduction to the theory of computation (electronic resource)Sipser, Michael9781133187813Cengage Learning2012-11-23
Algorithmics : the spirit of computing Harel, David, 1950-0321117840Addison-Wesley2004.
The art of computer programming. Vol.3 , Sorting and searching. Knuth, Donald E. (Donald Ervin), 1938-0201896850Addison-Wesley1998.
The art of computer programming. Vol.2, Seminumerical algorithms. Knuth, Donald E. (Donald Ervin), 1938-0201896842Addison-Wesleyc1998.
The art of computer programming. Vol.1 , Fundamental algorithms. Knuth, Donald E. (Donald Ervin), 1938-0201896834Addison-Wesley1997.
Algorithms in C Sedgewick, Robert, 1946-0201314525Addison-Wesleyc1998.
Algorithms in C Sedgewick, Robert, 1946-0201314525Addison-Wesleyc1998.
Algorithms in Java Sedgewick, Robert, 1946-0201361205Addison-Wesleyc2003.
Algorithms in Java Sedgewick, Robert, 1946-0201361213Addison-Wesley2004.
Data structures & problem solving using Java Weiss, Mark Allen.9780321541406Addison-Wesley2009.
Algorithms in C++. Part 5, Graph algorithms Sedgewick, Robert, 1946-9780201361186Addison-Wesley2002.
Algorithms in C++, Parts 1-4: Fundamentals, Data Structure, Sorting, Searching, Third Edition: Fundamentals, Data Structures, Sorting, Searching Pts. 1-4Robert Sedgewick0201350882Addison-Wesley Professional; 3 edition13 July 1998

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.