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Academic Handbook Postgraduate Programmes

Fundamentals of Computation, Data, and Algorithms Course Descriptor

Course code LCSCI7235 Discipline Computer Science
UK Credit 15 US Credit N/A
FHEQ level 7 Date approved June 2023
Core attributes N/A
Pre-requisites None
Co-requisites None

Course Summary

Mathematics is at the centre of computer science, data analysis, and AI. In particular, we often ask the questions “How can we analyse data efficiently?”, “How long will a computer take to output a solution?”, “How much memory space does it occupy?”, or “How confident are we in the solution?”. This course presents the mathematical techniques used for the design and analysis of data sets and computer algorithms. It covers fundamental concepts from linear algebra – such as vectors, matrices, and transformations – to describe and work with data, statistical methods, computational algorithms, and algorithm and data structure analysis. Hence, the course introduces the analytical tools that students will encounter throughout the duration of their Master’s programme as well as in their subsequent careers. There is a particular focus on sustainable development.

Course Aims

The aims of this course are:

  • Critical understanding of techniques for analysing data sets as well as the correctness, time, and space complexity of algorithms.
  • The ability to recognise which algorithms are best-suited to solve a computing problem.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

K1d Comprehensively understand and master techniques to rigorously analyse data sets and algorithms.
K2d Understand advanced aspects of analytic and algorithmic problems (e.g. statistical inference), algorithms and techniques that solve those problems (e.g. dynamic programming) and rigorous mathematical techniques to analyse the complexity of algorithms (e.g. asymptotic notation and NP-completeness).
K3d Evaluate the technical, social, and management dimensions of algorithms used in industry applications.

Subject Specific Skills

S1d Critically assess and review algorithms used in existing software in terms of their complexity and propose alternatives for improvement.
S2d Critically evaluate the requirements and limitations of algorithms and analytic tools within the context of their application.
S4d Communicate with mathematical rigour algorithms, tools, and techniques as well as their complexity and impact on industrial standards; including data management and use, security, equality, diversity, and inclusion (EDI), and sustainability.
S3d Identify and implement algorithms and analytical tools to solve problems that arise in a software or data analytics application efficiently.

Transferable and Professional Skills

T1d Lead or participate in the design and implementation of high efficient, well-proven software or analysis tools.
T2d Articulate algorithmic and analytic solutions and their complexity to both technical and non-technical audiences.
T2d Critically review and analyse the applicability and complexity of proposed software or analysis frameworks and propose directions for improvement.
T2d Consistently apply an excellent level of technical proficiency in written English, using an advanced application of scholarly terminology, that demonstrates the ability to deal with complex issues both systematically and with sophistication.
T3d Demonstrate initiative in working independently, effectively, and to deadlines.

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage you in your studies.
The scheduled teaching and learning activities for this course are:
Lectures/labs. Typically one lecture and one lab session per week:

  • Version 1: All sessions in the same sized group, or
  • Version 2: most of the sessions in larger groups; some of the sessions in smaller groups

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are required to attend and participate in all the formal and timetabled sessions for this course. Students are also expected to manage their directed learning and independent study in support of the course.
Indicative total learning hours for this course: 150

Employability Skills

  • Communication Skills
  • Mathematical skills



Students will be formatively assessed during the course by means of set assignments. These do not count towards the end of year results but will provide students with developmental feedback. Set assignments will also amplify problem-solving skills useful for the set exercises and written examination.


Assessment will be in two forms:

AE: Assessment Activity Weighting (%) Online submission Duration Length
1 Set exercises 40 Yes N/A Code and up to 2500-word explanation
2 Written examination 60 N/A 2 hours N/A

The examination will consist of a number of questions that students have to answer. Both the set exercises and the examination will be assessed in accordance with the assessment aims set out in the Programme Specification.


Students will receive formal feedback in a variety of ways: written (including via email correspondence); oral (within one-to-one tutorials or on an ad hoc basis) and indirectly through discussion during group tutorials. 

Feedback is provided on written assignments (including essays, briefings and reports) and through generic internal examiners’ reports, both of which are posted on the University’s VLE.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is used as part of the approval/modification process only.


  • Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to Algorithms, 3rd Edition (3rd. ed.). The MIT Press
  • Sanjoy Dasgupta, Christos H. Papadimitriou, and Umesh Vazirani. 2006. Algorithms (1st. ed.). McGraw-Hill, Inc., USA
  • Richard J. Larsen and Morris L. Marx. 2015. Introduction to Mathematical Statistics and Its Applications, 5th Edition. Pearson.
  • Stephen Boyd and Lieven Vandenberghe. 2018. Introduction to Applied Linear Algebra, Cambridge University Press

Indicative Topics

Students will study the following topics: 

  • Linear Algebra: vectors, matrices, transformations
  • Statistics: distributions, empirical statistics
  • Optimisation
  • Techniques for algorithm and data structure analysis
  • Dynamic programming
  • Graph algorithms
Title: LCSCI7235 Fundamentals of Computation, Data, and Algorithms Course Descriptor

Approved by: Academic Board

Location: Academic Handbook/Programme specifications and Handbooks/ Postgraduate Programme Specifications

Version number Date approved Date published  Owner Proposed next review date Modification (As per AQF4) & category number
1.0 June 2023 June 2023 Dr Alexandros Koliousis April 2028
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