Programme Specifications

N/A

Computer Science

August 2024

Provide graduates in Mathematics or Computer Science or Biological Sciences with an applied specialism in Statistics for Computational Biology.

Open up new employment opportunities for graduates in any of these disciplines.

Provide an entry point for interdisciplinary research.

The scheme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:

A1 Knowledge of a range of programming languages and software design techniques

A2 Knowledge of algorithm design and use of efficient data structures

A3 Knowledge of relevant concepts and techniques of analysis, mathematical modelling, probability and statistics

A4 Knowledge of a range of specialist topics in machine learning

A5 Knowledge of a wide range of approaches and technologies available in the biosciences

Learning and Teaching

• Lectures (A1-A5)

• Problem classes (A2, A3)

• Seminars (A4, A5)

• Laboratory work (A1, A2, A4)

• Group and individual projects (A1, A2, A3, A4, A5)

• Visiting lecturer series (A3, A4, A5)

Assessment Strategies and Methods

• Time-constrained examinations (A1-A5)

• Problem sheets (A1, A2, A3, A4)

• Project diaries (A1, A5)

• Project reports (A1, A2, A3, A4, A5)

• Oral presentations (A5)

• Computer programs and assignments (A1, A2, A3, A4)

• Capstone project (A1, A2, A3, A4, A5)

10.2.1 Intellectual Skills

By the end of their programme, all students are expected to be able to demonstrate:

B1 Application of a range of mathematical or statistical concepts and principles in well-defined biological contexts, showing judgement in the selection and application of tools and techniques.

B2 Implementation of computer programs in a range of modern languages

B3 The ability to develop and evaluate logical arguments

B4 The skill of abstracting the essential elements of problems, formulate them in a mathematical context and obtain solutions by appropriate methods

B5 Application of statistical and computational principles and knowledge to practical biological problems

Learning and Teaching

• Lectures (B1-B5)

• Problem classes (B1, B2, B3, B4, B5)

• Seminars (B1, B2, B3, B4, B5)

• Laboratory work (B1, B2, B3, B4, B5)

• Group and individual projects (B1-B5)

• Visiting lecturer series (B1, B5)

Assessment Strategies and Methods

• Time-constrained examinations (B1-B5)

• Problem sheets (B1, B2, B3, B4, B5

• Project diaries (B3, B4, B5)

• Project reports (B1-B5)

• Oral presentations (B3, B4, B5)

• Computer programs and assignments (B1, B2, B4, B5)

• Capstone project (B1, B2, B3, B4, B5)

10.2.2 Professional practical skills / Discipline Specific Skills

By the end of their programme, all students are expected to be able to demonstrate:

C1 Present arguments and conclusions effectively and accurately

C2 Use computer software to analyse and interpret data

C3 Use appropriate theory, practices and tools for the specification, design, implementation and evaluation of computer-based systems

C4 Deploy effectively the tools used for the construction and documentation of bioscience applications

C5 Select and apply appropriate statistical methods to problems in Computational Biology.

Learning and Teaching

• Lectures (C1-C5)

• Problem classes (C1, C2, C3)

• Seminars (C1-C5)

• Laboratory work (C2, C4, C5)

• Group and individual projects (C1-C5)

• Visiting lecturer series (C1, C3, C5)

Assessment Strategies and Methods

• Time-constrained examinations (C2, C4, C5)

• Problem sheets (C2, C4, C5)

• Project diaries (C2, C3, C5)

• Project reports (C1, C2, C3, C4, C5)

• Capstone Project (C1-C5)

10.3 Transferable/key skills

By the end of their programme, all students are expected to be able to demonstrate:

D1 Apply general mathematical skills to the interpretation of numerical data

D2 Work independently

D3 Use information technology effectively to manage information

D4 Manage time and resources effectively

D5 Develop effective learning skills

Learning and Teaching

• Lectures (D1-D5)

• Problem classes (D1-D5)

• Seminars (D1 - D5)

• Laboratory work (D3)

• Group and individual projects (D1-D5)

Assessment Strategies and Methods

• Time-constrained examinations (D1-D5)

• Problem sheets (D1, D3)

• Project diaries (D1, D3, D4)

• Project reports (D1-D5)

• Oral presentations (D5)

• Computer programs and assignments (D1, D3)

• Capstone project (D1-D5)