Module COMP42215: Introduction to Computer Science

Department: Computer Science

COMP42215: Introduction to Computer Science

Tied To

• G5K823: Data Science
• G5K923: Data Science (Digital Humanities)
• G5P123: Data Science (Earth and Environment) (see Postgraduate Handbook 2025-2026 for details)
• G5P223: Data Science (Bioinformatics and Biological Modelling)
• G5P323: Data Science (Health)
• G5P423: Data Science (Social Analytics)
• G5P523: Data Science (Heritage)

Pre-requisites

Corequisites

Excluded Combinations

Aims

• To introduce students to the key concepts of programming in python
• To examine how data structures affect the ease of implementation and efficiency of computer programs
• To give students an in-depth understanding at an advanced level of data structures appropriate to data science
• To provide an in-depth understanding and critical evaluation of specialist techniques in software engineering and their relevance to data science

Content

• This module is intended for students whose first degree is not in computer science or related disciplines
• All examples will be given with the python programming language. It is assumed that students will already be aware of python, from pre-course reading and preparation.
• Programming in python
• Data structures and their impact on execution time
• Algorithmic complexity
• Modern software engineering techniques e.g. source-code control, automated testing.

Learning Outcomes

• By the end of this module, students should:
• understand the core constructs of imperative programming and how they are used in python
• have a critical appreciation of the main strengths and weaknesses of a range of programming data structures and how to use them
• have a critical appreciation of modern software engineering techniques

• By the end of this module, students should be able to:
• write computer programs in python
• select appropriate data structures for modelling various data science scenarios
• evaluate the complexity of an algorithm
• use appropriate tools to manage source code
• use appropriate tools to test code automatically

• Effective written communication
• Planning, organising and time-management
• Problem solving and analysis

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• This module will be delivered by the Department of Computer Science
• Learning outcomes are met through practical workshops, supported by online resources. The workshops consist of a combination of taught input, groupwork, case studies, discussion and computing labs. Online resources provide preparatory material for the workshops, typically consisting of directed reading and video content.
• The summative assessment requires the development of a Jupyter notebook that will demonstrate the design, implementation, analysis and testing of Python code to solve specific data science problems. This might consist of programming source code files (Jupyter notebook or Python script) and/or a report of 1500 words max.
• Teaching on this module will be delivered in a blended mode with specific elements delivered online where student numbers determine online teaching as the most effective method

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment

The formative assessment consists of classroom-based exercises involving individual and group tasks on specific computer science topics, relevant to the learning outcomes of the modules. Oral and written feedback will be given on a group and/or individual basis as appropriate.